sagemaker-deployment/Tutorials/Boston Housing - Updating an Endpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Boston Housing Prices\n",
    "\n",
    "## Updating a model using SageMaker\n",
    "\n",
    "_Deep Learning Nanodegree Program | Deployment_\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, we will continue working with the [Boston Housing Dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). Our goal in this notebook will be to train two different models and to use SageMaker to switch a deployed endpoint from using one model to the other. One of the benefits of using SageMaker to do this is that we can make the change without interrupting service. What this means is that we can continue sending data to the endpoint and at no point will that endpoint disappear.\n",
    "\n",
    "## General Outline\n",
    "\n",
    "Typically, when using a notebook instance with SageMaker, you will proceed through the following steps. Of course, not every step will need to be done with each project. Also, there is quite a lot of room for variation in many of the steps, as you will see throughout these lessons.\n",
    "\n",
    "1. Download or otherwise retrieve the data.\n",
    "2. Process / Prepare the data.\n",
    "3. Upload the processed data to S3.\n",
    "4. Train a chosen model.\n",
    "5. Test the trained model (typically using a batch transform job).\n",
    "6. Deploy the trained model.\n",
    "7. Use the deployed model.\n",
    "\n",
    "In this notebook we will be skipping step 5, testing the model. In addition, we will perform steps 4, 6 and 7 multiple times with different models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Setting up the notebook\n",
    "\n",
    "We begin by setting up all of the necessary bits required to run our notebook. To start that means loading all of the Python modules we will need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from pprint import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "from time import gmtime, strftime\n",
    "\n",
    "from sklearn.datasets import load_boston\n",
    "import sklearn.model_selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to the modules above, we need to import the various bits of SageMaker that we will be using. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.amazon.amazon_estimator import get_image_uri\n",
    "from sagemaker.predictor import csv_serializer\n",
    "\n",
    "# This is an object that represents the SageMaker session that we are currently operating in. This\n",
    "# object contains some useful information that we will need to access later such as our region.\n",
    "session = sagemaker.Session()\n",
    "\n",
    "# This is an object that represents the IAM role that we are currently assigned. When we construct\n",
    "# and launch the training job later we will need to tell it what IAM role it should have. Since our\n",
    "# use case is relatively simple we will simply assign the training job the role we currently have.\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Downloading the data\n",
    "\n",
    "Fortunately, this dataset can be retrieved using sklearn and so this step is relatively straightforward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Preparing and splitting the data\n",
    "\n",
    "Given that this is clean tabular data, we don't need to do any processing. However, we do need to split the rows in the dataset up into train, test and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we package up the input data and the target variable (the median value) as pandas dataframes. This\n",
    "# will make saving the data to a file a little easier later on.\n",
    "\n",
    "X_bos_pd = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "Y_bos_pd = pd.DataFrame(boston.target)\n",
    "\n",
    "# We split the dataset into 2/3 training and 1/3 testing sets.\n",
    "X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(X_bos_pd, Y_bos_pd, test_size=0.33)\n",
    "\n",
    "# Then we split the training set further into 2/3 training and 1/3 validation sets.\n",
    "X_train, X_val, Y_train, Y_val = sklearn.model_selection.train_test_split(X_train, Y_train, test_size=0.33)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Uploading the training and validation files to S3\n",
    "\n",
    "When a training job is constructed using SageMaker, a container is executed which performs the training operation. This container is given access to data that is stored in S3. This means that we need to upload the data we want to use for training to S3. We can use the SageMaker API to do this and hide some of the details.\n",
    "\n",
    "### Save the data locally\n",
    "\n",
    "First we need to create the train and validation csv files which we will then upload to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is our local data directory. We need to make sure that it exists.\n",
    "data_dir = '../data/boston'\n",
    "if not os.path.exists(data_dir):\n",
    "    os.makedirs(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We use pandas to save our train and validation data to csv files. Note that we make sure not to include header\n",
    "# information or an index as this is required by the built in algorithms provided by Amazon. Also, it is assumed\n",
    "# that the first entry in each row is the target variable.\n",
    "\n",
    "pd.concat([Y_val, X_val], axis=1).to_csv(os.path.join(data_dir, 'validation.csv'), header=False, index=False)\n",
    "pd.concat([Y_train, X_train], axis=1).to_csv(os.path.join(data_dir, 'train.csv'), header=False, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload to S3\n",
    "\n",
    "Since we are currently running inside of a SageMaker session, we can use the object which represents this session to upload our data to the 'default' S3 bucket. Note that it is good practice to provide a custom prefix (essentially an S3 folder) to make sure that you don't accidentally interfere with data uploaded from some other notebook or project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "prefix = 'boston-update-endpoints'\n",
    "\n",
    "val_location = session.upload_data(os.path.join(data_dir, 'validation.csv'), key_prefix=prefix)\n",
    "train_location = session.upload_data(os.path.join(data_dir, 'train.csv'), key_prefix=prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 (A): Train the XGBoost model\n",
    "\n",
    "Now that we have the training and validation data uploaded to S3, we can construct our XGBoost model and train it. We will be making use of the high level SageMaker API to do this which will make the resulting code a little easier to read at the cost of some flexibility.\n",
    "\n",
    "To construct an estimator, the object which we wish to train, we need to provide the location of a container which contains the training code. Since we are using a built in algorithm this container is provided by Amazon. However, the full name of the container is a bit lengthy and depends on the region that we are operating in. Fortunately, SageMaker provides a useful utility method called `get_image_uri` that constructs the image name for us.\n",
    "\n",
    "To use the `get_image_uri` method we need to provide it with our current region, which can be obtained from the session object, and the name of the algorithm we wish to use. In this notebook we will be using XGBoost however you could try another algorithm if you wish. The list of built in algorithms can be found in the list of [Common Parameters](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As stated above, we use this utility method to construct the image name for the training container.\n",
    "xgb_container = get_image_uri(session.boto_region_name, 'xgboost')\n",
    "\n",
    "# Now that we know which container to use, we can construct the estimator object.\n",
    "xgb = sagemaker.estimator.Estimator(xgb_container, # The name of the training container\n",
    "                                    role,      # The IAM role to use (our current role in this case)\n",
    "                                    train_instance_count=1, # The number of instances to use for training\n",
    "                                    train_instance_type='ml.m4.xlarge', # The type of instance ot use for training\n",
    "                                    output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),\n",
    "                                                                        # Where to save the output (the model artifacts)\n",
    "                                    sagemaker_session=session) # The current SageMaker session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before asking SageMaker to begin the training job, we should probably set any model specific hyperparameters. There are quite a few that can be set when using the XGBoost algorithm, below are just a few of them. If you would like to change the hyperparameters below or modify additional ones you can find additional information on the [XGBoost hyperparameter page](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost_hyperparameters.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb.set_hyperparameters(max_depth=5,\n",
    "                        eta=0.2,\n",
    "                        gamma=4,\n",
    "                        min_child_weight=6,\n",
    "                        subsample=0.8,\n",
    "                        objective='reg:linear',\n",
    "                        early_stopping_rounds=10,\n",
    "                        num_round=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our estimator object completely set up, it is time to train it. To do this we make sure that SageMaker knows our input data is in csv format and then execute the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: xgboost-2019-01-16-09-57-41-446\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-16 09:57:41 Starting - Starting the training job...\n",
      "2019-01-16 09:57:43 Starting - Launching requested ML instances...\n",
      "2019-01-16 09:58:41 Starting - Preparing the instances for training.........\n",
      "2019-01-16 09:59:59 Downloading - Downloading input data...\n",
      "2019-01-16 10:00:37 Training - Training image download completed. Training in progress.\n",
      "2019-01-16 10:00:37 Uploading - Uploading generated training model\n",
      "2019-01-16 10:00:37 Completed - Training job completed\n",
      "\n",
      "\u001b[31mArguments: train\u001b[0m\n",
      "\u001b[31m[2019-01-16:10:00:25:INFO] Running standalone xgboost training.\u001b[0m\n",
      "\u001b[31m[2019-01-16:10:00:25:INFO] File size need to be processed in the node: 0.02mb. Available memory size in the node: 8451.06mb\u001b[0m\n",
      "\u001b[31m[2019-01-16:10:00:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[10:00:25] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[10:00:25] 227x13 matrix with 2951 entries loaded from /opt/ml/input/data/train?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[2019-01-16:10:00:25:INFO] Determined delimiter of CSV input is ','\u001b[0m\n",
      "\u001b[31m[10:00:25] S3DistributionType set as FullyReplicated\u001b[0m\n",
      "\u001b[31m[10:00:25] 112x13 matrix with 1456 entries loaded from /opt/ml/input/data/validation?format=csv&label_column=0&delimiter=,\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 8 extra nodes, 2 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[0]#011train-rmse:19.4079#011validation-rmse:18.9338\u001b[0m\n",
      "\u001b[31mMultiple eval metrics have been passed: 'validation-rmse' will be used for early stopping.\n",
      "\u001b[0m\n",
      "\u001b[31mWill train until validation-rmse hasn't improved in 10 rounds.\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 0 pruned nodes, max_depth=3\u001b[0m\n",
      "\u001b[31m[1]#011train-rmse:15.7853#011validation-rmse:15.2754\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=4\u001b[0m\n",
      "\u001b[31m[2]#011train-rmse:12.9416#011validation-rmse:12.5028\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 2 pruned nodes, max_depth=4\u001b[0m\n",
      "\u001b[31m[3]#011train-rmse:10.6399#011validation-rmse:10.281\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[4]#011train-rmse:8.80521#011validation-rmse:8.51949\u001b[0m\n",
      "\u001b[31m[5]#011train-rmse:7.32261#011validation-rmse:7.08641\u001b[0m\n",
      "\u001b[31m[6]#011train-rmse:6.10613#011validation-rmse:5.97759\u001b[0m\n",
      "\u001b[31m[7]#011train-rmse:5.15366#011validation-rmse:5.09614\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 26 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[8]#011train-rmse:4.38416#011validation-rmse:4.48525\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[9]#011train-rmse:3.82533#011validation-rmse:4.05871\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10]#011train-rmse:3.38458#011validation-rmse:3.78966\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[11]#011train-rmse:3.03013#011validation-rmse:3.54551\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 6 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[12]#011train-rmse:2.74168#011validation-rmse:3.43351\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[13]#011train-rmse:2.50213#011validation-rmse:3.33315\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[14]#011train-rmse:2.33708#011validation-rmse:3.25689\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[15]#011train-rmse:2.21272#011validation-rmse:3.17692\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 24 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[16]#011train-rmse:2.10341#011validation-rmse:3.1484\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[17]#011train-rmse:2.00886#011validation-rmse:3.13132\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[18]#011train-rmse:1.92761#011validation-rmse:3.09641\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[19]#011train-rmse:1.8364#011validation-rmse:3.09399\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[20]#011train-rmse:1.79427#011validation-rmse:3.08894\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[21]#011train-rmse:1.75346#011validation-rmse:3.08012\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[22]#011train-rmse:1.73061#011validation-rmse:3.08396\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 18 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[23]#011train-rmse:1.66333#011validation-rmse:3.04374\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 22 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[24]#011train-rmse:1.61262#011validation-rmse:3.01423\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[25]#011train-rmse:1.57533#011validation-rmse:3.01679\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 28 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[26]#011train-rmse:1.51564#011validation-rmse:2.98536\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[27]#011train-rmse:1.49567#011validation-rmse:2.96029\u001b[0m\n",
      "\u001b[31m[28]#011train-rmse:1.46205#011validation-rmse:2.96105\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 20 extra nodes, 10 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[29]#011train-rmse:1.42902#011validation-rmse:2.96399\u001b[0m\n",
      "\u001b[31m[30]#011train-rmse:1.41554#011validation-rmse:2.95607\u001b[0m\n",
      "\u001b[31m[31]#011train-rmse:1.38505#011validation-rmse:2.97116\u001b[0m\n",
      "\u001b[31m[32]#011train-rmse:1.36692#011validation-rmse:2.96566\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[33]#011train-rmse:1.3491#011validation-rmse:2.95194\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[34]#011train-rmse:1.33837#011validation-rmse:2.94615\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 16 extra nodes, 4 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[35]#011train-rmse:1.31377#011validation-rmse:2.96914\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[36]#011train-rmse:1.29279#011validation-rmse:2.9597\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[37]#011train-rmse:1.26675#011validation-rmse:2.96484\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[38]#011train-rmse:1.24066#011validation-rmse:2.95859\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[39]#011train-rmse:1.2211#011validation-rmse:2.96409\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 6 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[40]#011train-rmse:1.20251#011validation-rmse:2.95856\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 2 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[41]#011train-rmse:1.18574#011validation-rmse:2.97744\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 10 extra nodes, 6 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[42]#011train-rmse:1.17096#011validation-rmse:2.97293\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 8 pruned nodes, max_depth=4\u001b[0m\n",
      "\u001b[31m[43]#011train-rmse:1.15348#011validation-rmse:2.96896\u001b[0m\n",
      "\u001b[31m[10:00:25] src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 12 pruned nodes, max_depth=5\u001b[0m\n",
      "\u001b[31m[44]#011train-rmse:1.1356#011validation-rmse:2.96927\u001b[0m\n",
      "\u001b[31mStopping. Best iteration:\u001b[0m\n",
      "\u001b[31m[34]#011train-rmse:1.33837#011validation-rmse:2.94615\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Billable seconds: 38\n"
     ]
    }
   ],
   "source": [
    "# This is a wrapper around the location of our train and validation data, to make sure that SageMaker\n",
    "# knows our data is in csv format.\n",
    "s3_input_train = sagemaker.s3_input(s3_data=train_location, content_type='text/csv')\n",
    "s3_input_validation = sagemaker.s3_input(s3_data=val_location, content_type='text/csv')\n",
    "\n",
    "xgb.fit({'train': s3_input_train, 'validation': s3_input_validation})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Test the trained model\n",
    "\n",
    "We will be skipping this step for now.\n",
    "\n",
    "\n",
    "## Step 6 (A): Deploy the trained model\n",
    "\n",
    "Even though we used the high level approach to construct and train the XGBoost model, we will be using the lower level approach to deploy it. One of the reasons for this is so that we have additional control over how the endpoint is constructed. This will be a little more clear later on when construct more advanced endpoints.\n",
    "\n",
    "### Build the model\n",
    "\n",
    "Of course, before we can deploy the model, we need to first create it. The `fit` method that we used earlier created some model artifacts and we can use these to construct a model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remember that a model needs to have a unique name\n",
    "xgb_model_name = \"boston-update-xgboost-model\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# We also need to tell SageMaker which container should be used for inference and where it should\n",
    "# retrieve the model artifacts from. In our case, the xgboost container that we used for training\n",
    "# can also be used for inference and the model artifacts come from the previous call to fit.\n",
    "xgb_primary_container = {\n",
    "    \"Image\": xgb_container,\n",
    "    \"ModelDataUrl\": xgb.model_data\n",
    "}\n",
    "\n",
    "# And lastly we construct the SageMaker model\n",
    "xgb_model_info = session.sagemaker_client.create_model(\n",
    "                                ModelName = xgb_model_name,\n",
    "                                ExecutionRoleArn = role,\n",
    "                                PrimaryContainer = xgb_primary_container)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the endpoint configuration\n",
    "\n",
    "Once we have a model we can start putting together the endpoint. Recall that to do this we need to first create an endpoint configuration, essentially the blueprint that SageMaker will use to build the endpoint itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As before, we need to give our endpoint configuration a name which should be unique\n",
    "xgb_endpoint_config_name = \"boston-update-xgboost-endpoint-config-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# And then we ask SageMaker to construct the endpoint configuration\n",
    "xgb_endpoint_config_info = session.sagemaker_client.create_endpoint_config(\n",
    "                            EndpointConfigName = xgb_endpoint_config_name,\n",
    "                            ProductionVariants = [{\n",
    "                                \"InstanceType\": \"ml.m4.xlarge\",\n",
    "                                \"InitialVariantWeight\": 1,\n",
    "                                \"InitialInstanceCount\": 1,\n",
    "                                \"ModelName\": xgb_model_name,\n",
    "                                \"VariantName\": \"XGB-Model\"\n",
    "                            }])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy the endpoint\n",
    "\n",
    "Now that the endpoint configuration has been created, we can ask SageMaker to build our endpoint.\n",
    "\n",
    "**Note:** This is a friendly (repeated) reminder that you are about to deploy an endpoint. Make sure that you shut it down once you've finished with it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again, we need a unique name for our endpoint\n",
    "endpoint_name = \"boston-update-endpoint-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# And then we can deploy our endpoint\n",
    "endpoint_info = session.sagemaker_client.create_endpoint(\n",
    "                    EndpointName = endpoint_name,\n",
    "                    EndpointConfigName = xgb_endpoint_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "endpoint_dec = session.wait_for_endpoint(endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 (A): Use the model\n",
    "\n",
    "Now that our model is trained and deployed we can send some test data to it and evaluate the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = session.sagemaker_runtime_client.invoke_endpoint(\n",
    "                                                EndpointName = endpoint_name,\n",
    "                                                ContentType = 'text/csv',\n",
    "                                                Body = ','.join(map(str, X_test.values[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f46ed0b8>,\n",
      " 'ContentType': 'text/csv; charset=utf-8',\n",
      " 'InvokedProductionVariant': 'XGB-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '13',\n",
      "                                      'content-type': 'text/csv; charset=utf-8',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:07:13 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'XGB-Model',\n",
      "                                      'x-amzn-requestid': 'dd0841ab-0a7d-4a46-8f13-33c4ff57d735'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'dd0841ab-0a7d-4a46-8f13-33c4ff57d735',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "pprint(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = response['Body'].read().decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'19.7827796936'\n"
     ]
    }
   ],
   "source": [
    "pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shut down the endpoint\n",
    "\n",
    "Now that we know that the XGBoost endpoint works, we can shut it down. We will make use of it again later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '19a8739b-3bae-437b-80f8-3afe66c0e2b4',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '19a8739b-3bae-437b-80f8-3afe66c0e2b4',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '0',\n",
       "   'date': 'Wed, 16 Jan 2019 10:07:57 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sagemaker_client.delete_endpoint(EndpointName = endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4 (B): Train the Linear model\n",
    "\n",
    "Suppose we are working in an environment where the XGBoost model that we trained earlier is becoming too costly. Perhaps the number of calls to our endpoint has increased and the length of time it takes to perform inference with the XGBoost model is becoming problematic.\n",
    "\n",
    "A possible solution might be to train a simpler model to see if it performs nearly as well. In our case, we will construct a linear model. The process of doing this is the same as for creating the XGBoost model that we created earlier, although there are different hyperparameters that we need to set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similar to the XGBoost model, we will use the utility method to construct the image name for the training container.\n",
    "linear_container = get_image_uri(session.boto_region_name, 'linear-learner')\n",
    "\n",
    "# Now that we know which container to use, we can construct the estimator object.\n",
    "linear = sagemaker.estimator.Estimator(linear_container, # The name of the training container\n",
    "                                        role,      # The IAM role to use (our current role in this case)\n",
    "                                        train_instance_count=1, # The number of instances to use for training\n",
    "                                        train_instance_type='ml.m4.xlarge', # The type of instance ot use for training\n",
    "                                        output_path='s3://{}/{}/output'.format(session.default_bucket(), prefix),\n",
    "                                                                            # Where to save the output (the model artifacts)\n",
    "                                        sagemaker_session=session) # The current SageMaker session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before asking SageMaker to train our model, we need to set some hyperparameters. In this case we will be using a linear model so the number of hyperparameters we need to set is much fewer. For more details see the [Linear model hyperparameter page](https://docs.aws.amazon.com/sagemaker/latest/dg/ll_hyperparameters.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear.set_hyperparameters(feature_dim=13, # Our data has 13 feature columns\n",
    "                           predictor_type='regressor', # We wish to create a regression model\n",
    "                           mini_batch_size=200) # Here we set how many samples to look at in each iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the hyperparameters have been set, we can ask SageMaker to fit the linear model to our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: linear-learner-2019-01-16-10-12-36-497\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-01-16 10:12:36 Starting - Starting the training job...\n",
      "2019-01-16 10:12:37 Starting - Launching requested ML instances......\n",
      "2019-01-16 10:13:37 Starting - Preparing the instances for training...\n",
      "2019-01-16 10:14:31 Downloading - Downloading input data...\n",
      "2019-01-16 10:14:44 Training - Downloading the training image...\n",
      "2019-01-16 10:15:31 Uploading - Uploading generated training model\n",
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Reading default configuration from /opt/amazon/lib/python2.7/site-packages/algorithm/default-input.json: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'1000', u'huber_delta': u'1.0', u'num_classes': u'1', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'feature_dim': u'13', u'mini_batch_size': u'200', u'predictor_type': u'regressor'}\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'feature_dim': u'13', u'init_bias': u'0.0', u'lr_scheduler_factor': u'auto', u'num_calibration_samples': u'10000000', u'accuracy_top_k': u'3', u'_num_kv_servers': u'auto', u'use_bias': u'true', u'num_point_for_scaler': u'10000', u'_log_level': u'info', u'quantile': u'0.5', u'bias_lr_mult': u'auto', u'lr_scheduler_step': u'auto', u'init_method': u'uniform', u'init_sigma': u'0.01', u'lr_scheduler_minimum_lr': u'auto', u'target_recall': u'0.8', u'num_models': u'auto', u'early_stopping_patience': u'3', u'momentum': u'auto', u'unbias_label': u'auto', u'wd': u'auto', u'optimizer': u'auto', u'_tuning_objective_metric': u'', u'early_stopping_tolerance': u'0.001', u'learning_rate': u'auto', u'_kvstore': u'auto', u'normalize_data': u'true', u'binary_classifier_model_selection_criteria': u'accuracy', u'use_lr_scheduler': u'true', u'target_precision': u'0.8', u'unbias_data': u'auto', u'init_scale': u'0.07', u'bias_wd_mult': u'auto', u'f_beta': u'1.0', u'mini_batch_size': u'200', u'huber_delta': u'1.0', u'num_classes': u'1', u'predictor_type': u'regressor', u'beta_1': u'auto', u'loss': u'auto', u'beta_2': u'auto', u'_enable_profiler': u'false', u'normalize_label': u'auto', u'_num_gpus': u'auto', u'balance_multiclass_weights': u'false', u'positive_example_weight_mult': u'1.0', u'l1': u'auto', u'margin': u'1.0'}\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 WARNING 139926660151104] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Using default worker.\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Create Store: local\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7f42e188a350>\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[  9.42476177e+00   2.33093719e+01   7.00789833e+00   2.17944950e-01\n",
      "   1.22664250e-01   7.56545424e-01   2.86413860e+01   2.17046547e+00\n",
      "   8.59482956e+00   1.64227386e+02   2.38126040e+00   9.14000549e+01\n",
      "   7.33579922e+00]\u001b[0m\n",
      "\u001b[31m<NDArray 13 @cpu(0)>, 'stdev_label': \u001b[0m\n",
      "\u001b[31m[ 9.5268631]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_label': \u001b[0m\n",
      "\u001b[31m[ 22.61149979]\u001b[0m\n",
      "\u001b[31m<NDArray 1 @cpu(0)>, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[  3.66938043e+00   1.18850002e+01   1.12385998e+01   5.00000007e-02\n",
      "   5.64795494e-01   6.29056501e+00   7.04920044e+01   3.75028110e+00\n",
      "   9.67000008e+00   4.18309998e+02   1.81700001e+01   3.54634186e+02\n",
      "   1.31303501e+01]\u001b[0m\n",
      "\u001b[31m<NDArray 13 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] nvidia-smi took: 0.0251319408417 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Number of GPUs being used: 0\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 66.6136474609375, \"sum\": 66.6136474609375, \"min\": 66.6136474609375}}, \"EndTime\": 1547633728.469804, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.469788}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.96958705357143, \"sum\": 46.96958705357143, \"min\": 46.96958705357143}}, \"EndTime\": 1547633728.469875, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.469846}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.47520228794643, \"sum\": 40.47520228794643, \"min\": 40.47520228794643}}, \"EndTime\": 1547633728.469936, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.469919}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.55999755859375, \"sum\": 48.55999755859375, \"min\": 48.55999755859375}}, \"EndTime\": 1547633728.469991, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.469976}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.72029767717634, \"sum\": 47.72029767717634, \"min\": 47.72029767717634}}, \"EndTime\": 1547633728.470047, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470031}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.57199968610491, \"sum\": 62.57199968610491, \"min\": 62.57199968610491}}, \"EndTime\": 1547633728.470104, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470088}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.58646937779018, \"sum\": 61.58646937779018, \"min\": 61.58646937779018}}, \"EndTime\": 1547633728.470161, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470145}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 77.56549072265625, \"sum\": 77.56549072265625, \"min\": 77.56549072265625}}, \"EndTime\": 1547633728.470231, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470214}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 72.89463152204242, \"sum\": 72.89463152204242, \"min\": 72.89463152204242}}, \"EndTime\": 1547633728.470288, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 68.81466238839286, \"sum\": 68.81466238839286, \"min\": 68.81466238839286}}, \"EndTime\": 1547633728.470345, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470329}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 80.77000209263393, \"sum\": 80.77000209263393, \"min\": 80.77000209263393}}, \"EndTime\": 1547633728.470407, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470392}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 54.093292236328125, \"sum\": 54.093292236328125, \"min\": 54.093292236328125}}, \"EndTime\": 1547633728.470463, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 54.41660853794643, \"sum\": 54.41660853794643, \"min\": 54.41660853794643}}, \"EndTime\": 1547633728.470539, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.470522}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=0, validation mse_objective <loss>=57.9075535366\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=mse_objective, value=34.5165972028\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}, \"Total Records Seen\": {\"count\": 1, \"max\": 654, \"sum\": 654.0, \"min\": 654}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1547633728.472663, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1547633728.369603}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2199.78098928 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9131086730957031, \"sum\": 0.9131086730957031, \"min\": 0.9131086730957031}}, \"EndTime\": 1547633728.50719, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507136}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.825289535522461, \"sum\": 0.825289535522461, \"min\": 0.825289535522461}}, \"EndTime\": 1547633728.507265, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507247}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2745558166503905, \"sum\": 1.2745558166503905, \"min\": 1.2745558166503905}}, \"EndTime\": 1547633728.507345, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507313}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8317852020263672, \"sum\": 0.8317852020263672, \"min\": 0.8317852020263672}}, \"EndTime\": 1547633728.507405, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507388}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6778795623779297, \"sum\": 0.6778795623779297, \"min\": 0.6778795623779297}}, \"EndTime\": 1547633728.507465, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.45669715881347656, \"sum\": 0.45669715881347656, \"min\": 0.45669715881347656}}, \"EndTime\": 1547633728.507522, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507506}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6059398651123047, \"sum\": 0.6059398651123047, \"min\": 0.6059398651123047}}, \"EndTime\": 1547633728.507581, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507565}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5829898452758789, \"sum\": 0.5829898452758789, \"min\": 0.5829898452758789}}, \"EndTime\": 1547633728.507635, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507621}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8867177581787109, \"sum\": 0.8867177581787109, \"min\": 0.8867177581787109}}, \"EndTime\": 1547633728.507691, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507677}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.345242462158203, \"sum\": 1.345242462158203, \"min\": 1.345242462158203}}, \"EndTime\": 1547633728.507747, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507731}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8331321716308594, \"sum\": 0.8331321716308594, \"min\": 0.8331321716308594}}, \"EndTime\": 1547633728.507805, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.50779}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.067519760131836, \"sum\": 1.067519760131836, \"min\": 1.067519760131836}}, \"EndTime\": 1547633728.507879, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6208480834960938, \"sum\": 0.6208480834960938, \"min\": 0.6208480834960938}}, \"EndTime\": 1547633728.507937, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507921}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6452236175537109, \"sum\": 0.6452236175537109, \"min\": 0.6452236175537109}}, \"EndTime\": 1547633728.507992, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.507977}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7062844848632812, \"sum\": 0.7062844848632812, \"min\": 0.7062844848632812}}, \"EndTime\": 1547633728.508047, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508031}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.510892677307129, \"sum\": 0.510892677307129, \"min\": 0.510892677307129}}, \"EndTime\": 1547633728.508101, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508086}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9903516387939453, \"sum\": 0.9903516387939453, \"min\": 0.9903516387939453}}, \"EndTime\": 1547633728.508156, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.50814}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8176390075683594, \"sum\": 0.8176390075683594, \"min\": 0.8176390075683594}}, \"EndTime\": 1547633728.508214, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508198}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1656124114990234, \"sum\": 1.1656124114990234, \"min\": 1.1656124114990234}}, \"EndTime\": 1547633728.508312, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508294}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9831961059570312, \"sum\": 0.9831961059570312, \"min\": 0.9831961059570312}}, \"EndTime\": 1547633728.508383, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508367}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5475855255126953, \"sum\": 0.5475855255126953, \"min\": 0.5475855255126953}}, \"EndTime\": 1547633728.508472, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508445}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5379938125610352, \"sum\": 0.5379938125610352, \"min\": 0.5379938125610352}}, \"EndTime\": 1547633728.508529, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508513}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5939045715332031, \"sum\": 0.5939045715332031, \"min\": 0.5939045715332031}}, \"EndTime\": 1547633728.508577, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508564}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5832545852661133, \"sum\": 0.5832545852661133, \"min\": 0.5832545852661133}}, \"EndTime\": 1547633728.508629, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508614}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9625408935546875, \"sum\": 0.9625408935546875, \"min\": 0.9625408935546875}}, \"EndTime\": 1547633728.508682, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508667}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9544194030761719, \"sum\": 0.9544194030761719, \"min\": 0.9544194030761719}}, \"EndTime\": 1547633728.508733, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508719}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1978189849853516, \"sum\": 1.1978189849853516, \"min\": 1.1978189849853516}}, \"EndTime\": 1547633728.508808, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508791}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.125997085571289, \"sum\": 1.125997085571289, \"min\": 1.125997085571289}}, \"EndTime\": 1547633728.508863, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508848}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9894755554199218, \"sum\": 0.9894755554199218, \"min\": 0.9894755554199218}}, \"EndTime\": 1547633728.508919, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508904}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2122437286376953, \"sum\": 1.2122437286376953, \"min\": 1.2122437286376953}}, \"EndTime\": 1547633728.508973, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.508959}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8420714569091797, \"sum\": 0.8420714569091797, \"min\": 0.8420714569091797}}, \"EndTime\": 1547633728.50903, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.509014}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8517247009277343, \"sum\": 0.8517247009277343, \"min\": 0.8517247009277343}}, \"EndTime\": 1547633728.509084, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.509069}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=1, train mse_objective <loss>=0.913108673096\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 54.43915230887277, \"sum\": 54.43915230887277, \"min\": 54.43915230887277}}, \"EndTime\": 1547633728.552477, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552425}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.76190621512277, \"sum\": 48.76190621512277, \"min\": 48.76190621512277}}, \"EndTime\": 1547633728.55255, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552531}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 77.50831821986607, \"sum\": 77.50831821986607, \"min\": 77.50831821986607}}, \"EndTime\": 1547633728.552616, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552599}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.98347691127232, \"sum\": 48.98347691127232, \"min\": 48.98347691127232}}, \"EndTime\": 1547633728.552684, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552666}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 81.56883893694196, \"sum\": 81.56883893694196, \"min\": 81.56883893694196}}, \"EndTime\": 1547633728.552745, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552728}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 92.09154401506696, \"sum\": 92.09154401506696, \"min\": 92.09154401506696}}, \"EndTime\": 1547633728.552821, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552804}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 92.37208775111607, \"sum\": 92.37208775111607, \"min\": 92.37208775111607}}, \"EndTime\": 1547633728.552879, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 94.42299979073661, \"sum\": 94.42299979073661, \"min\": 94.42299979073661}}, \"EndTime\": 1547633728.552937, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552922}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 53.342494419642854, \"sum\": 53.342494419642854, \"min\": 53.342494419642854}}, \"EndTime\": 1547633728.552998, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.552983}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 83.58404541015625, \"sum\": 83.58404541015625, \"min\": 83.58404541015625}}, \"EndTime\": 1547633728.553056, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.55304}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.87646920340402, \"sum\": 47.87646920340402, \"min\": 47.87646920340402}}, \"EndTime\": 1547633728.553112, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.09429495675223, \"sum\": 63.09429495675223, \"min\": 63.09429495675223}}, \"EndTime\": 1547633728.553168, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553152}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 88.46012660435268, \"sum\": 88.46012660435268, \"min\": 88.46012660435268}}, \"EndTime\": 1547633728.553224, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553209}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 91.46796526227679, \"sum\": 91.46796526227679, \"min\": 91.46796526227679}}, \"EndTime\": 1547633728.55328, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553264}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 86.48717389787946, \"sum\": 86.48717389787946, \"min\": 86.48717389787946}}, \"EndTime\": 1547633728.55333, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.55332}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 89.98890904017857, \"sum\": 89.98890904017857, \"min\": 89.98890904017857}}, \"EndTime\": 1547633728.553383, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553367}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.15740966796875, \"sum\": 63.15740966796875, \"min\": 63.15740966796875}}, \"EndTime\": 1547633728.553438, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553423}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.71405029296875, \"sum\": 47.71405029296875, \"min\": 47.71405029296875}}, \"EndTime\": 1547633728.553497, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553482}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 74.66933768136161, \"sum\": 74.66933768136161, \"min\": 74.66933768136161}}, \"EndTime\": 1547633728.553545, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.55353}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.992501395089285, \"sum\": 62.992501395089285, \"min\": 62.992501395089285}}, \"EndTime\": 1547633728.553602, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553588}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 66.23517281668526, \"sum\": 66.23517281668526, \"min\": 66.23517281668526}}, \"EndTime\": 1547633728.553654, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553644}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 84.84299142020089, \"sum\": 84.84299142020089, \"min\": 84.84299142020089}}, \"EndTime\": 1547633728.553705, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.55369}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 73.93638392857143, \"sum\": 73.93638392857143, \"min\": 73.93638392857143}}, \"EndTime\": 1547633728.55376, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553745}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 75.15576171875, \"sum\": 75.15576171875, \"min\": 75.15576171875}}, \"EndTime\": 1547633728.553816, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.5538}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.89716012137277, \"sum\": 61.89716012137277, \"min\": 61.89716012137277}}, \"EndTime\": 1547633728.55386, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.55385}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.95338657924107, \"sum\": 61.95338657924107, \"min\": 61.95338657924107}}, \"EndTime\": 1547633728.553911, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553896}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 74.50113351004464, \"sum\": 74.50113351004464, \"min\": 74.50113351004464}}, \"EndTime\": 1547633728.553966, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.553951}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 71.49134172712054, \"sum\": 71.49134172712054, \"min\": 71.49134172712054}}, \"EndTime\": 1547633728.554022, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.554006}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 100.37680489676339, \"sum\": 100.37680489676339, \"min\": 100.37680489676339}}, \"EndTime\": 1547633728.554077, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.554062}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 99.37141636439732, \"sum\": 99.37141636439732, \"min\": 99.37141636439732}}, \"EndTime\": 1547633728.554132, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.554117}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 93.62797328404018, \"sum\": 93.62797328404018, \"min\": 93.62797328404018}}, \"EndTime\": 1547633728.554187, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.554172}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 93.02658517020089, \"sum\": 93.02658517020089, \"min\": 93.02658517020089}}, \"EndTime\": 1547633728.554238, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.554227}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=1, validation mse_objective <loss>=54.4391523089\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=mse_objective, value=47.714050293\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Total Records Seen\": {\"count\": 1, \"max\": 881, \"sum\": 881.0, \"min\": 881}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1547633728.555068, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1547633728.472996}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2761.99609535 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8638102722167968, \"sum\": 0.8638102722167968, \"min\": 0.8638102722167968}}, \"EndTime\": 1547633728.579159, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579111}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7836966705322266, \"sum\": 0.7836966705322266, \"min\": 0.7836966705322266}}, \"EndTime\": 1547633728.579222, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579209}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2060385894775392, \"sum\": 1.2060385894775392, \"min\": 1.2060385894775392}}, \"EndTime\": 1547633728.579286, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579269}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7895453643798828, \"sum\": 0.7895453643798828, \"min\": 0.7895453643798828}}, \"EndTime\": 1547633728.579345, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579329}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.052473373413086, \"sum\": 1.052473373413086, \"min\": 1.052473373413086}}, \"EndTime\": 1547633728.579404, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579387}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1368295288085937, \"sum\": 1.1368295288085937, \"min\": 1.1368295288085937}}, \"EndTime\": 1547633728.579465, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.17553466796875, \"sum\": 1.17553466796875, \"min\": 1.17553466796875}}, \"EndTime\": 1547633728.579525, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579509}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2132700347900391, \"sum\": 1.2132700347900391, \"min\": 1.2132700347900391}}, \"EndTime\": 1547633728.579584, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579569}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8449211883544921, \"sum\": 0.8449211883544921, \"min\": 0.8449211883544921}}, \"EndTime\": 1547633728.579644, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579627}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2729102325439454, \"sum\": 1.2729102325439454, \"min\": 1.2729102325439454}}, \"EndTime\": 1547633728.579702, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579686}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7944960021972656, \"sum\": 0.7944960021972656, \"min\": 0.7944960021972656}}, \"EndTime\": 1547633728.579758, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579743}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0135570526123048, \"sum\": 1.0135570526123048, \"min\": 1.0135570526123048}}, \"EndTime\": 1547633728.579816, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579801}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.121952438354492, \"sum\": 1.121952438354492, \"min\": 1.121952438354492}}, \"EndTime\": 1547633728.579876, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.57986}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.18255859375, \"sum\": 1.18255859375, \"min\": 1.18255859375}}, \"EndTime\": 1547633728.579934, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579918}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0668852996826172, \"sum\": 1.0668852996826172, \"min\": 1.0668852996826172}}, \"EndTime\": 1547633728.579993, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.579977}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1089710235595702, \"sum\": 1.1089710235595702, \"min\": 1.1089710235595702}}, \"EndTime\": 1547633728.580053, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580037}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9371578216552734, \"sum\": 0.9371578216552734, \"min\": 0.9371578216552734}}, \"EndTime\": 1547633728.58011, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580095}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.780611343383789, \"sum\": 0.780611343383789, \"min\": 0.780611343383789}}, \"EndTime\": 1547633728.580169, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580153}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1081312561035157, \"sum\": 1.1081312561035157, \"min\": 1.1081312561035157}}, \"EndTime\": 1547633728.580228, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580212}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9330459594726562, \"sum\": 0.9330459594726562, \"min\": 0.9330459594726562}}, \"EndTime\": 1547633728.580284, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580269}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.798686752319336, \"sum\": 0.798686752319336, \"min\": 0.798686752319336}}, \"EndTime\": 1547633728.580342, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580327}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0567652893066406, \"sum\": 1.0567652893066406, \"min\": 1.0567652893066406}}, \"EndTime\": 1547633728.5804, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580384}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9070051574707031, \"sum\": 0.9070051574707031, \"min\": 0.9070051574707031}}, \"EndTime\": 1547633728.580458, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580443}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9388290405273437, \"sum\": 0.9388290405273437, \"min\": 0.9388290405273437}}, \"EndTime\": 1547633728.580505, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.58049}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9585899353027344, \"sum\": 0.9585899353027344, \"min\": 0.9585899353027344}}, \"EndTime\": 1547633728.580562, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580546}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9643259429931641, \"sum\": 0.9643259429931641, \"min\": 0.9643259429931641}}, \"EndTime\": 1547633728.58062, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580604}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1545367431640625, \"sum\": 1.1545367431640625, \"min\": 1.1545367431640625}}, \"EndTime\": 1547633728.580678, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580663}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1084490966796876, \"sum\": 1.1084490966796876, \"min\": 1.1084490966796876}}, \"EndTime\": 1547633728.580735, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.58072}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3660966491699218, \"sum\": 1.3660966491699218, \"min\": 1.3660966491699218}}, \"EndTime\": 1547633728.580793, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580777}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.4316926574707032, \"sum\": 1.4316926574707032, \"min\": 1.4316926574707032}}, \"EndTime\": 1547633728.580851, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580835}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2846380615234374, \"sum\": 1.2846380615234374, \"min\": 1.2846380615234374}}, \"EndTime\": 1547633728.58091, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580894}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3018208312988282, \"sum\": 1.3018208312988282, \"min\": 1.3018208312988282}}, \"EndTime\": 1547633728.580968, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.580952}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=2, train mse_objective <loss>=0.863810272217\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.43603079659598, \"sum\": 51.43603079659598, \"min\": 51.43603079659598}}, \"EndTime\": 1547633728.628684, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.628592}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.45686994280134, \"sum\": 46.45686994280134, \"min\": 46.45686994280134}}, \"EndTime\": 1547633728.628797, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.628776}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 73.04184831891742, \"sum\": 73.04184831891742, \"min\": 73.04184831891742}}, \"EndTime\": 1547633728.628859, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.628843}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.603240966796875, \"sum\": 46.603240966796875, \"min\": 46.603240966796875}}, \"EndTime\": 1547633728.628917, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.628901}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 30.975123814174108, \"sum\": 30.975123814174108, \"min\": 30.975123814174108}}, \"EndTime\": 1547633728.628976, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.62896}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 52.9879150390625, \"sum\": 52.9879150390625, \"min\": 52.9879150390625}}, \"EndTime\": 1547633728.629034, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629017}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.9339599609375, \"sum\": 51.9339599609375, \"min\": 51.9339599609375}}, \"EndTime\": 1547633728.629091, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629076}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.756888253348215, \"sum\": 48.756888253348215, \"min\": 48.756888253348215}}, \"EndTime\": 1547633728.629148, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629132}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.012398856026785, \"sum\": 51.012398856026785, \"min\": 51.012398856026785}}, \"EndTime\": 1547633728.629205, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629189}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 78.79900251116071, \"sum\": 78.79900251116071, \"min\": 78.79900251116071}}, \"EndTime\": 1547633728.62926, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629245}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.735321044921875, \"sum\": 45.735321044921875, \"min\": 45.735321044921875}}, \"EndTime\": 1547633728.629314, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 59.69804164341518, \"sum\": 59.69804164341518, \"min\": 59.69804164341518}}, \"EndTime\": 1547633728.62937, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629354}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.15331595284598, \"sum\": 42.15331595284598, \"min\": 42.15331595284598}}, \"EndTime\": 1547633728.629425, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.62941}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.11662074497768, \"sum\": 42.11662074497768, \"min\": 42.11662074497768}}, \"EndTime\": 1547633728.629479, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629464}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 30.701732090541295, \"sum\": 30.701732090541295, \"min\": 30.701732090541295}}, \"EndTime\": 1547633728.629534, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629519}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 49.173126220703125, \"sum\": 49.173126220703125, \"min\": 49.173126220703125}}, \"EndTime\": 1547633728.629589, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629573}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 59.83770315987723, \"sum\": 59.83770315987723, \"min\": 59.83770315987723}}, \"EndTime\": 1547633728.629644, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.62963}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.61610630580357, \"sum\": 45.61610630580357, \"min\": 45.61610630580357}}, \"EndTime\": 1547633728.629699, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629683}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 71.13602992466518, \"sum\": 71.13602992466518, \"min\": 71.13602992466518}}, \"EndTime\": 1547633728.629754, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629738}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.020167759486604, \"sum\": 60.020167759486604, \"min\": 60.020167759486604}}, \"EndTime\": 1547633728.629809, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629794}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 24.99723379952567, \"sum\": 24.99723379952567, \"min\": 24.99723379952567}}, \"EndTime\": 1547633728.629865, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.62985}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.653699602399556, \"sum\": 42.653699602399556, \"min\": 42.653699602399556}}, \"EndTime\": 1547633728.62992, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629904}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 25.92594473702567, \"sum\": 25.92594473702567, \"min\": 25.92594473702567}}, \"EndTime\": 1547633728.629976, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.629961}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 25.20986066545759, \"sum\": 25.20986066545759, \"min\": 25.20986066545759}}, \"EndTime\": 1547633728.630033, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630016}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.5220947265625, \"sum\": 61.5220947265625, \"min\": 61.5220947265625}}, \"EndTime\": 1547633728.630088, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630073}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.503914969308035, \"sum\": 62.503914969308035, \"min\": 62.503914969308035}}, \"EndTime\": 1547633728.630143, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630128}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 71.7728271484375, \"sum\": 71.7728271484375, \"min\": 71.7728271484375}}, \"EndTime\": 1547633728.630198, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630183}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 70.26196725027901, \"sum\": 70.26196725027901, \"min\": 70.26196725027901}}, \"EndTime\": 1547633728.630253, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630238}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 57.881399972098215, \"sum\": 57.881399972098215, \"min\": 57.881399972098215}}, \"EndTime\": 1547633728.630309, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630294}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 53.60404750279018, \"sum\": 53.60404750279018, \"min\": 53.60404750279018}}, \"EndTime\": 1547633728.630364, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630349}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 58.19164603097098, \"sum\": 58.19164603097098, \"min\": 58.19164603097098}}, \"EndTime\": 1547633728.630419, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630403}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.798671177455354, \"sum\": 61.798671177455354, \"min\": 61.798671177455354}}, \"EndTime\": 1547633728.630474, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.630459}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=2, validation mse_objective <loss>=51.4360307966\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=mse_objective, value=24.9972337995\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}, \"Total Records Seen\": {\"count\": 1, \"max\": 1108, \"sum\": 1108.0, \"min\": 1108}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1547633728.632956, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1547633728.555301}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2910.6720023 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8160397338867188, \"sum\": 0.8160397338867188, \"min\": 0.8160397338867188}}, \"EndTime\": 1547633728.676111, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676055}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7434967041015625, \"sum\": 0.7434967041015625, \"min\": 0.7434967041015625}}, \"EndTime\": 1547633728.676239, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676219}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1385865783691407, \"sum\": 1.1385865783691407, \"min\": 1.1385865783691407}}, \"EndTime\": 1547633728.676314, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7486367797851563, \"sum\": 0.7486367797851563, \"min\": 0.7486367797851563}}, \"EndTime\": 1547633728.67638, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676369}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3666529083251953, \"sum\": 0.3666529083251953, \"min\": 0.3666529083251953}}, \"EndTime\": 1547633728.676447, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676437}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5719198989868164, \"sum\": 0.5719198989868164, \"min\": 0.5719198989868164}}, \"EndTime\": 1547633728.676512, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676499}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5967399978637695, \"sum\": 0.5967399978637695, \"min\": 0.5967399978637695}}, \"EndTime\": 1547633728.676573, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.676563}\n",
      "\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=3, train mse_objective <loss>=0.816039733887\u001b[0m\n",
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      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 82.07950265066964, \"sum\": 82.07950265066964, \"min\": 82.07950265066964}}, \"EndTime\": 1547633728.729808, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.729794}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 75.82164655412946, \"sum\": 75.82164655412946, \"min\": 75.82164655412946}}, \"EndTime\": 1547633728.729863, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.729848}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 89.67088099888393, \"sum\": 89.67088099888393, \"min\": 89.67088099888393}}, \"EndTime\": 1547633728.729923, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.729908}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 88.86727469308036, \"sum\": 88.86727469308036, \"min\": 88.86727469308036}}, \"EndTime\": 1547633728.72998, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.729964}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=3, validation mse_objective <loss>=48.8701477051\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=mse_objective, value=42.1057041713\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}, \"Total Records Seen\": {\"count\": 1, \"max\": 1335, \"sum\": 1335.0, \"min\": 1335}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1547633728.730826, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1547633728.63356}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2330.40843161 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7735396575927734, \"sum\": 0.7735396575927734, \"min\": 0.7735396575927734}}, \"EndTime\": 1547633728.761443, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.761319}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7086225128173829, \"sum\": 0.7086225128173829, \"min\": 0.7086225128173829}}, \"EndTime\": 1547633728.761585, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.76156}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.076744384765625, \"sum\": 1.076744384765625, \"min\": 1.076744384765625}}, \"EndTime\": 1547633728.761659, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.76164}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7131157684326171, \"sum\": 0.7131157684326171, \"min\": 0.7131157684326171}}, \"EndTime\": 1547633728.761773, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.761752}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.48308055877685546, \"sum\": 0.48308055877685546, \"min\": 0.48308055877685546}}, \"EndTime\": 1547633728.76184, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.761822}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5649132919311524, \"sum\": 0.5649132919311524, \"min\": 0.5649132919311524}}, \"EndTime\": 1547633728.761949, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.761929}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5656106567382813, \"sum\": 0.5656106567382813, \"min\": 0.5656106567382813}}, \"EndTime\": 1547633728.762011, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.761995}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5731802368164063, \"sum\": 0.5731802368164063, \"min\": 0.5731802368164063}}, \"EndTime\": 1547633728.762124, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762104}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7692974853515625, \"sum\": 0.7692974853515625, \"min\": 0.7692974853515625}}, \"EndTime\": 1547633728.762182, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762167}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1366236877441407, \"sum\": 1.1366236877441407, \"min\": 1.1366236877441407}}, \"EndTime\": 1547633728.762294, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762274}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7254959106445312, \"sum\": 0.7254959106445312, \"min\": 0.7254959106445312}}, \"EndTime\": 1547633728.762363, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762346}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.913933334350586, \"sum\": 0.913933334350586, \"min\": 0.913933334350586}}, \"EndTime\": 1547633728.762473, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762453}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5006346130371093, \"sum\": 0.5006346130371093, \"min\": 0.5006346130371093}}, \"EndTime\": 1547633728.762582, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762524}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5184407424926758, \"sum\": 0.5184407424926758, \"min\": 0.5184407424926758}}, \"EndTime\": 1547633728.76266, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762641}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5147569274902344, \"sum\": 0.5147569274902344, \"min\": 0.5147569274902344}}, \"EndTime\": 1547633728.762744, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762702}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.52668212890625, \"sum\": 0.52668212890625, \"min\": 0.52668212890625}}, \"EndTime\": 1547633728.762815, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762797}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8392116546630859, \"sum\": 0.8392116546630859, \"min\": 0.8392116546630859}}, \"EndTime\": 1547633728.762935, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.762914}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7119468688964844, \"sum\": 0.7119468688964844, \"min\": 0.7119468688964844}}, \"EndTime\": 1547633728.762997, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.76298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.001710433959961, \"sum\": 1.001710433959961, \"min\": 1.001710433959961}}, \"EndTime\": 1547633728.763085, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763038}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.841094970703125, \"sum\": 0.841094970703125, \"min\": 0.841094970703125}}, \"EndTime\": 1547633728.763156, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763138}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5594062423706054, \"sum\": 0.5594062423706054, \"min\": 0.5594062423706054}}, \"EndTime\": 1547633728.763258, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763205}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5193174743652343, \"sum\": 0.5193174743652343, \"min\": 0.5193174743652343}}, \"EndTime\": 1547633728.763332, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763314}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5968724060058593, \"sum\": 0.5968724060058593, \"min\": 0.5968724060058593}}, \"EndTime\": 1547633728.763427, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763374}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5566600036621093, \"sum\": 0.5566600036621093, \"min\": 0.5566600036621093}}, \"EndTime\": 1547633728.7635, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763482}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9538646697998047, \"sum\": 0.9538646697998047, \"min\": 0.9538646697998047}}, \"EndTime\": 1547633728.763594, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763549}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.969781494140625, \"sum\": 0.969781494140625, \"min\": 0.969781494140625}}, \"EndTime\": 1547633728.763667, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763649}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.072112808227539, \"sum\": 1.072112808227539, \"min\": 1.072112808227539}}, \"EndTime\": 1547633728.763724, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763709}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0675981903076173, \"sum\": 1.0675981903076173, \"min\": 1.0675981903076173}}, \"EndTime\": 1547633728.763824, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763805}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.167567138671875, \"sum\": 1.167567138671875, \"min\": 1.167567138671875}}, \"EndTime\": 1547633728.763893, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.763876}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0395034790039062, \"sum\": 1.0395034790039062, \"min\": 1.0395034790039062}}, \"EndTime\": 1547633728.763999, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.76398}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2310050964355468, \"sum\": 1.2310050964355468, \"min\": 1.2310050964355468}}, \"EndTime\": 1547633728.764069, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.764052}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2210723876953125, \"sum\": 1.2210723876953125, \"min\": 1.2210723876953125}}, \"EndTime\": 1547633728.764127, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.764112}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=4, train mse_objective <loss>=0.773539657593\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.426199776785715, \"sum\": 46.426199776785715, \"min\": 46.426199776785715}}, \"EndTime\": 1547633728.831127, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831073}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.775360107421875, \"sum\": 42.775360107421875, \"min\": 42.775360107421875}}, \"EndTime\": 1547633728.831196, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831183}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 65.17167445591518, \"sum\": 65.17167445591518, \"min\": 65.17167445591518}}, \"EndTime\": 1547633728.831258, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831241}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.744859967912944, \"sum\": 42.744859967912944, \"min\": 42.744859967912944}}, \"EndTime\": 1547633728.831304, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831289}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 49.39028058733259, \"sum\": 49.39028058733259, \"min\": 49.39028058733259}}, \"EndTime\": 1547633728.831363, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831346}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.298740931919646, \"sum\": 51.298740931919646, \"min\": 51.298740931919646}}, \"EndTime\": 1547633728.831463, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831426}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.5672607421875, \"sum\": 47.5672607421875, \"min\": 47.5672607421875}}, \"EndTime\": 1547633728.831527, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.83151}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 53.14850289481027, \"sum\": 53.14850289481027, \"min\": 53.14850289481027}}, \"EndTime\": 1547633728.831586, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.83157}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.29219273158482, \"sum\": 47.29219273158482, \"min\": 47.29219273158482}}, \"EndTime\": 1547633728.831641, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831626}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 70.27856881277901, \"sum\": 70.27856881277901, \"min\": 70.27856881277901}}, \"EndTime\": 1547633728.831697, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831681}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.3536376953125, \"sum\": 42.3536376953125, \"min\": 42.3536376953125}}, \"EndTime\": 1547633728.831754, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831738}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 53.941781180245535, \"sum\": 53.941781180245535, \"min\": 53.941781180245535}}, \"EndTime\": 1547633728.831808, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831793}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.389944893973215, \"sum\": 47.389944893973215, \"min\": 47.389944893973215}}, \"EndTime\": 1547633728.831865, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.83185}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.166268484933035, \"sum\": 51.166268484933035, \"min\": 51.166268484933035}}, \"EndTime\": 1547633728.831922, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831906}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 52.41258021763393, \"sum\": 52.41258021763393, \"min\": 52.41258021763393}}, \"EndTime\": 1547633728.831977, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.831962}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.281018938337056, \"sum\": 46.281018938337056, \"min\": 46.281018938337056}}, \"EndTime\": 1547633728.832031, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832016}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 54.257655552455354, \"sum\": 54.257655552455354, \"min\": 54.257655552455354}}, \"EndTime\": 1547633728.832087, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832071}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.20859636579241, \"sum\": 42.20859636579241, \"min\": 42.20859636579241}}, \"EndTime\": 1547633728.832159, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832143}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 65.02491106305804, \"sum\": 65.02491106305804, \"min\": 65.02491106305804}}, \"EndTime\": 1547633728.832214, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832199}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 55.015415736607146, \"sum\": 55.015415736607146, \"min\": 55.015415736607146}}, \"EndTime\": 1547633728.832268, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832254}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.84021432059152, \"sum\": 47.84021432059152, \"min\": 47.84021432059152}}, \"EndTime\": 1547633728.832324, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832308}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 50.676361083984375, \"sum\": 50.676361083984375, \"min\": 50.676361083984375}}, \"EndTime\": 1547633728.83238, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832364}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 57.102347237723215, \"sum\": 57.102347237723215, \"min\": 57.102347237723215}}, \"EndTime\": 1547633728.832435, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.83242}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 55.853140694754465, \"sum\": 55.853140694754465, \"min\": 55.853140694754465}}, \"EndTime\": 1547633728.83249, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832475}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.86120169503348, \"sum\": 61.86120169503348, \"min\": 61.86120169503348}}, \"EndTime\": 1547633728.832546, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.83253}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.06211199079241, \"sum\": 63.06211199079241, \"min\": 63.06211199079241}}, \"EndTime\": 1547633728.8326, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832586}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 67.24706595284599, \"sum\": 67.24706595284599, \"min\": 67.24706595284599}}, \"EndTime\": 1547633728.832657, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832641}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 67.70022583007812, \"sum\": 67.70022583007812, \"min\": 67.70022583007812}}, \"EndTime\": 1547633728.832712, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832697}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 93.72065952845982, \"sum\": 93.72065952845982, \"min\": 93.72065952845982}}, \"EndTime\": 1547633728.832768, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832753}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 87.31622314453125, \"sum\": 87.31622314453125, \"min\": 87.31622314453125}}, \"EndTime\": 1547633728.832823, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832809}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 104.78440638950893, \"sum\": 104.78440638950893, \"min\": 104.78440638950893}}, \"EndTime\": 1547633728.832889, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832872}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 99.25153459821429, \"sum\": 99.25153459821429, \"min\": 99.25153459821429}}, \"EndTime\": 1547633728.832956, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.832939}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=4, validation mse_objective <loss>=46.4261997768\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=mse_objective, value=42.2085963658\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Total Records Seen\": {\"count\": 1, \"max\": 1562, \"sum\": 1562.0, \"min\": 1562}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1547633728.833823, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1547633728.731088}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2206.90416344 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7344944763183594, \"sum\": 0.7344944763183594, \"min\": 0.7344944763183594}}, \"EndTime\": 1547633728.86923, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869122}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.67710205078125, \"sum\": 0.67710205078125, \"min\": 0.67710205078125}}, \"EndTime\": 1547633728.869352, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869328}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.01841552734375, \"sum\": 1.01841552734375, \"min\": 1.01841552734375}}, \"EndTime\": 1547633728.869422, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869404}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6809888458251954, \"sum\": 0.6809888458251954, \"min\": 0.6809888458251954}}, \"EndTime\": 1547633728.869485, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869467}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6294528198242187, \"sum\": 0.6294528198242187, \"min\": 0.6294528198242187}}, \"EndTime\": 1547633728.869555, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869537}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5731284713745117, \"sum\": 0.5731284713745117, \"min\": 0.5731284713745117}}, \"EndTime\": 1547633728.869618, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869602}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5613279724121094, \"sum\": 0.5613279724121094, \"min\": 0.5613279724121094}}, \"EndTime\": 1547633728.869739, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869688}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6108528900146485, \"sum\": 0.6108528900146485, \"min\": 0.6108528900146485}}, \"EndTime\": 1547633728.869929, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869796}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7375724029541015, \"sum\": 0.7375724029541015, \"min\": 0.7375724029541015}}, \"EndTime\": 1547633728.870012, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.869991}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0746280670166015, \"sum\": 1.0746280670166015, \"min\": 1.0746280670166015}}, \"EndTime\": 1547633728.870278, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870255}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6966555023193359, \"sum\": 0.6966555023193359, \"min\": 0.6966555023193359}}, \"EndTime\": 1547633728.870341, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870325}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8702118682861328, \"sum\": 0.8702118682861328, \"min\": 0.8702118682861328}}, \"EndTime\": 1547633728.870402, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870385}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5771385192871094, \"sum\": 0.5771385192871094, \"min\": 0.5771385192871094}}, \"EndTime\": 1547633728.870459, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870444}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6190734481811524, \"sum\": 0.6190734481811524, \"min\": 0.6190734481811524}}, \"EndTime\": 1547633728.870542, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870523}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6709259033203125, \"sum\": 0.6709259033203125, \"min\": 0.6709259033203125}}, \"EndTime\": 1547633728.870602, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870586}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5420879745483398, \"sum\": 0.5420879745483398, \"min\": 0.5420879745483398}}, \"EndTime\": 1547633728.870658, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870643}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7964575958251953, \"sum\": 0.7964575958251953, \"min\": 0.7964575958251953}}, \"EndTime\": 1547633728.870713, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870698}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6822406768798828, \"sum\": 0.6822406768798828, \"min\": 0.6822406768798828}}, \"EndTime\": 1547633728.870768, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870753}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9546888732910156, \"sum\": 0.9546888732910156, \"min\": 0.9546888732910156}}, \"EndTime\": 1547633728.870824, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870808}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8013351440429688, \"sum\": 0.8013351440429688, \"min\": 0.8013351440429688}}, \"EndTime\": 1547633728.870879, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870864}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6203229904174805, \"sum\": 0.6203229904174805, \"min\": 0.6203229904174805}}, \"EndTime\": 1547633728.870933, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870919}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6153883361816406, \"sum\": 0.6153883361816406, \"min\": 0.6153883361816406}}, \"EndTime\": 1547633728.87099, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.870974}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7447660827636718, \"sum\": 0.7447660827636718, \"min\": 0.7447660827636718}}, \"EndTime\": 1547633728.871044, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7312835693359375, \"sum\": 0.7312835693359375, \"min\": 0.7312835693359375}}, \"EndTime\": 1547633728.87114, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871109}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.958655014038086, \"sum\": 0.958655014038086, \"min\": 0.958655014038086}}, \"EndTime\": 1547633728.8712, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871183}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9683676910400391, \"sum\": 0.9683676910400391, \"min\": 0.9683676910400391}}, \"EndTime\": 1547633728.871254, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.87124}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0385859680175782, \"sum\": 1.0385859680175782, \"min\": 1.0385859680175782}}, \"EndTime\": 1547633728.871309, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871294}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.045322494506836, \"sum\": 1.045322494506836, \"min\": 1.045322494506836}}, \"EndTime\": 1547633728.871364, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871349}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.28932373046875, \"sum\": 1.28932373046875, \"min\": 1.28932373046875}}, \"EndTime\": 1547633728.871418, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871403}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1670755004882813, \"sum\": 1.1670755004882813, \"min\": 1.1670755004882813}}, \"EndTime\": 1547633728.871479, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871463}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3995591735839843, \"sum\": 1.3995591735839843, \"min\": 1.3995591735839843}}, \"EndTime\": 1547633728.871535, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871519}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.3377330017089843, \"sum\": 1.3377330017089843, \"min\": 1.3377330017089843}}, \"EndTime\": 1547633728.87159, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.871576}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=5, train mse_objective <loss>=0.734494476318\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 44.09539358956473, \"sum\": 44.09539358956473, \"min\": 44.09539358956473}}, \"EndTime\": 1547633728.931665, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931611}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.015228271484375, \"sum\": 41.015228271484375, \"min\": 41.015228271484375}}, \"EndTime\": 1547633728.931741, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931727}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.408355712890625, \"sum\": 61.408355712890625, \"min\": 61.408355712890625}}, \"EndTime\": 1547633728.931804, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931788}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.859950474330354, \"sum\": 40.859950474330354, \"min\": 40.859950474330354}}, \"EndTime\": 1547633728.931841, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931832}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 32.57436043875558, \"sum\": 32.57436043875558, \"min\": 32.57436043875558}}, \"EndTime\": 1547633728.931894, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931879}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.01054818289621, \"sum\": 33.01054818289621, \"min\": 33.01054818289621}}, \"EndTime\": 1547633728.931948, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931933}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 31.154737200055802, \"sum\": 31.154737200055802, \"min\": 31.154737200055802}}, \"EndTime\": 1547633728.932003, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.931987}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.31862313406808, \"sum\": 35.31862313406808, \"min\": 35.31862313406808}}, \"EndTime\": 1547633728.932058, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932043}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.50789533342634, \"sum\": 45.50789533342634, \"min\": 45.50789533342634}}, \"EndTime\": 1547633728.932114, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932098}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 66.25492204938617, \"sum\": 66.25492204938617, \"min\": 66.25492204938617}}, \"EndTime\": 1547633728.93217, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932154}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.73541259765625, \"sum\": 40.73541259765625, \"min\": 40.73541259765625}}, \"EndTime\": 1547633728.932227, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932211}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.277352469308035, \"sum\": 51.277352469308035, \"min\": 51.277352469308035}}, \"EndTime\": 1547633728.932284, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932268}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.44832284109933, \"sum\": 33.44832284109933, \"min\": 33.44832284109933}}, \"EndTime\": 1547633728.932341, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932325}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.67986842564174, \"sum\": 34.67986842564174, \"min\": 34.67986842564174}}, \"EndTime\": 1547633728.932396, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932381}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.40381513323103, \"sum\": 35.40381513323103, \"min\": 35.40381513323103}}, \"EndTime\": 1547633728.932453, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932437}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 31.790298461914062, \"sum\": 31.790298461914062, \"min\": 31.790298461914062}}, \"EndTime\": 1547633728.93251, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932494}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.70137241908482, \"sum\": 51.70137241908482, \"min\": 51.70137241908482}}, \"EndTime\": 1547633728.932567, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932551}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.596422467912944, \"sum\": 40.596422467912944, \"min\": 40.596422467912944}}, \"EndTime\": 1547633728.932621, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932606}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.10341099330357, \"sum\": 62.10341099330357, \"min\": 62.10341099330357}}, \"EndTime\": 1547633728.932677, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932661}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 52.602831159319194, \"sum\": 52.602831159319194, \"min\": 52.602831159319194}}, \"EndTime\": 1547633728.932733, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932718}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.467237200055802, \"sum\": 28.467237200055802, \"min\": 28.467237200055802}}, \"EndTime\": 1547633728.93279, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932775}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 38.446201869419646, \"sum\": 38.446201869419646, \"min\": 38.446201869419646}}, \"EndTime\": 1547633728.932856, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.93284}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.92445591517857, \"sum\": 35.92445591517857, \"min\": 35.92445591517857}}, \"EndTime\": 1547633728.93293, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932917}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.55611964634487, \"sum\": 36.55611964634487, \"min\": 36.55611964634487}}, \"EndTime\": 1547633728.932986, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.932971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.201590401785715, \"sum\": 62.201590401785715, \"min\": 62.201590401785715}}, \"EndTime\": 1547633728.93304, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933025}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.90991646902902, \"sum\": 62.90991646902902, \"min\": 62.90991646902902}}, \"EndTime\": 1547633728.933096, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933081}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 65.22427368164062, \"sum\": 65.22427368164062, \"min\": 65.22427368164062}}, \"EndTime\": 1547633728.933152, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933136}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 66.17629568917411, \"sum\": 66.17629568917411, \"min\": 66.17629568917411}}, \"EndTime\": 1547633728.933208, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933193}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 75.51453508649554, \"sum\": 75.51453508649554, \"min\": 75.51453508649554}}, \"EndTime\": 1547633728.933263, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933247}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 73.82278006417411, \"sum\": 73.82278006417411, \"min\": 73.82278006417411}}, \"EndTime\": 1547633728.933318, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933303}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 83.28521728515625, \"sum\": 83.28521728515625, \"min\": 83.28521728515625}}, \"EndTime\": 1547633728.933374, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933359}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 77.45584542410714, \"sum\": 77.45584542410714, \"min\": 77.45584542410714}}, \"EndTime\": 1547633728.933429, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.933414}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=5, validation mse_objective <loss>=44.0953935896\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=mse_objective, value=28.4672372001\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Total Records Seen\": {\"count\": 1, \"max\": 1789, \"sum\": 1789.0, \"min\": 1789}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1547633728.934358, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1547633728.834035}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2259.92107306 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.698463363647461, \"sum\": 0.698463363647461, \"min\": 0.698463363647461}}, \"EndTime\": 1547633728.964336, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964252}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6483576965332031, \"sum\": 0.6483576965332031, \"min\": 0.6483576965332031}}, \"EndTime\": 1547633728.964423, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964404}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.963035888671875, \"sum\": 0.963035888671875, \"min\": 0.963035888671875}}, \"EndTime\": 1547633728.964485, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964469}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6517003631591797, \"sum\": 0.6517003631591797, \"min\": 0.6517003631591797}}, \"EndTime\": 1547633728.964542, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964527}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4610425567626953, \"sum\": 0.4610425567626953, \"min\": 0.4610425567626953}}, \"EndTime\": 1547633728.964597, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964582}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4396144104003906, \"sum\": 0.4396144104003906, \"min\": 0.4396144104003906}}, \"EndTime\": 1547633728.96465, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964636}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.43145965576171874, \"sum\": 0.43145965576171874, \"min\": 0.43145965576171874}}, \"EndTime\": 1547633728.964703, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964689}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4727622604370117, \"sum\": 0.4727622604370117, \"min\": 0.4727622604370117}}, \"EndTime\": 1547633728.964757, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964743}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7086281585693359, \"sum\": 0.7086281585693359, \"min\": 0.7086281585693359}}, \"EndTime\": 1547633728.964809, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964795}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0157626342773438, \"sum\": 1.0157626342773438, \"min\": 1.0157626342773438}}, \"EndTime\": 1547633728.964881, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964847}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.670250015258789, \"sum\": 0.670250015258789, \"min\": 0.670250015258789}}, \"EndTime\": 1547633728.964937, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964923}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8294898223876953, \"sum\": 0.8294898223876953, \"min\": 0.8294898223876953}}, \"EndTime\": 1547633728.96499, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.964976}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.46274139404296877, \"sum\": 0.46274139404296877, \"min\": 0.46274139404296877}}, \"EndTime\": 1547633728.965042, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.48715946197509763, \"sum\": 0.48715946197509763, \"min\": 0.48715946197509763}}, \"EndTime\": 1547633728.965098, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965084}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.48145614624023436, \"sum\": 0.48145614624023436, \"min\": 0.48145614624023436}}, \"EndTime\": 1547633728.965151, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965137}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4344316864013672, \"sum\": 0.4344316864013672, \"min\": 0.4344316864013672}}, \"EndTime\": 1547633728.965203, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.96519}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7568251037597656, \"sum\": 0.7568251037597656, \"min\": 0.7568251037597656}}, \"EndTime\": 1547633728.965256, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965241}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6545741271972656, \"sum\": 0.6545741271972656, \"min\": 0.6545741271972656}}, \"EndTime\": 1547633728.965307, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965293}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9108063507080079, \"sum\": 0.9108063507080079, \"min\": 0.9108063507080079}}, \"EndTime\": 1547633728.965358, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965344}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7645771789550782, \"sum\": 0.7645771789550782, \"min\": 0.7645771789550782}}, \"EndTime\": 1547633728.96541, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965396}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.36882144927978516, \"sum\": 0.36882144927978516, \"min\": 0.36882144927978516}}, \"EndTime\": 1547633728.965461, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5368477630615235, \"sum\": 0.5368477630615235, \"min\": 0.5368477630615235}}, \"EndTime\": 1547633728.965513, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.9655}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.47596656799316406, \"sum\": 0.47596656799316406, \"min\": 0.47596656799316406}}, \"EndTime\": 1547633728.965565, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965551}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5016366958618164, \"sum\": 0.5016366958618164, \"min\": 0.5016366958618164}}, \"EndTime\": 1547633728.965618, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965604}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9659049224853515, \"sum\": 0.9659049224853515, \"min\": 0.9659049224853515}}, \"EndTime\": 1547633728.965669, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965655}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9649933624267578, \"sum\": 0.9649933624267578, \"min\": 0.9649933624267578}}, \"EndTime\": 1547633728.965721, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965708}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.009905776977539, \"sum\": 1.009905776977539, \"min\": 1.009905776977539}}, \"EndTime\": 1547633728.965771, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965757}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0224309539794922, \"sum\": 1.0224309539794922, \"min\": 1.0224309539794922}}, \"EndTime\": 1547633728.965822, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965809}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0992240142822265, \"sum\": 1.0992240142822265, \"min\": 1.0992240142822265}}, \"EndTime\": 1547633728.965874, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.96586}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0596477508544921, \"sum\": 1.0596477508544921, \"min\": 1.0596477508544921}}, \"EndTime\": 1547633728.965925, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965912}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.18626220703125, \"sum\": 1.18626220703125, \"min\": 1.18626220703125}}, \"EndTime\": 1547633728.965976, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.965963}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.109506607055664, \"sum\": 1.109506607055664, \"min\": 1.109506607055664}}, \"EndTime\": 1547633728.966026, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.966013}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:28 INFO 139926660151104] #quality_metric: host=algo-1, epoch=6, train mse_objective <loss>=0.698463363647\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.011954171316965, \"sum\": 42.011954171316965, \"min\": 42.011954171316965}}, \"EndTime\": 1547633729.019382, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019324}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.45650373186384, \"sum\": 39.45650373186384, \"min\": 39.45650373186384}}, \"EndTime\": 1547633729.019464, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019444}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 57.89245169503348, \"sum\": 57.89245169503348, \"min\": 57.89245169503348}}, \"EndTime\": 1547633729.019529, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019512}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.18097359793527, \"sum\": 39.18097359793527, \"min\": 39.18097359793527}}, \"EndTime\": 1547633729.019629, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.01961}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 23.623580932617188, \"sum\": 23.623580932617188, \"min\": 23.623580932617188}}, \"EndTime\": 1547633729.019692, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019675}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.079677036830358, \"sum\": 28.079677036830358, \"min\": 28.079677036830358}}, \"EndTime\": 1547633729.01975, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019735}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.629119873046875, \"sum\": 28.629119873046875, \"min\": 28.629119873046875}}, \"EndTime\": 1547633729.019809, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019793}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.04040309361049, \"sum\": 28.04040309361049, \"min\": 28.04040309361049}}, \"EndTime\": 1547633729.019867, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019852}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.92478724888393, \"sum\": 43.92478724888393, \"min\": 43.92478724888393}}, \"EndTime\": 1547633729.019923, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019908}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.50374494280134, \"sum\": 62.50374494280134, \"min\": 62.50374494280134}}, \"EndTime\": 1547633729.019979, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.019963}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.29700142996652, \"sum\": 39.29700142996652, \"min\": 39.29700142996652}}, \"EndTime\": 1547633729.020036, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020019}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.86689976283482, \"sum\": 48.86689976283482, \"min\": 48.86689976283482}}, \"EndTime\": 1547633729.020093, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020078}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 27.913763863699778, \"sum\": 27.913763863699778, \"min\": 27.913763863699778}}, \"EndTime\": 1547633729.020148, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020133}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 25.48948233468192, \"sum\": 25.48948233468192, \"min\": 25.48948233468192}}, \"EndTime\": 1547633729.020203, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020188}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 24.873380388532365, \"sum\": 24.873380388532365, \"min\": 24.873380388532365}}, \"EndTime\": 1547633729.020258, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020243}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 29.50675528390067, \"sum\": 29.50675528390067, \"min\": 29.50675528390067}}, \"EndTime\": 1547633729.020313, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 49.408216203962056, \"sum\": 49.408216203962056, \"min\": 49.408216203962056}}, \"EndTime\": 1547633729.020369, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020353}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.15997314453125, \"sum\": 39.15997314453125, \"min\": 39.15997314453125}}, \"EndTime\": 1547633729.02045, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020434}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 59.425789969308035, \"sum\": 59.425789969308035, \"min\": 59.425789969308035}}, \"EndTime\": 1547633729.020507, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020491}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 50.409109933035715, \"sum\": 50.409109933035715, \"min\": 50.409109933035715}}, \"EndTime\": 1547633729.02058, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020564}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 20.510977608816965, \"sum\": 20.510977608816965, \"min\": 20.510977608816965}}, \"EndTime\": 1547633729.02064, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020624}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 31.783724103655135, \"sum\": 31.783724103655135, \"min\": 31.783724103655135}}, \"EndTime\": 1547633729.020716, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.02068}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 21.98179190499442, \"sum\": 21.98179190499442, \"min\": 21.98179190499442}}, \"EndTime\": 1547633729.020775, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.02076}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 23.042755126953125, \"sum\": 23.042755126953125, \"min\": 23.042755126953125}}, \"EndTime\": 1547633729.020841, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020824}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.67093767438616, \"sum\": 62.67093767438616, \"min\": 62.67093767438616}}, \"EndTime\": 1547633729.020909, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020892}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.720249720982146, \"sum\": 62.720249720982146, \"min\": 62.720249720982146}}, \"EndTime\": 1547633729.020974, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.020958}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.524549211774556, \"sum\": 63.524549211774556, \"min\": 63.524549211774556}}, \"EndTime\": 1547633729.021035, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.021018}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.73936244419643, \"sum\": 64.73936244419643, \"min\": 64.73936244419643}}, \"EndTime\": 1547633729.0211, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.021084}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.56934465680804, \"sum\": 64.56934465680804, \"min\": 64.56934465680804}}, \"EndTime\": 1547633729.021157, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.021141}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 70.18548583984375, \"sum\": 70.18548583984375, \"min\": 70.18548583984375}}, \"EndTime\": 1547633729.021215, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.021199}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 65.47222028459821, \"sum\": 65.47222028459821, \"min\": 65.47222028459821}}, \"EndTime\": 1547633729.021273, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.021258}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.803597586495535, \"sum\": 60.803597586495535, \"min\": 60.803597586495535}}, \"EndTime\": 1547633729.021329, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633729.021313}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=6, validation mse_objective <loss>=42.0119541713\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=mse_objective, value=20.5109776088\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] Epoch 6: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 46 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}, \"Total Records Seen\": {\"count\": 1, \"max\": 2016, \"sum\": 2016.0, \"min\": 2016}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1547633729.023065, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1547633728.934695}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2564.62563368 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6662816619873047, \"sum\": 0.6662816619873047, \"min\": 0.6662816619873047}}, \"EndTime\": 1547633729.049312, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.049232}\n",
      "\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=7, train mse_objective <loss>=0.666281661987\u001b[0m\n",
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      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 29.791878836495535, \"sum\": 29.791878836495535, \"min\": 29.791878836495535}}, \"EndTime\": 1547633729.10214, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102133}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 31.17661394391741, \"sum\": 31.17661394391741, \"min\": 31.17661394391741}}, \"EndTime\": 1547633729.102167, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.10216}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.223536900111604, \"sum\": 63.223536900111604, \"min\": 63.223536900111604}}, \"EndTime\": 1547633729.102208, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102195}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.59235055106027, \"sum\": 62.59235055106027, \"min\": 62.59235055106027}}, \"EndTime\": 1547633729.102261, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102246}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.19016810825893, \"sum\": 62.19016810825893, \"min\": 62.19016810825893}}, \"EndTime\": 1547633729.102313, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102299}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.521388462611604, \"sum\": 63.521388462611604, \"min\": 63.521388462611604}}, \"EndTime\": 1547633729.102362, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102349}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 72.4409659249442, \"sum\": 72.4409659249442, \"min\": 72.4409659249442}}, \"EndTime\": 1547633729.102452, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102435}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 82.08198765345982, \"sum\": 82.08198765345982, \"min\": 82.08198765345982}}, \"EndTime\": 1547633729.102503, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.10249}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 67.1807381766183, \"sum\": 67.1807381766183, \"min\": 67.1807381766183}}, \"EndTime\": 1547633729.102571, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.102556}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.36503383091518, \"sum\": 64.36503383091518, \"min\": 64.36503383091518}}, \"EndTime\": 1547633729.102623, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.10261}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=7, validation mse_objective <loss>=40.2483171735\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=mse_objective, value=29.7918788365\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 53 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 19, \"sum\": 19.0, \"min\": 19}, \"Total Records Seen\": {\"count\": 1, \"max\": 2243, \"sum\": 2243.0, \"min\": 2243}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1547633729.103614, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1547633729.02335}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2823.17413646 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6380550003051758, \"sum\": 0.6380550003051758, \"min\": 0.6380550003051758}}, \"EndTime\": 1547633729.12977, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.129721}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6013260269165039, \"sum\": 0.6013260269165039, \"min\": 0.6013260269165039}}, \"EndTime\": 1547633729.129833, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.12982}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8644845581054688, \"sum\": 0.8644845581054688, \"min\": 0.8644845581054688}}, \"EndTime\": 1547633729.129898, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.12988}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.604074592590332, \"sum\": 0.604074592590332, \"min\": 0.604074592590332}}, \"EndTime\": 1547633729.129948, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.129933}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4076205825805664, \"sum\": 0.4076205825805664, \"min\": 0.4076205825805664}}, \"EndTime\": 1547633729.130009, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.129993}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4791893005371094, \"sum\": 0.4791893005371094, \"min\": 0.4791893005371094}}, \"EndTime\": 1547633729.130072, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130056}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4849600982666016, \"sum\": 0.4849600982666016, \"min\": 0.4849600982666016}}, \"EndTime\": 1547633729.130129, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130114}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4628951644897461, \"sum\": 0.4628951644897461, \"min\": 0.4628951644897461}}, \"EndTime\": 1547633729.130195, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130178}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.661448974609375, \"sum\": 0.661448974609375, \"min\": 0.661448974609375}}, \"EndTime\": 1547633729.130259, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130243}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9103706359863282, \"sum\": 0.9103706359863282, \"min\": 0.9103706359863282}}, \"EndTime\": 1547633729.130321, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130305}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6264045715332032, \"sum\": 0.6264045715332032, \"min\": 0.6264045715332032}}, \"EndTime\": 1547633729.130376, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130361}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.759632339477539, \"sum\": 0.759632339477539, \"min\": 0.759632339477539}}, \"EndTime\": 1547633729.130433, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130417}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4345566177368164, \"sum\": 0.4345566177368164, \"min\": 0.4345566177368164}}, \"EndTime\": 1547633729.130487, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130472}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4163863372802734, \"sum\": 0.4163863372802734, \"min\": 0.4163863372802734}}, \"EndTime\": 1547633729.130572, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130556}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.39880115509033204, \"sum\": 0.39880115509033204, \"min\": 0.39880115509033204}}, \"EndTime\": 1547633729.130628, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130613}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.47647594451904296, \"sum\": 0.47647594451904296, \"min\": 0.47647594451904296}}, \"EndTime\": 1547633729.13069, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130675}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6897097778320312, \"sum\": 0.6897097778320312, \"min\": 0.6897097778320312}}, \"EndTime\": 1547633729.130751, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130735}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6077847671508789, \"sum\": 0.6077847671508789, \"min\": 0.6077847671508789}}, \"EndTime\": 1547633729.130808, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130792}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.835110855102539, \"sum\": 0.835110855102539, \"min\": 0.835110855102539}}, \"EndTime\": 1547633729.130864, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130849}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7025080108642578, \"sum\": 0.7025080108642578, \"min\": 0.7025080108642578}}, \"EndTime\": 1547633729.130916, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130902}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.387067985534668, \"sum\": 0.387067985534668, \"min\": 0.387067985534668}}, \"EndTime\": 1547633729.130977, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.130962}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.48343231201171877, \"sum\": 0.48343231201171877, \"min\": 0.48343231201171877}}, \"EndTime\": 1547633729.131033, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131018}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3599483871459961, \"sum\": 0.3599483871459961, \"min\": 0.3599483871459961}}, \"EndTime\": 1547633729.131089, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131074}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.36996356964111327, \"sum\": 0.36996356964111327, \"min\": 0.36996356964111327}}, \"EndTime\": 1547633729.131143, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131128}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9843119812011719, \"sum\": 0.9843119812011719, \"min\": 0.9843119812011719}}, \"EndTime\": 1547633729.1312, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131185}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9589663696289062, \"sum\": 0.9589663696289062, \"min\": 0.9589663696289062}}, \"EndTime\": 1547633729.131256, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.13124}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9685313415527343, \"sum\": 0.9685313415527343, \"min\": 0.9685313415527343}}, \"EndTime\": 1547633729.131313, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131298}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9829354095458984, \"sum\": 0.9829354095458984, \"min\": 0.9829354095458984}}, \"EndTime\": 1547633729.13137, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131354}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.068667449951172, \"sum\": 1.068667449951172, \"min\": 1.068667449951172}}, \"EndTime\": 1547633729.131425, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131409}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.221041259765625, \"sum\": 1.221041259765625, \"min\": 1.221041259765625}}, \"EndTime\": 1547633729.131481, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131465}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0207620239257813, \"sum\": 1.0207620239257813, \"min\": 1.0207620239257813}}, \"EndTime\": 1547633729.131535, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.13152}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0084745788574219, \"sum\": 1.0084745788574219, \"min\": 1.0084745788574219}}, \"EndTime\": 1547633729.131591, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.131575}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=8, train mse_objective <loss>=0.638055000305\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 38.794969831194194, \"sum\": 38.794969831194194, \"min\": 38.794969831194194}}, \"EndTime\": 1547633729.184063, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.183982}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.183829171316965, \"sum\": 37.183829171316965, \"min\": 37.183829171316965}}, \"EndTime\": 1547633729.184184, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184164}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 51.87461635044643, \"sum\": 51.87461635044643, \"min\": 51.87461635044643}}, \"EndTime\": 1547633729.184241, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184228}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.765328543526785, \"sum\": 36.765328543526785, \"min\": 36.765328543526785}}, \"EndTime\": 1547633729.184291, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184279}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 47.17205374581473, \"sum\": 47.17205374581473, \"min\": 47.17205374581473}}, \"EndTime\": 1547633729.184339, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184327}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.605438232421875, \"sum\": 43.605438232421875, \"min\": 43.605438232421875}}, \"EndTime\": 1547633729.184385, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184373}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 44.29238019670759, \"sum\": 44.29238019670759, \"min\": 44.29238019670759}}, \"EndTime\": 1547633729.184431, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184419}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.651925223214285, \"sum\": 45.651925223214285, \"min\": 45.651925223214285}}, \"EndTime\": 1547633729.184477, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184465}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.6197509765625, \"sum\": 41.6197509765625, \"min\": 41.6197509765625}}, \"EndTime\": 1547633729.184538, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184511}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 55.999668666294646, \"sum\": 55.999668666294646, \"min\": 55.999668666294646}}, \"EndTime\": 1547633729.18459, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184577}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.18770054408482, \"sum\": 37.18770054408482, \"min\": 37.18770054408482}}, \"EndTime\": 1547633729.184636, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184625}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 44.962306431361604, \"sum\": 44.962306431361604, \"min\": 44.962306431361604}}, \"EndTime\": 1547633729.184685, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184673}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.707528250558035, \"sum\": 43.707528250558035, \"min\": 43.707528250558035}}, \"EndTime\": 1547633729.184731, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.18472}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 44.005384172712056, \"sum\": 44.005384172712056, \"min\": 44.005384172712056}}, \"EndTime\": 1547633729.184777, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184765}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.43601771763393, \"sum\": 48.43601771763393, \"min\": 48.43601771763393}}, \"EndTime\": 1547633729.184822, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184811}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.9390869140625, \"sum\": 42.9390869140625, \"min\": 42.9390869140625}}, \"EndTime\": 1547633729.184867, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184856}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.759225027901785, \"sum\": 45.759225027901785, \"min\": 45.759225027901785}}, \"EndTime\": 1547633729.184913, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184901}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.95800345284598, \"sum\": 36.95800345284598, \"min\": 36.95800345284598}}, \"EndTime\": 1547633729.184958, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184947}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 55.068206787109375, \"sum\": 55.068206787109375, \"min\": 55.068206787109375}}, \"EndTime\": 1547633729.185004, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.184992}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.94829886300223, \"sum\": 46.94829886300223, \"min\": 46.94829886300223}}, \"EndTime\": 1547633729.185049, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185037}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.18999154227121, \"sum\": 35.18999154227121, \"min\": 35.18999154227121}}, \"EndTime\": 1547633729.185096, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185084}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.40961783272879, \"sum\": 35.40961783272879, \"min\": 35.40961783272879}}, \"EndTime\": 1547633729.185142, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.18513}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 38.417471749441965, \"sum\": 38.417471749441965, \"min\": 38.417471749441965}}, \"EndTime\": 1547633729.185187, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185175}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.24391828264509, \"sum\": 41.24391828264509, \"min\": 41.24391828264509}}, \"EndTime\": 1547633729.185233, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185221}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.74440656389509, \"sum\": 63.74440656389509, \"min\": 63.74440656389509}}, \"EndTime\": 1547633729.185278, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185266}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.521432059151785, \"sum\": 62.521432059151785, \"min\": 62.521432059151785}}, \"EndTime\": 1547633729.185323, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185312}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.17354038783482, \"sum\": 61.17354038783482, \"min\": 61.17354038783482}}, \"EndTime\": 1547633729.185369, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185357}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.55051967075893, \"sum\": 62.55051967075893, \"min\": 62.55051967075893}}, \"EndTime\": 1547633729.185414, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185402}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 78.52689906529018, \"sum\": 78.52689906529018, \"min\": 78.52689906529018}}, \"EndTime\": 1547633729.185459, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185448}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 85.4500732421875, \"sum\": 85.4500732421875, \"min\": 85.4500732421875}}, \"EndTime\": 1547633729.185504, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185493}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 70.56032453264508, \"sum\": 70.56032453264508, \"min\": 70.56032453264508}}, \"EndTime\": 1547633729.185549, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.185538}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 71.15159824916294, \"sum\": 71.15159824916294, \"min\": 71.15159824916294}}, \"EndTime\": 1547633729.185603, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.18559}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=8, validation mse_objective <loss>=38.7949698312\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=mse_objective, value=35.1899915423\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 21, \"sum\": 21.0, \"min\": 21}, \"Total Records Seen\": {\"count\": 1, \"max\": 2470, \"sum\": 2470.0, \"min\": 2470}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}}, \"EndTime\": 1547633729.186634, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1547633729.103876}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2738.40266677 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6130456161499024, \"sum\": 0.6130456161499024, \"min\": 0.6130456161499024}}, \"EndTime\": 1547633729.220062, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220012}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5821455001831055, \"sum\": 0.5821455001831055, \"min\": 0.5821455001831055}}, \"EndTime\": 1547633729.220136, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220118}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8211900329589844, \"sum\": 0.8211900329589844, \"min\": 0.8211900329589844}}, \"EndTime\": 1547633729.220201, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220185}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5849679946899414, \"sum\": 0.5849679946899414, \"min\": 0.5849679946899414}}, \"EndTime\": 1547633729.220258, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220242}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5195850372314453, \"sum\": 0.5195850372314453, \"min\": 0.5195850372314453}}, \"EndTime\": 1547633729.220315, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.2203}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5076587295532227, \"sum\": 0.5076587295532227, \"min\": 0.5076587295532227}}, \"EndTime\": 1547633729.220373, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220357}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5043251037597656, \"sum\": 0.5043251037597656, \"min\": 0.5043251037597656}}, \"EndTime\": 1547633729.22043, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220415}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5193427658081055, \"sum\": 0.5193427658081055, \"min\": 0.5193427658081055}}, \"EndTime\": 1547633729.220487, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220471}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6424230194091797, \"sum\": 0.6424230194091797, \"min\": 0.6424230194091797}}, \"EndTime\": 1547633729.220544, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220528}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8634647369384766, \"sum\": 0.8634647369384766, \"min\": 0.8634647369384766}}, \"EndTime\": 1547633729.220599, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220583}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6081224060058594, \"sum\": 0.6081224060058594, \"min\": 0.6081224060058594}}, \"EndTime\": 1547633729.220655, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.22064}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7298431396484375, \"sum\": 0.7298431396484375, \"min\": 0.7298431396484375}}, \"EndTime\": 1547633729.22071, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220695}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4796131134033203, \"sum\": 0.4796131134033203, \"min\": 0.4796131134033203}}, \"EndTime\": 1547633729.220767, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220752}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4983633422851563, \"sum\": 0.4983633422851563, \"min\": 0.4983633422851563}}, \"EndTime\": 1547633729.220823, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220807}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5219110870361328, \"sum\": 0.5219110870361328, \"min\": 0.5219110870361328}}, \"EndTime\": 1547633729.220879, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4822005081176758, \"sum\": 0.4822005081176758, \"min\": 0.4822005081176758}}, \"EndTime\": 1547633729.220935, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220919}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.661695556640625, \"sum\": 0.661695556640625, \"min\": 0.661695556640625}}, \"EndTime\": 1547633729.22099, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.220975}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5879040145874024, \"sum\": 0.5879040145874024, \"min\": 0.5879040145874024}}, \"EndTime\": 1547633729.221044, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221029}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8026915740966797, \"sum\": 0.8026915740966797, \"min\": 0.8026915740966797}}, \"EndTime\": 1547633729.2211, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221085}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.676435546875, \"sum\": 0.676435546875, \"min\": 0.676435546875}}, \"EndTime\": 1547633729.221155, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.22114}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4730760955810547, \"sum\": 0.4730760955810547, \"min\": 0.4730760955810547}}, \"EndTime\": 1547633729.22121, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221195}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.45710971832275393, \"sum\": 0.45710971832275393, \"min\": 0.45710971832275393}}, \"EndTime\": 1547633729.221265, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.22125}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4701373291015625, \"sum\": 0.4701373291015625, \"min\": 0.4701373291015625}}, \"EndTime\": 1547633729.221321, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221306}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4767938232421875, \"sum\": 0.4767938232421875, \"min\": 0.4767938232421875}}, \"EndTime\": 1547633729.221377, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221361}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9919643402099609, \"sum\": 0.9919643402099609, \"min\": 0.9919643402099609}}, \"EndTime\": 1547633729.22143, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221416}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9568095397949219, \"sum\": 0.9568095397949219, \"min\": 0.9568095397949219}}, \"EndTime\": 1547633729.221486, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.22147}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9538040161132812, \"sum\": 0.9538040161132812, \"min\": 0.9538040161132812}}, \"EndTime\": 1547633729.221542, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221527}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9675908660888672, \"sum\": 0.9675908660888672, \"min\": 0.9675908660888672}}, \"EndTime\": 1547633729.221598, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221583}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.1263502502441407, \"sum\": 1.1263502502441407, \"min\": 1.1263502502441407}}, \"EndTime\": 1547633729.221655, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221639}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.2615953063964844, \"sum\": 1.2615953063964844, \"min\": 1.2615953063964844}}, \"EndTime\": 1547633729.221712, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221696}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0467013549804687, \"sum\": 1.0467013549804687, \"min\": 1.0467013549804687}}, \"EndTime\": 1547633729.221767, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221752}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0893254089355469, \"sum\": 1.0893254089355469, \"min\": 1.0893254089355469}}, \"EndTime\": 1547633729.221831, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.221815}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=9, train mse_objective <loss>=0.61304561615\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.618076869419646, \"sum\": 37.618076869419646, \"min\": 37.618076869419646}}, \"EndTime\": 1547633729.2606, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.26052}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.420824323381694, \"sum\": 36.420824323381694, \"min\": 36.420824323381694}}, \"EndTime\": 1547633729.260667, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260652}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 49.354173932756694, \"sum\": 49.354173932756694, \"min\": 49.354173932756694}}, \"EndTime\": 1547633729.260731, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260714}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.99941798618862, \"sum\": 35.99941798618862, \"min\": 35.99941798618862}}, \"EndTime\": 1547633729.260771, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260762}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.395738874162944, \"sum\": 43.395738874162944, \"min\": 43.395738874162944}}, \"EndTime\": 1547633729.260801, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260794}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.13197980608259, \"sum\": 37.13197980608259, \"min\": 37.13197980608259}}, \"EndTime\": 1547633729.26083, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260823}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.407278878348215, \"sum\": 37.407278878348215, \"min\": 37.407278878348215}}, \"EndTime\": 1547633729.260866, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260852}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.986868722098215, \"sum\": 41.986868722098215, \"min\": 41.986868722098215}}, \"EndTime\": 1547633729.260921, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260905}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.85072544642857, \"sum\": 40.85072544642857, \"min\": 40.85072544642857}}, \"EndTime\": 1547633729.260978, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.260962}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 53.216823032924104, \"sum\": 53.216823032924104, \"min\": 53.216823032924104}}, \"EndTime\": 1547633729.261034, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261019}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.47869219098772, \"sum\": 36.47869219098772, \"min\": 36.47869219098772}}, \"EndTime\": 1547633729.26109, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261075}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.41210501534598, \"sum\": 43.41210501534598, \"min\": 43.41210501534598}}, \"EndTime\": 1547633729.261145, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261129}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.02454049246652, \"sum\": 40.02454049246652, \"min\": 40.02454049246652}}, \"EndTime\": 1547633729.26121, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261193}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.547459193638396, \"sum\": 42.547459193638396, \"min\": 42.547459193638396}}, \"EndTime\": 1547633729.261274, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261258}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.207833426339285, \"sum\": 45.207833426339285, \"min\": 45.207833426339285}}, \"EndTime\": 1547633729.261338, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261323}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.01564025878906, \"sum\": 36.01564025878906, \"min\": 36.01564025878906}}, \"EndTime\": 1547633729.2614, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261383}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 44.348728724888396, \"sum\": 44.348728724888396, \"min\": 44.348728724888396}}, \"EndTime\": 1547633729.261466, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.26145}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.12821088518415, \"sum\": 36.12821088518415, \"min\": 36.12821088518415}}, \"EndTime\": 1547633729.261532, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261515}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 53.35697283063616, \"sum\": 53.35697283063616, \"min\": 53.35697283063616}}, \"EndTime\": 1547633729.261588, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261573}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.638828822544646, \"sum\": 45.638828822544646, \"min\": 45.638828822544646}}, \"EndTime\": 1547633729.261643, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261628}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 29.728836059570312, \"sum\": 29.728836059570312, \"min\": 29.728836059570312}}, \"EndTime\": 1547633729.261692, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261679}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.631862095424108, \"sum\": 28.631862095424108, \"min\": 28.631862095424108}}, \"EndTime\": 1547633729.261746, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261731}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.70162745884487, \"sum\": 33.70162745884487, \"min\": 33.70162745884487}}, \"EndTime\": 1547633729.261801, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261786}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.544586181640625, \"sum\": 37.544586181640625, \"min\": 37.544586181640625}}, \"EndTime\": 1547633729.261855, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.26184}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.15235246930804, \"sum\": 64.15235246930804, \"min\": 64.15235246930804}}, \"EndTime\": 1547633729.261908, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261894}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.479200090680806, \"sum\": 62.479200090680806, \"min\": 62.479200090680806}}, \"EndTime\": 1547633729.261964, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.261948}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.405857631138396, \"sum\": 60.405857631138396, \"min\": 60.405857631138396}}, \"EndTime\": 1547633729.262019, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.262004}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.81797136579241, \"sum\": 61.81797136579241, \"min\": 61.81797136579241}}, \"EndTime\": 1547633729.262073, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.262058}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 70.96509660993304, \"sum\": 70.96509660993304, \"min\": 70.96509660993304}}, \"EndTime\": 1547633729.262127, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.262112}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 72.05500139508929, \"sum\": 72.05500139508929, \"min\": 72.05500139508929}}, \"EndTime\": 1547633729.262182, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.262166}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.753448486328125, \"sum\": 63.753448486328125, \"min\": 63.753448486328125}}, \"EndTime\": 1547633729.262237, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.262222}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 68.2261962890625, \"sum\": 68.2261962890625, \"min\": 68.2261962890625}}, \"EndTime\": 1547633729.262292, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.262277}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=9, validation mse_objective <loss>=37.6180768694\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=mse_objective, value=28.6318620954\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 66 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 23, \"sum\": 23.0, \"min\": 23}, \"Total Records Seen\": {\"count\": 1, \"max\": 2697, \"sum\": 2697.0, \"min\": 2697}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1547633729.263192, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1547633729.186888}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2970.19225466 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5907597351074219, \"sum\": 0.5907597351074219, \"min\": 0.5907597351074219}}, \"EndTime\": 1547633729.288174, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288124}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5649132919311524, \"sum\": 0.5649132919311524, \"min\": 0.5649132919311524}}, \"EndTime\": 1547633729.288236, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288224}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7811775207519531, \"sum\": 0.7811775207519531, \"min\": 0.7811775207519531}}, \"EndTime\": 1547633729.288302, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288285}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5680242156982422, \"sum\": 0.5680242156982422, \"min\": 0.5680242156982422}}, \"EndTime\": 1547633729.288366, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288349}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4782006454467773, \"sum\": 0.4782006454467773, \"min\": 0.4782006454467773}}, \"EndTime\": 1547633729.288493, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288471}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4155936431884766, \"sum\": 0.4155936431884766, \"min\": 0.4155936431884766}}, \"EndTime\": 1547633729.288566, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288549}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4141122055053711, \"sum\": 0.4141122055053711, \"min\": 0.4141122055053711}}, \"EndTime\": 1547633729.288624, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288609}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4620540237426758, \"sum\": 0.4620540237426758, \"min\": 0.4620540237426758}}, \"EndTime\": 1547633729.28869, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288673}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6254343795776367, \"sum\": 0.6254343795776367, \"min\": 0.6254343795776367}}, \"EndTime\": 1547633729.288757, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.28874}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.819919204711914, \"sum\": 0.819919204711914, \"min\": 0.819919204711914}}, \"EndTime\": 1547633729.288822, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.288805}\n",
      "\u001b[0m\n",
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      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=10, validation mse_objective <loss>=36.706451416\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=10, criteria=mse_objective, value=25.5413316999\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] Epoch 10: Loss has not improved for 0 epochs.\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 73 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 25, \"sum\": 25.0, \"min\": 25}, \"Total Records Seen\": {\"count\": 1, \"max\": 2924, \"sum\": 2924.0, \"min\": 2924}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}}, \"EndTime\": 1547633729.340955, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1547633729.263447}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2923.40737648 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5714083862304687, \"sum\": 0.5714083862304687, \"min\": 0.5714083862304687}}, \"EndTime\": 1547633729.367851, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.367803}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5495949935913086, \"sum\": 0.5495949935913086, \"min\": 0.5495949935913086}}, \"EndTime\": 1547633729.367912, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.3679}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7448780822753907, \"sum\": 0.7448780822753907, \"min\": 0.7448780822753907}}, \"EndTime\": 1547633729.367976, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.367959}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5531479263305664, \"sum\": 0.5531479263305664, \"min\": 0.5531479263305664}}, \"EndTime\": 1547633729.368037, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.36802}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.35590198516845706, \"sum\": 0.35590198516845706, \"min\": 0.35590198516845706}}, \"EndTime\": 1547633729.368095, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368079}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.32792560577392577, \"sum\": 0.32792560577392577, \"min\": 0.32792560577392577}}, \"EndTime\": 1547633729.368154, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368138}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3450798034667969, \"sum\": 0.3450798034667969, \"min\": 0.3450798034667969}}, \"EndTime\": 1547633729.368211, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368196}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.37672073364257813, \"sum\": 0.37672073364257813, \"min\": 0.37672073364257813}}, \"EndTime\": 1547633729.368268, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368252}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6105273056030274, \"sum\": 0.6105273056030274, \"min\": 0.6105273056030274}}, \"EndTime\": 1547633729.368326, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368311}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7799676513671875, \"sum\": 0.7799676513671875, \"min\": 0.7799676513671875}}, \"EndTime\": 1547633729.368381, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368365}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5769940185546875, \"sum\": 0.5769940185546875, \"min\": 0.5769940185546875}}, \"EndTime\": 1547633729.368435, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.36842}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6782695770263671, \"sum\": 0.6782695770263671, \"min\": 0.6782695770263671}}, \"EndTime\": 1547633729.36849, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368475}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.36120521545410156, \"sum\": 0.36120521545410156, \"min\": 0.36120521545410156}}, \"EndTime\": 1547633729.368545, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368529}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.39089523315429686, \"sum\": 0.39089523315429686, \"min\": 0.39089523315429686}}, \"EndTime\": 1547633729.368601, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368586}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3605870056152344, \"sum\": 0.3605870056152344, \"min\": 0.3605870056152344}}, \"EndTime\": 1547633729.368655, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368641}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3304927444458008, \"sum\": 0.3304927444458008, \"min\": 0.3304927444458008}}, \"EndTime\": 1547633729.36871, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368696}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6149537277221679, \"sum\": 0.6149537277221679, \"min\": 0.6149537277221679}}, \"EndTime\": 1547633729.368767, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368752}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5528641891479492, \"sum\": 0.5528641891479492, \"min\": 0.5528641891479492}}, \"EndTime\": 1547633729.368824, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368809}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7469762420654297, \"sum\": 0.7469762420654297, \"min\": 0.7469762420654297}}, \"EndTime\": 1547633729.36888, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368865}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.631532096862793, \"sum\": 0.631532096862793, \"min\": 0.631532096862793}}, \"EndTime\": 1547633729.368935, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.36892}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3945431137084961, \"sum\": 0.3945431137084961, \"min\": 0.3945431137084961}}, \"EndTime\": 1547633729.368991, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.368975}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2971279525756836, \"sum\": 0.2971279525756836, \"min\": 0.2971279525756836}}, \"EndTime\": 1547633729.369047, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369032}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3497953414916992, \"sum\": 0.3497953414916992, \"min\": 0.3497953414916992}}, \"EndTime\": 1547633729.369102, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369087}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.34981388092041016, \"sum\": 0.34981388092041016, \"min\": 0.34981388092041016}}, \"EndTime\": 1547633729.369157, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369142}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9993106842041015, \"sum\": 0.9993106842041015, \"min\": 0.9993106842041015}}, \"EndTime\": 1547633729.369214, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369199}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9538143920898438, \"sum\": 0.9538143920898438, \"min\": 0.9538143920898438}}, \"EndTime\": 1547633729.369268, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369253}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9314964294433594, \"sum\": 0.9314964294433594, \"min\": 0.9314964294433594}}, \"EndTime\": 1547633729.369323, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369308}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9458499145507813, \"sum\": 0.9458499145507813, \"min\": 0.9458499145507813}}, \"EndTime\": 1547633729.369377, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369362}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9344828796386718, \"sum\": 0.9344828796386718, \"min\": 0.9344828796386718}}, \"EndTime\": 1547633729.369431, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369416}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9068909454345703, \"sum\": 0.9068909454345703, \"min\": 0.9068909454345703}}, \"EndTime\": 1547633729.369486, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369471}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.887874984741211, \"sum\": 0.887874984741211, \"min\": 0.887874984741211}}, \"EndTime\": 1547633729.36954, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.369525}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9844517517089844, \"sum\": 0.9844517517089844, \"min\": 0.9844517517089844}}, \"EndTime\": 1547633729.369596, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.36958}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=11, train mse_objective <loss>=0.57140838623\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.01091875348772, \"sum\": 36.01091875348772, \"min\": 36.01091875348772}}, \"EndTime\": 1547633729.417671, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.417618}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.45264107840402, \"sum\": 35.45264107840402, \"min\": 35.45264107840402}}, \"EndTime\": 1547633729.417747, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.417727}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 45.274588448660715, \"sum\": 45.274588448660715, \"min\": 45.274588448660715}}, \"EndTime\": 1547633729.417808, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.417795}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.13466099330357, \"sum\": 35.13466099330357, \"min\": 35.13466099330357}}, \"EndTime\": 1547633729.417854, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.41784}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 26.795789446149552, \"sum\": 26.795789446149552, \"min\": 26.795789446149552}}, \"EndTime\": 1547633729.417914, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.417898}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 32.500440325055806, \"sum\": 32.500440325055806, \"min\": 32.500440325055806}}, \"EndTime\": 1547633729.417973, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.417957}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.770006452287944, \"sum\": 34.770006452287944, \"min\": 34.770006452287944}}, \"EndTime\": 1547633729.418031, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418014}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.391226632254465, \"sum\": 33.391226632254465, \"min\": 33.391226632254465}}, \"EndTime\": 1547633729.418088, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418072}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.89099557059152, \"sum\": 39.89099557059152, \"min\": 39.89099557059152}}, \"EndTime\": 1547633729.418146, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418129}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.56855991908482, \"sum\": 48.56855991908482, \"min\": 48.56855991908482}}, \"EndTime\": 1547633729.418202, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418187}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.59409877232143, \"sum\": 35.59409877232143, \"min\": 35.59409877232143}}, \"EndTime\": 1547633729.418258, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418243}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.010140555245535, \"sum\": 41.010140555245535, \"min\": 41.010140555245535}}, \"EndTime\": 1547633729.418314, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418299}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.773241315569194, \"sum\": 33.773241315569194, \"min\": 33.773241315569194}}, \"EndTime\": 1547633729.418372, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418356}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 31.34075927734375, \"sum\": 31.34075927734375, \"min\": 31.34075927734375}}, \"EndTime\": 1547633729.418425, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418412}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 27.126929146902903, \"sum\": 27.126929146902903, \"min\": 27.126929146902903}}, \"EndTime\": 1547633729.418484, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418469}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.399911063058035, \"sum\": 34.399911063058035, \"min\": 34.399911063058035}}, \"EndTime\": 1547633729.418565, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418548}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.27931867327009, \"sum\": 42.27931867327009, \"min\": 42.27931867327009}}, \"EndTime\": 1547633729.418622, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418606}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.89760480608259, \"sum\": 34.89760480608259, \"min\": 34.89760480608259}}, \"EndTime\": 1547633729.418676, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418661}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 50.73006330217634, \"sum\": 50.73006330217634, \"min\": 50.73006330217634}}, \"EndTime\": 1547633729.418726, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418712}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.664171491350444, \"sum\": 43.664171491350444, \"min\": 43.664171491350444}}, \"EndTime\": 1547633729.41878, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418765}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 32.574977329799104, \"sum\": 32.574977329799104, \"min\": 32.574977329799104}}, \"EndTime\": 1547633729.418839, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418824}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 30.928179059709823, \"sum\": 30.928179059709823, \"min\": 30.928179059709823}}, \"EndTime\": 1547633729.418894, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418879}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.568485804966517, \"sum\": 28.568485804966517, \"min\": 28.568485804966517}}, \"EndTime\": 1547633729.418946, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418931}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 28.72863987513951, \"sum\": 28.72863987513951, \"min\": 28.72863987513951}}, \"EndTime\": 1547633729.419008, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.418992}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.59682791573661, \"sum\": 64.59682791573661, \"min\": 64.59682791573661}}, \"EndTime\": 1547633729.419056, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.419042}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.49109322684152, \"sum\": 62.49109322684152, \"min\": 62.49109322684152}}, \"EndTime\": 1547633729.419113, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.419098}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 59.43180193219866, \"sum\": 59.43180193219866, \"min\": 59.43180193219866}}, \"EndTime\": 1547633729.419172, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.419157}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.020516531808035, \"sum\": 61.020516531808035, \"min\": 61.020516531808035}}, \"EndTime\": 1547633729.419225, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.419211}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 61.01863316127232, \"sum\": 61.01863316127232, \"min\": 61.01863316127232}}, \"EndTime\": 1547633729.41928, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.419265}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 56.04392787388393, \"sum\": 56.04392787388393, \"min\": 56.04392787388393}}, \"EndTime\": 1547633729.419335, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.41932}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.885999407087056, \"sum\": 60.885999407087056, \"min\": 60.885999407087056}}, \"EndTime\": 1547633729.419395, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.41938}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 65.99786376953125, \"sum\": 65.99786376953125, \"min\": 65.99786376953125}}, \"EndTime\": 1547633729.41944, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.419426}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=11, validation mse_objective <loss>=36.0109187535\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=11, criteria=mse_objective, value=26.7957894461\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 27, \"sum\": 27.0, \"min\": 27}, \"Total Records Seen\": {\"count\": 1, \"max\": 3151, \"sum\": 3151.0, \"min\": 3151}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}}, \"EndTime\": 1547633729.420299, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1547633729.341216}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2865.54808885 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5547348022460937, \"sum\": 0.5547348022460937, \"min\": 0.5547348022460937}}, \"EndTime\": 1547633729.445577, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445527}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5362078475952149, \"sum\": 0.5362078475952149, \"min\": 0.5362078475952149}}, \"EndTime\": 1547633729.445647, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445629}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7121248626708985, \"sum\": 0.7121248626708985, \"min\": 0.7121248626708985}}, \"EndTime\": 1547633729.445711, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445694}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5403203582763672, \"sum\": 0.5403203582763672, \"min\": 0.5403203582763672}}, \"EndTime\": 1547633729.445767, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445751}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.2998818778991699, \"sum\": 0.2998818778991699, \"min\": 0.2998818778991699}}, \"EndTime\": 1547633729.445826, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.44581}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3391565322875977, \"sum\": 0.3391565322875977, \"min\": 0.3391565322875977}}, \"EndTime\": 1547633729.445887, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445872}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3703395080566406, \"sum\": 0.3703395080566406, \"min\": 0.3703395080566406}}, \"EndTime\": 1547633729.445945, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445929}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.35861930847167967, \"sum\": 0.35861930847167967, \"min\": 0.35861930847167967}}, \"EndTime\": 1547633729.446, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.445985}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5975364303588867, \"sum\": 0.5975364303588867, \"min\": 0.5975364303588867}}, \"EndTime\": 1547633729.446057, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446041}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7437854766845703, \"sum\": 0.7437854766845703, \"min\": 0.7437854766845703}}, \"EndTime\": 1547633729.446113, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5641904449462891, \"sum\": 0.5641904449462891, \"min\": 0.5641904449462891}}, \"EndTime\": 1547633729.446162, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446148}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6565008544921875, \"sum\": 0.6565008544921875, \"min\": 0.6565008544921875}}, \"EndTime\": 1547633729.446217, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446201}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3486512756347656, \"sum\": 0.3486512756347656, \"min\": 0.3486512756347656}}, \"EndTime\": 1547633729.446271, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446255}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.34443668365478514, \"sum\": 0.34443668365478514, \"min\": 0.34443668365478514}}, \"EndTime\": 1547633729.446327, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446312}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.29807697296142577, \"sum\": 0.29807697296142577, \"min\": 0.29807697296142577}}, \"EndTime\": 1547633729.446382, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446367}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3603301239013672, \"sum\": 0.3603301239013672, \"min\": 0.3603301239013672}}, \"EndTime\": 1547633729.446446, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446429}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5962346649169922, \"sum\": 0.5962346649169922, \"min\": 0.5962346649169922}}, \"EndTime\": 1547633729.446524, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446485}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5377841567993165, \"sum\": 0.5377841567993165, \"min\": 0.5377841567993165}}, \"EndTime\": 1547633729.446585, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446569}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7236689758300782, \"sum\": 0.7236689758300782, \"min\": 0.7236689758300782}}, \"EndTime\": 1547633729.446645, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446629}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6126941680908203, \"sum\": 0.6126941680908203, \"min\": 0.6126941680908203}}, \"EndTime\": 1547633729.446701, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446685}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.45294673919677736, \"sum\": 0.45294673919677736, \"min\": 0.45294673919677736}}, \"EndTime\": 1547633729.446754, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.44674}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.33278472900390627, \"sum\": 0.33278472900390627, \"min\": 0.33278472900390627}}, \"EndTime\": 1547633729.446808, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446794}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.38370574951171876, \"sum\": 0.38370574951171876, \"min\": 0.38370574951171876}}, \"EndTime\": 1547633729.446863, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446848}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3604613494873047, \"sum\": 0.3604613494873047, \"min\": 0.3604613494873047}}, \"EndTime\": 1547633729.446927, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.44691}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9996576690673828, \"sum\": 0.9996576690673828, \"min\": 0.9996576690673828}}, \"EndTime\": 1547633729.446981, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.446966}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9535243225097656, \"sum\": 0.9535243225097656, \"min\": 0.9535243225097656}}, \"EndTime\": 1547633729.447036, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.447021}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9232281494140625, \"sum\": 0.9232281494140625, \"min\": 0.9232281494140625}}, \"EndTime\": 1547633729.447093, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.447078}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9395637512207031, \"sum\": 0.9395637512207031, \"min\": 0.9395637512207031}}, \"EndTime\": 1547633729.447147, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.447132}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9380694580078125, \"sum\": 0.9380694580078125, \"min\": 0.9380694580078125}}, \"EndTime\": 1547633729.447202, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.447186}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.8553954315185547, \"sum\": 0.8553954315185547, \"min\": 0.8553954315185547}}, \"EndTime\": 1547633729.447258, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.447243}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9282711029052735, \"sum\": 0.9282711029052735, \"min\": 0.9282711029052735}}, \"EndTime\": 1547633729.447311, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.447296}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0030338287353515, \"sum\": 1.0030338287353515, \"min\": 1.0030338287353515}}, \"EndTime\": 1547633729.447365, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.44735}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=12, train mse_objective <loss>=0.554734802246\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.4565669468471, \"sum\": 35.4565669468471, \"min\": 35.4565669468471}}, \"EndTime\": 1547633729.496967, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.49687}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.12144252232143, \"sum\": 35.12144252232143, \"min\": 35.12144252232143}}, \"EndTime\": 1547633729.497092, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497069}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 43.643484933035715, \"sum\": 43.643484933035715, \"min\": 43.643484933035715}}, \"EndTime\": 1547633729.49716, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497142}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.89355032784598, \"sum\": 34.89355032784598, \"min\": 34.89355032784598}}, \"EndTime\": 1547633729.497224, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497207}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 30.17358616420201, \"sum\": 30.17358616420201, \"min\": 30.17358616420201}}, \"EndTime\": 1547633729.497287, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497271}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.37745884486607, \"sum\": 39.37745884486607, \"min\": 39.37745884486607}}, \"EndTime\": 1547633729.497347, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.49733}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.58254132952009, \"sum\": 40.58254132952009, \"min\": 40.58254132952009}}, \"EndTime\": 1547633729.497403, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497388}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.58391898018973, \"sum\": 37.58391898018973, \"min\": 37.58391898018973}}, \"EndTime\": 1547633729.49746, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497444}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.57377406529018, \"sum\": 39.57377406529018, \"min\": 39.57377406529018}}, \"EndTime\": 1547633729.497517, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497501}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 46.635646275111604, \"sum\": 46.635646275111604, \"min\": 46.635646275111604}}, \"EndTime\": 1547633729.497576, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.49756}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 35.27890232631138, \"sum\": 35.27890232631138, \"min\": 35.27890232631138}}, \"EndTime\": 1547633729.497631, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497616}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.062670026506694, \"sum\": 40.062670026506694, \"min\": 40.062670026506694}}, \"EndTime\": 1547633729.497687, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497672}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.28632027762277, \"sum\": 36.28632027762277, \"min\": 36.28632027762277}}, \"EndTime\": 1547633729.497744, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497729}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.44395010811942, \"sum\": 33.44395010811942, \"min\": 33.44395010811942}}, \"EndTime\": 1547633729.4978, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497784}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 30.49499729701451, \"sum\": 30.49499729701451, \"min\": 30.49499729701451}}, \"EndTime\": 1547633729.497858, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497843}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.354457310267854, \"sum\": 39.354457310267854, \"min\": 39.354457310267854}}, \"EndTime\": 1547633729.497913, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497898}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.51200212751116, \"sum\": 41.51200212751116, \"min\": 41.51200212751116}}, \"EndTime\": 1547633729.497966, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.497953}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.40818568638393, \"sum\": 34.40818568638393, \"min\": 34.40818568638393}}, \"EndTime\": 1547633729.49802, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498004}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 49.66261945452009, \"sum\": 49.66261945452009, \"min\": 49.66261945452009}}, \"EndTime\": 1547633729.498072, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498058}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.865753173828125, \"sum\": 42.865753173828125, \"min\": 42.865753173828125}}, \"EndTime\": 1547633729.498126, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498111}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.49878365652902, \"sum\": 40.49878365652902, \"min\": 40.49878365652902}}, \"EndTime\": 1547633729.498182, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498167}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.91453334263393, \"sum\": 37.91453334263393, \"min\": 37.91453334263393}}, \"EndTime\": 1547633729.49824, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498224}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 38.06450980050223, \"sum\": 38.06450980050223, \"min\": 38.06450980050223}}, \"EndTime\": 1547633729.498294, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498278}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.924002511160715, \"sum\": 36.924002511160715, \"min\": 36.924002511160715}}, \"EndTime\": 1547633729.498346, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498331}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.62893676757812, \"sum\": 64.62893676757812, \"min\": 64.62893676757812}}, \"EndTime\": 1547633729.4984, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498385}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.521144321986604, \"sum\": 62.521144321986604, \"min\": 62.521144321986604}}, \"EndTime\": 1547633729.498448, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498436}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 59.112901960100444, \"sum\": 59.112901960100444, \"min\": 59.112901960100444}}, \"EndTime\": 1547633729.498492, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498481}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.84564644949777, \"sum\": 60.84564644949777, \"min\": 60.84564644949777}}, \"EndTime\": 1547633729.498565, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498552}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 68.99474661690849, \"sum\": 68.99474661690849, \"min\": 68.99474661690849}}, \"EndTime\": 1547633729.498612, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.4986}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 63.048496791294646, \"sum\": 63.048496791294646, \"min\": 63.048496791294646}}, \"EndTime\": 1547633729.498655, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498644}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 72.58373151506696, \"sum\": 72.58373151506696, \"min\": 72.58373151506696}}, \"EndTime\": 1547633729.498699, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498686}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 73.7137451171875, \"sum\": 73.7137451171875, \"min\": 73.7137451171875}}, \"EndTime\": 1547633729.498749, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.498736}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=12, validation mse_objective <loss>=35.4565669468\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=12, criteria=mse_objective, value=30.1735861642\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 86 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 29, \"sum\": 29.0, \"min\": 29}, \"Total Records Seen\": {\"count\": 1, \"max\": 3378, \"sum\": 3378.0, \"min\": 3378}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1547633729.499792, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1547633729.420567}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2861.46593976 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5403468704223633, \"sum\": 0.5403468704223633, \"min\": 0.5403468704223633}}, \"EndTime\": 1547633729.538557, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538451}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5244259643554687, \"sum\": 0.5244259643554687, \"min\": 0.5244259643554687}}, \"EndTime\": 1547633729.538653, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538634}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6828791809082031, \"sum\": 0.6828791809082031, \"min\": 0.6828791809082031}}, \"EndTime\": 1547633729.538719, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538702}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5291980743408203, \"sum\": 0.5291980743408203, \"min\": 0.5291980743408203}}, \"EndTime\": 1547633729.538776, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538761}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.33955070495605466, \"sum\": 0.33955070495605466, \"min\": 0.33955070495605466}}, \"EndTime\": 1547633729.538834, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538818}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.40137126922607425, \"sum\": 0.40137126922607425, \"min\": 0.40137126922607425}}, \"EndTime\": 1547633729.53889, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538875}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.42389396667480467, \"sum\": 0.42389396667480467, \"min\": 0.42389396667480467}}, \"EndTime\": 1547633729.538945, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538931}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3969279861450195, \"sum\": 0.3969279861450195, \"min\": 0.3969279861450195}}, \"EndTime\": 1547633729.539002, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.538986}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5861715698242187, \"sum\": 0.5861715698242187, \"min\": 0.5861715698242187}}, \"EndTime\": 1547633729.539093, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539076}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.7111500549316406, \"sum\": 0.7111500549316406, \"min\": 0.7111500549316406}}, \"EndTime\": 1547633729.539152, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539136}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5527803039550782, \"sum\": 0.5527803039550782, \"min\": 0.5527803039550782}}, \"EndTime\": 1547633729.539233, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539204}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6370354461669921, \"sum\": 0.6370354461669921, \"min\": 0.6370354461669921}}, \"EndTime\": 1547633729.539294, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539279}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3724619674682617, \"sum\": 0.3724619674682617, \"min\": 0.3724619674682617}}, \"EndTime\": 1547633729.53935, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539335}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.36151226043701173, \"sum\": 0.36151226043701173, \"min\": 0.36151226043701173}}, \"EndTime\": 1547633729.539406, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539391}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.3410277557373047, \"sum\": 0.3410277557373047, \"min\": 0.3410277557373047}}, \"EndTime\": 1547633729.539461, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539446}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4061970520019531, \"sum\": 0.4061970520019531, \"min\": 0.4061970520019531}}, \"EndTime\": 1547633729.539518, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539502}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5801386260986328, \"sum\": 0.5801386260986328, \"min\": 0.5801386260986328}}, \"EndTime\": 1547633729.539573, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539558}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5240359115600586, \"sum\": 0.5240359115600586, \"min\": 0.5240359115600586}}, \"EndTime\": 1547633729.539628, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539613}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.702890625, \"sum\": 0.702890625, \"min\": 0.702890625}}, \"EndTime\": 1547633729.539683, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539667}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5959354782104492, \"sum\": 0.5959354782104492, \"min\": 0.5959354782104492}}, \"EndTime\": 1547633729.539739, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539724}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5229639053344727, \"sum\": 0.5229639053344727, \"min\": 0.5229639053344727}}, \"EndTime\": 1547633729.539826, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539811}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.40322227478027345, \"sum\": 0.40322227478027345, \"min\": 0.40322227478027345}}, \"EndTime\": 1547633729.539881, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539866}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5025334930419922, \"sum\": 0.5025334930419922, \"min\": 0.5025334930419922}}, \"EndTime\": 1547633729.539936, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539921}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.4720193481445312, \"sum\": 0.4720193481445312, \"min\": 0.4720193481445312}}, \"EndTime\": 1547633729.539991, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.539976}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.998243408203125, \"sum\": 0.998243408203125, \"min\": 0.998243408203125}}, \"EndTime\": 1547633729.540046, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540031}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9539783477783204, \"sum\": 0.9539783477783204, \"min\": 0.9539783477783204}}, \"EndTime\": 1547633729.540102, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540087}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9165166473388672, \"sum\": 0.9165166473388672, \"min\": 0.9165166473388672}}, \"EndTime\": 1547633729.540157, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540141}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9356632995605468, \"sum\": 0.9356632995605468, \"min\": 0.9356632995605468}}, \"EndTime\": 1547633729.54021, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540196}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.028734893798828, \"sum\": 1.028734893798828, \"min\": 1.028734893798828}}, \"EndTime\": 1547633729.540266, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540251}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.9158275604248047, \"sum\": 0.9158275604248047, \"min\": 0.9158275604248047}}, \"EndTime\": 1547633729.540322, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540306}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0556949615478515, \"sum\": 1.0556949615478515, \"min\": 1.0556949615478515}}, \"EndTime\": 1547633729.540376, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540361}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 1.0743702697753905, \"sum\": 1.0743702697753905, \"min\": 1.0743702697753905}}, \"EndTime\": 1547633729.540432, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.540416}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=13, train mse_objective <loss>=0.540346870422\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.967577253069194, \"sum\": 34.967577253069194, \"min\": 34.967577253069194}}, \"EndTime\": 1547633729.588556, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588476}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.78041512625558, \"sum\": 34.78041512625558, \"min\": 34.78041512625558}}, \"EndTime\": 1547633729.588643, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588623}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.20218767438616, \"sum\": 42.20218767438616, \"min\": 42.20218767438616}}, \"EndTime\": 1547633729.588709, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588692}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.65531049455915, \"sum\": 34.65531049455915, \"min\": 34.65531049455915}}, \"EndTime\": 1547633729.588764, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.58875}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.010506766183035, \"sum\": 33.010506766183035, \"min\": 33.010506766183035}}, \"EndTime\": 1547633729.588821, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588806}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.942413330078125, \"sum\": 39.942413330078125, \"min\": 39.942413330078125}}, \"EndTime\": 1547633729.588876, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588861}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.038848876953125, \"sum\": 39.038848876953125, \"min\": 39.038848876953125}}, \"EndTime\": 1547633729.588931, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588916}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 38.602735246930806, \"sum\": 38.602735246930806, \"min\": 38.602735246930806}}, \"EndTime\": 1547633729.588986, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.588971}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.25171334402902, \"sum\": 39.25171334402902, \"min\": 39.25171334402902}}, \"EndTime\": 1547633729.589044, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589028}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 44.88582066127232, \"sum\": 44.88582066127232, \"min\": 44.88582066127232}}, \"EndTime\": 1547633729.589097, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589083}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.949087960379465, \"sum\": 34.949087960379465, \"min\": 34.949087960379465}}, \"EndTime\": 1547633729.589153, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589138}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 39.190909249441965, \"sum\": 39.190909249441965, \"min\": 39.190909249441965}}, \"EndTime\": 1547633729.58921, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589194}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.587716238839285, \"sum\": 34.587716238839285, \"min\": 34.587716238839285}}, \"EndTime\": 1547633729.589264, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589249}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.675096784319194, \"sum\": 34.675096784319194, \"min\": 34.675096784319194}}, \"EndTime\": 1547633729.589319, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589304}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 34.280552455357146, \"sum\": 34.280552455357146, \"min\": 34.280552455357146}}, \"EndTime\": 1547633729.589375, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.58936}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 36.49741690499442, \"sum\": 36.49741690499442, \"min\": 36.49741690499442}}, \"EndTime\": 1547633729.589429, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589415}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.825391496930806, \"sum\": 40.825391496930806, \"min\": 40.825391496930806}}, \"EndTime\": 1547633729.589485, \"Dimensions\": {\"model\": 16, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589469}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 33.92321341378348, \"sum\": 33.92321341378348, \"min\": 33.92321341378348}}, \"EndTime\": 1547633729.58954, \"Dimensions\": {\"model\": 17, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589525}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 48.651842389787944, \"sum\": 48.651842389787944, \"min\": 48.651842389787944}}, \"EndTime\": 1547633729.589596, \"Dimensions\": {\"model\": 18, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589581}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 42.078626360212056, \"sum\": 42.078626360212056, \"min\": 42.078626360212056}}, \"EndTime\": 1547633729.589648, \"Dimensions\": {\"model\": 19, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589634}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 40.742344447544646, \"sum\": 40.742344447544646, \"min\": 40.742344447544646}}, \"EndTime\": 1547633729.589706, \"Dimensions\": {\"model\": 20, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.58969}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 37.93152291434152, \"sum\": 37.93152291434152, \"min\": 37.93152291434152}}, \"EndTime\": 1547633729.589761, \"Dimensions\": {\"model\": 21, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589746}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.91664777483259, \"sum\": 41.91664777483259, \"min\": 41.91664777483259}}, \"EndTime\": 1547633729.589814, \"Dimensions\": {\"model\": 22, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.5898}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 41.33886282784598, \"sum\": 41.33886282784598, \"min\": 41.33886282784598}}, \"EndTime\": 1547633729.589869, \"Dimensions\": {\"model\": 23, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589854}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 64.51139177594867, \"sum\": 64.51139177594867, \"min\": 64.51139177594867}}, \"EndTime\": 1547633729.589922, \"Dimensions\": {\"model\": 24, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589908}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 62.51611328125, \"sum\": 62.51611328125, \"min\": 62.51611328125}}, \"EndTime\": 1547633729.589978, \"Dimensions\": {\"model\": 25, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.589963}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 58.84470912388393, \"sum\": 58.84470912388393, \"min\": 58.84470912388393}}, \"EndTime\": 1547633729.590031, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.590016}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.72454833984375, \"sum\": 60.72454833984375, \"min\": 60.72454833984375}}, \"EndTime\": 1547633729.590086, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.59007}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 73.59784807477679, \"sum\": 73.59784807477679, \"min\": 73.59784807477679}}, \"EndTime\": 1547633729.590142, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.590126}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 67.28815133231026, \"sum\": 67.28815133231026, \"min\": 67.28815133231026}}, \"EndTime\": 1547633729.590197, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.590182}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 79.09933907645089, \"sum\": 79.09933907645089, \"min\": 79.09933907645089}}, \"EndTime\": 1547633729.590252, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.590237}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 75.96597726004464, \"sum\": 75.96597726004464, \"min\": 75.96597726004464}}, \"EndTime\": 1547633729.590306, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.590291}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=13, validation mse_objective <loss>=34.9675772531\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=13, criteria=mse_objective, value=33.0105067662\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 93 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 31, \"sum\": 31.0, \"min\": 31}, \"Total Records Seen\": {\"count\": 1, \"max\": 3605, \"sum\": 3605.0, \"min\": 3605}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}}, \"EndTime\": 1547633729.591308, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1547633729.499995}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2482.50320838 records/second\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.527684211730957, \"sum\": 0.527684211730957, \"min\": 0.527684211730957}}, \"EndTime\": 1547633729.617306, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.617246}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5137072372436523, \"sum\": 0.5137072372436523, \"min\": 0.5137072372436523}}, \"EndTime\": 1547633729.617384, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.617365}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.6567520141601563, \"sum\": 0.6567520141601563, \"min\": 0.6567520141601563}}, \"EndTime\": 1547633729.617452, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.617435}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.5192033004760742, \"sum\": 0.5192033004760742, \"min\": 0.5192033004760742}}, \"EndTime\": 1547633729.61751, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.617494}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_mse_objective\": {\"count\": 1, \"max\": 0.37526527404785154, \"sum\": 0.37526527404785154, \"min\": 0.37526527404785154}}, \"EndTime\": 1547633729.617567, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.617551}\n",
      "\u001b[0m\n",
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      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 58.63487025669643, \"sum\": 58.63487025669643, \"min\": 58.63487025669643}}, \"EndTime\": 1547633729.66697, \"Dimensions\": {\"model\": 26, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.666954}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 60.637974330357146, \"sum\": 60.637974330357146, \"min\": 60.637974330357146}}, \"EndTime\": 1547633729.667026, \"Dimensions\": {\"model\": 27, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.66701}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 70.20861380440849, \"sum\": 70.20861380440849, \"min\": 70.20861380440849}}, \"EndTime\": 1547633729.66708, \"Dimensions\": {\"model\": 28, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.667065}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 66.42393711635044, \"sum\": 66.42393711635044, \"min\": 66.42393711635044}}, \"EndTime\": 1547633729.66714, \"Dimensions\": {\"model\": 29, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.667123}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 75.23707798549107, \"sum\": 75.23707798549107, \"min\": 75.23707798549107}}, \"EndTime\": 1547633729.667195, \"Dimensions\": {\"model\": 30, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.66718}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"validation_mse_objective\": {\"count\": 1, \"max\": 69.57865687779018, \"sum\": 69.57865687779018, \"min\": 69.57865687779018}}, \"EndTime\": 1547633729.66725, \"Dimensions\": {\"model\": 31, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.667235}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, epoch=14, validation mse_objective <loss>=34.5174865723\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=14, criteria=mse_objective, value=27.6343361991\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] Early stop condition met. Stopping training.\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #progress_metric: host=algo-1, completed 100 % epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Total Batches Seen\": {\"count\": 1, \"max\": 33, \"sum\": 33.0, \"min\": 33}, \"Total Records Seen\": {\"count\": 1, \"max\": 3832, \"sum\": 3832.0, \"min\": 3832}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 227, \"sum\": 227.0, \"min\": 227}, \"Reset Count\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}}, \"EndTime\": 1547633729.668251, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1547633729.591588}\n",
      "\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #throughput_metric: host=algo-1, train throughput=2955.72501187 records/second\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 WARNING 139926660151104] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 WARNING 139926660151104] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #early_stopping_criteria_metric: host=algo-1, epoch=14, criteria=mse_objective, value=27.6343361991\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #validation_score (algo-1) : ('mse_objective', 20.510977608816965)\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] #quality_metric: host=algo-1, validation mse_objective <loss>=20.5109776088\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] Best model found for hyperparameters: {\"lr_scheduler_step\": 10, \"wd\": 1, \"optimizer\": \"adam\", \"lr_scheduler_factor\": 0.99, \"l1\": 0.0, \"learning_rate\": 0.1, \"lr_scheduler_minimum_lr\": 1e-05}\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] Saved checkpoint to \"/tmp/tmpZTnnn8/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[01/16/2019 10:15:29 INFO 139926660151104] Test data is not provided.\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 1600.5840301513672, \"sum\": 1600.5840301513672, \"min\": 1600.5840301513672}, \"finalize.time\": {\"count\": 1, \"max\": 52.73294448852539, \"sum\": 52.73294448852539, \"min\": 52.73294448852539}, \"initialize.time\": {\"count\": 1, \"max\": 154.8919677734375, \"sum\": 154.8919677734375, \"min\": 154.8919677734375}, \"check_early_stopping.time\": {\"count\": 16, \"max\": 1.2979507446289062, \"sum\": 6.120920181274414, \"min\": 0.17404556274414062}, \"setuptime\": {\"count\": 1, \"max\": 16.831159591674805, \"sum\": 16.831159591674805, \"min\": 16.831159591674805}, \"update.time\": {\"count\": 15, \"max\": 102.80203819274902, \"sum\": 1291.417121887207, \"min\": 76.12800598144531}, \"epochs\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1547633729.729353, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1547633728.214139}\n",
      "\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2019-01-16 10:15:38 Completed - Training job completed\n",
      "Billable seconds: 67\n"
     ]
    }
   ],
   "source": [
    "linear.fit({'train': s3_input_train, 'validation': s3_input_validation})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 (B): Deploy the trained model\n",
    "\n",
    "Similar to the XGBoost model, now that we've fit the model we need to deploy it. Also like the XGBoost model, we will use the lower level approach so that we have more control over the endpoint that gets created.\n",
    "\n",
    "### Build the model\n",
    "\n",
    "Of course, before we can deploy the model, we need to first create it. The `fit` method that we used earlier created some model artifacts and we can use these to construct a model object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we create a unique model name\n",
    "linear_model_name = \"boston-update-linear-model\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# We also need to tell SageMaker which container should be used for inference and where it should\n",
    "# retrieve the model artifacts from. In our case, the linear-learner container that we used for training\n",
    "# can also be used for inference.\n",
    "linear_primary_container = {\n",
    "    \"Image\": linear_container,\n",
    "    \"ModelDataUrl\": linear.model_data\n",
    "}\n",
    "\n",
    "# And lastly we construct the SageMaker model\n",
    "linear_model_info = session.sagemaker_client.create_model(\n",
    "                                ModelName = linear_model_name,\n",
    "                                ExecutionRoleArn = role,\n",
    "                                PrimaryContainer = linear_primary_container)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the endpoint configuration\n",
    "\n",
    "Once we have the model we can start putting together the endpoint by creating an endpoint configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As before, we need to give our endpoint configuration a name which should be unique\n",
    "linear_endpoint_config_name = \"boston-linear-endpoint-config-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# And then we ask SageMaker to construct the endpoint configuration\n",
    "linear_endpoint_config_info = session.sagemaker_client.create_endpoint_config(\n",
    "                            EndpointConfigName = linear_endpoint_config_name,\n",
    "                            ProductionVariants = [{\n",
    "                                \"InstanceType\": \"ml.m4.xlarge\",\n",
    "                                \"InitialVariantWeight\": 1,\n",
    "                                \"InitialInstanceCount\": 1,\n",
    "                                \"ModelName\": linear_model_name,\n",
    "                                \"VariantName\": \"Linear-Model\"\n",
    "                            }])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy the endpoint\n",
    "\n",
    "Now that the endpoint configuration has been created, we can ask SageMaker to build our endpoint.\n",
    "\n",
    "**Note:** This is a friendly (repeated) reminder that you are about to deploy an endpoint. Make sure that you shut it down once you've finished with it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again, we need a unique name for our endpoint\n",
    "endpoint_name = \"boston-update-endpoint-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# And then we can deploy our endpoint\n",
    "endpoint_info = session.sagemaker_client.create_endpoint(\n",
    "                    EndpointName = endpoint_name,\n",
    "                    EndpointConfigName = linear_endpoint_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "endpoint_dec = session.wait_for_endpoint(endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 (B): Use the model\n",
    "\n",
    "Just like with the XGBoost model, we will send some data to our endpoint to make sure that it is working properly. An important note is that the output format for the linear model is different from the XGBoost model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = session.sagemaker_runtime_client.invoke_endpoint(\n",
    "                                                EndpointName = endpoint_name,\n",
    "                                                ContentType = 'text/csv',\n",
    "                                                Body = ','.join(map(str, X_test.values[0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4fa8f60>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '47',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:22:38 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': '21b03c20-e658-444f-ad16-0a8cf18ce9bb'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '21b03c20-e658-444f-ad16-0a8cf18ce9bb',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "pprint(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = response['Body'].read().decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'{\"predictions\": [{\"score\": 20.38943862915039}]}'\n"
     ]
    }
   ],
   "source": [
    "pprint(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.2])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.values[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shut down the endpoint\n",
    "\n",
    "Now that we know that the Linear model's endpoint works, we can shut it down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': 'cd4526fe-2c23-4244-b329-5694cd0e7c2b',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': 'cd4526fe-2c23-4244-b329-5694cd0e7c2b',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '0',\n",
       "   'date': 'Wed, 16 Jan 2019 10:22:38 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sagemaker_client.delete_endpoint(EndpointName = endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6 (C): Deploy a combined model\n",
    "\n",
    "So far we've constructed two separate models which we could deploy and use. Before we talk about how we can change a deployed endpoint from one configuration to another, let's consider a slightly different situation. Suppose that before we switch from using only the XGBoost model to only the Linear model, we first want to do something like an A-B test, where we send some of the incoming data to the XGBoost model and some of the data to the Linear model.\n",
    "\n",
    "Fortunately, SageMaker provides this functionality. And to actually get SageMaker to do this for us is not too different from deploying a model in the way that we've already done. The only difference is that we need to list more than one model in the production variants parameter of the endpoint configuration.\n",
    "\n",
    "A reasonable question to ask is, how much data is sent to each of the models that I list in the production variants parameter? The answer is that it depends on the weight set for each model.\n",
    "\n",
    "Suppose that we have $k$ models listed in the production variants and that each model $i$ is assigned the weight $w_i$. Then each model $i$ will receive $w_i / W$ of the traffic where $W = \\sum_{i} w_i$.\n",
    "\n",
    "In our case, since we have two models, the linear model and the XGBoost model, and each model has weight 1, we see that each model will get 1 / (1 + 1) = 1/2 of the data sent to the endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As before, we need to give our endpoint configuration a name which should be unique\n",
    "combined_endpoint_config_name = \"boston-combined-endpoint-config-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# And then we ask SageMaker to construct the endpoint configuration\n",
    "combined_endpoint_config_info = session.sagemaker_client.create_endpoint_config(\n",
    "                            EndpointConfigName = combined_endpoint_config_name,\n",
    "                            ProductionVariants = [\n",
    "                                { # First we include the linear model\n",
    "                                    \"InstanceType\": \"ml.m4.xlarge\",\n",
    "                                    \"InitialVariantWeight\": 1,\n",
    "                                    \"InitialInstanceCount\": 1,\n",
    "                                    \"ModelName\": linear_model_name,\n",
    "                                    \"VariantName\": \"Linear-Model\"\n",
    "                                }, { # And next we include the xgb model\n",
    "                                    \"InstanceType\": \"ml.m4.xlarge\",\n",
    "                                    \"InitialVariantWeight\": 1,\n",
    "                                    \"InitialInstanceCount\": 1,\n",
    "                                    \"ModelName\": xgb_model_name,\n",
    "                                    \"VariantName\": \"XGB-Model\"\n",
    "                                }])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've created the endpoint configuration, we can ask SageMaker to construct the endpoint.\n",
    "\n",
    "**Note:** This is a friendly (repeated) reminder that you are about to deploy an endpoint. Make sure that you shut it down once you've finished with it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again, we need a unique name for our endpoint\n",
    "endpoint_name = \"boston-update-endpoint-\" + strftime(\"%Y-%m-%d-%H-%M-%S\", gmtime())\n",
    "\n",
    "# And then we can deploy our endpoint\n",
    "endpoint_info = session.sagemaker_client.create_endpoint(\n",
    "                    EndpointName = endpoint_name,\n",
    "                    EndpointConfigName = combined_endpoint_config_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "endpoint_dec = session.wait_for_endpoint(endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7 (C): Use the model\n",
    "\n",
    "Now that we've constructed an endpoint which sends data to both the XGBoost model and the linear model we can send some data to the endpoint and see what sort of results we get back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4f45da0>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '47',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': 'c2846882-7b46-44f1-a3ce-38f6e6887d99'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'c2846882-7b46-44f1-a3ce-38f6e6887d99',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "response = session.sagemaker_runtime_client.invoke_endpoint(\n",
    "                                                EndpointName = endpoint_name,\n",
    "                                                ContentType = 'text/csv',\n",
    "                                                Body = ','.join(map(str, X_test.values[0])))\n",
    "pprint(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since looking at a single response doesn't give us a clear look at what is happening, we can instead take a look at a few different responses to our endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f45c8978>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '47',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': 'ddc4c762-34d5-4372-bcc9-0fc7237569b3'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'ddc4c762-34d5-4372-bcc9-0fc7237569b3',\n",
      "                      'RetryAttempts': 0}}\n",
      "{\"predictions\": [{\"score\": 20.38943862915039}]}\n",
      "[18.2]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f45c8550>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '47',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': '923ffe5e-9c8a-4b36-b6c7-d0410dbbcb4b'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '923ffe5e-9c8a-4b36-b6c7-d0410dbbcb4b',\n",
      "                      'RetryAttempts': 0}}\n",
      "{\"predictions\": [{\"score\": 25.63663101196289}]}\n",
      "[25.]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f45c84e0>,\n",
      " 'ContentType': 'text/csv; charset=utf-8',\n",
      " 'InvokedProductionVariant': 'XGB-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '13',\n",
      "                                      'content-type': 'text/csv; charset=utf-8',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'XGB-Model',\n",
      "                                      'x-amzn-requestid': '7133c5f6-2435-4e91-92dd-4a4c9dc174cc'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '7133c5f6-2435-4e91-92dd-4a4c9dc174cc',\n",
      "                      'RetryAttempts': 0}}\n",
      "37.0259666443\n",
      "[33.2]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f45c80b8>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '48',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': '3b8c8cda-c4f4-476e-899c-0c494c34c816'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '3b8c8cda-c4f4-476e-899c-0c494c34c816',\n",
      "                      'RetryAttempts': 0}}\n",
      "{\"predictions\": [{\"score\": 21.727432250976562}]}\n",
      "[23.8]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f45c85f8>,\n",
      " 'ContentType': 'text/csv; charset=utf-8',\n",
      " 'InvokedProductionVariant': 'XGB-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '13',\n",
      "                                      'content-type': 'text/csv; charset=utf-8',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'XGB-Model',\n",
      "                                      'x-amzn-requestid': 'cf7af13a-fbf8-4402-b25d-5db5979620c7'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'cf7af13a-fbf8-4402-b25d-5db5979620c7',\n",
      "                      'RetryAttempts': 0}}\n",
      "11.0866117477\n",
      "[13.9]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4fba240>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '48',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': '130e68fb-a2b6-4b5e-8676-ec28c766d6e2'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '130e68fb-a2b6-4b5e-8676-ec28c766d6e2',\n",
      "                      'RetryAttempts': 0}}\n",
      "{\"predictions\": [{\"score\": 14.808506965637207}]}\n",
      "[13.3]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4f7aa20>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'Linear-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '46',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'Linear-Model',\n",
      "                                      'x-amzn-requestid': '338d801c-bd3f-4733-b0b1-e78040255b8a'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '338d801c-bd3f-4733-b0b1-e78040255b8a',\n",
      "                      'RetryAttempts': 0}}\n",
      "{\"predictions\": [{\"score\": 8.20130729675293}]}\n",
      "[5.]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4f7acc0>,\n",
      " 'ContentType': 'text/csv; charset=utf-8',\n",
      " 'InvokedProductionVariant': 'XGB-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '13',\n",
      "                                      'content-type': 'text/csv; charset=utf-8',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'XGB-Model',\n",
      "                                      'x-amzn-requestid': 'ec1719fd-ed2e-4c04-9857-12071e5529f9'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'ec1719fd-ed2e-4c04-9857-12071e5529f9',\n",
      "                      'RetryAttempts': 0}}\n",
      "24.4507904053\n",
      "[28.1]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4f7a9b0>,\n",
      " 'ContentType': 'text/csv; charset=utf-8',\n",
      " 'InvokedProductionVariant': 'XGB-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '13',\n",
      "                                      'content-type': 'text/csv; charset=utf-8',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'XGB-Model',\n",
      "                                      'x-amzn-requestid': '64ba44ee-56b8-471c-9c4f-bdf481c672e8'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '64ba44ee-56b8-471c-9c4f-bdf481c672e8',\n",
      "                      'RetryAttempts': 0}}\n",
      "12.0927648544\n",
      "[6.3]\n",
      "{'Body': <botocore.response.StreamingBody object at 0x7f68f4fa8eb8>,\n",
      " 'ContentType': 'text/csv; charset=utf-8',\n",
      " 'InvokedProductionVariant': 'XGB-Model',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '13',\n",
      "                                      'content-type': 'text/csv; charset=utf-8',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'XGB-Model',\n",
      "                                      'x-amzn-requestid': 'f584ca92-da5b-4988-a25e-953b8ebd857e'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'f584ca92-da5b-4988-a25e-953b8ebd857e',\n",
      "                      'RetryAttempts': 0}}\n",
      "24.2065410614\n",
      "[25.]\n"
     ]
    }
   ],
   "source": [
    "for rec in range(10):\n",
    "    response = session.sagemaker_runtime_client.invoke_endpoint(\n",
    "                                                EndpointName = endpoint_name,\n",
    "                                                ContentType = 'text/csv',\n",
    "                                                Body = ','.join(map(str, X_test.values[rec])))\n",
    "    pprint(response)\n",
    "    result = response['Body'].read().decode(\"utf-8\")\n",
    "    print(result)\n",
    "    print(Y_test.values[rec])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If at some point we aren't sure about the properties of a deployed endpoint, we can use the `describe_endpoint` function to get SageMaker to return a description of the deployed endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CreationTime': datetime.datetime(2019, 1, 16, 10, 22, 46, 168000, tzinfo=tzlocal()),\n",
      " 'EndpointArn': 'arn:aws:sagemaker:eu-west-1:177473201433:endpoint/boston-update-endpoint-2019-01-16-10-22-46',\n",
      " 'EndpointConfigName': 'boston-combined-endpoint-config-2019-01-16-10-22-45',\n",
      " 'EndpointName': 'boston-update-endpoint-2019-01-16-10-22-46',\n",
      " 'EndpointStatus': 'InService',\n",
      " 'LastModifiedTime': datetime.datetime(2019, 1, 16, 10, 29, 5, 480000, tzinfo=tzlocal()),\n",
      " 'ProductionVariants': [{'CurrentInstanceCount': 1,\n",
      "                         'CurrentWeight': 1.0,\n",
      "                         'DeployedImages': [{'ResolutionTime': datetime.datetime(2019, 1, 16, 10, 22, 50, 654000, tzinfo=tzlocal()),\n",
      "                                             'ResolvedImage': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner@sha256:f02018ed0f268981463ada8374ec20641120891cd22fc334f5c94af4a1673ba4',\n",
      "                                             'SpecifiedImage': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:1'}],\n",
      "                         'DesiredInstanceCount': 1,\n",
      "                         'DesiredWeight': 1.0,\n",
      "                         'VariantName': 'Linear-Model'},\n",
      "                        {'CurrentInstanceCount': 1,\n",
      "                         'CurrentWeight': 1.0,\n",
      "                         'DeployedImages': [{'ResolutionTime': datetime.datetime(2019, 1, 16, 10, 22, 50, 753000, tzinfo=tzlocal()),\n",
      "                                             'ResolvedImage': '685385470294.dkr.ecr.eu-west-1.amazonaws.com/xgboost@sha256:9372ea9af627ace4c2ef71158162de5a0864bf41304d981df62049bfe55fd316',\n",
      "                                             'SpecifiedImage': '685385470294.dkr.ecr.eu-west-1.amazonaws.com/xgboost:1'}],\n",
      "                         'DesiredInstanceCount': 1,\n",
      "                         'DesiredWeight': 1.0,\n",
      "                         'VariantName': 'XGB-Model'}],\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '1161',\n",
      "                                      'content-type': 'application/x-amz-json-1.1',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-requestid': '0c88a6e9-ae06-445f-b36f-273d9990cac0'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '0c88a6e9-ae06-445f-b36f-273d9990cac0',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "pprint(session.sagemaker_client.describe_endpoint(EndpointName=endpoint_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Updating an Endpoint\n",
    "\n",
    "Now suppose that we've done our A-B test and the new linear model is working well enough. What we'd like to do now is to switch our endpoint from sending data to both the XGBoost model and the linear model to sending data only to the linear model.\n",
    "\n",
    "Of course, we don't really want to shut down the endpoint to do this as doing so would interrupt service to whoever depends on our endpoint. Instead, we can ask SageMaker to **update** an endpoint to a new endpoint configuration.\n",
    "\n",
    "What is actually happening is that SageMaker will set up a new endpoint with the new characteristics. Once this new endpoint is running, SageMaker will switch the old endpoint so that it now points at the newly deployed model, making sure that this happens seamlessly in the background."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'EndpointArn': 'arn:aws:sagemaker:eu-west-1:177473201433:endpoint/boston-update-endpoint-2019-01-16-10-22-46',\n",
       " 'ResponseMetadata': {'RequestId': '6e81dba1-b295-429b-b1c3-5bdcfda1c9c9',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '6e81dba1-b295-429b-b1c3-5bdcfda1c9c9',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '110',\n",
       "   'date': 'Wed, 16 Jan 2019 10:29:07 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sagemaker_client.update_endpoint(EndpointName=endpoint_name, EndpointConfigName=linear_endpoint_config_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a glimpse at what is going on, we can ask SageMaker to describe our in-use endpoint now, before the update process has completed. When we do so, we can see that the in-use endpoint still has the same characteristics it had before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CreationTime': datetime.datetime(2019, 1, 16, 10, 22, 46, 168000, tzinfo=tzlocal()),\n",
      " 'EndpointArn': 'arn:aws:sagemaker:eu-west-1:177473201433:endpoint/boston-update-endpoint-2019-01-16-10-22-46',\n",
      " 'EndpointConfigName': 'boston-combined-endpoint-config-2019-01-16-10-22-45',\n",
      " 'EndpointName': 'boston-update-endpoint-2019-01-16-10-22-46',\n",
      " 'EndpointStatus': 'Updating',\n",
      " 'LastModifiedTime': datetime.datetime(2019, 1, 16, 10, 29, 7, 774000, tzinfo=tzlocal()),\n",
      " 'ProductionVariants': [{'CurrentInstanceCount': 1,\n",
      "                         'CurrentWeight': 1.0,\n",
      "                         'DeployedImages': [{'ResolutionTime': datetime.datetime(2019, 1, 16, 10, 22, 50, 654000, tzinfo=tzlocal()),\n",
      "                                             'ResolvedImage': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner@sha256:f02018ed0f268981463ada8374ec20641120891cd22fc334f5c94af4a1673ba4',\n",
      "                                             'SpecifiedImage': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:1'}],\n",
      "                         'DesiredInstanceCount': 1,\n",
      "                         'DesiredWeight': 1.0,\n",
      "                         'VariantName': 'Linear-Model'},\n",
      "                        {'CurrentInstanceCount': 1,\n",
      "                         'CurrentWeight': 1.0,\n",
      "                         'DeployedImages': [{'ResolutionTime': datetime.datetime(2019, 1, 16, 10, 22, 50, 753000, tzinfo=tzlocal()),\n",
      "                                             'ResolvedImage': '685385470294.dkr.ecr.eu-west-1.amazonaws.com/xgboost@sha256:9372ea9af627ace4c2ef71158162de5a0864bf41304d981df62049bfe55fd316',\n",
      "                                             'SpecifiedImage': '685385470294.dkr.ecr.eu-west-1.amazonaws.com/xgboost:1'}],\n",
      "                         'DesiredInstanceCount': 1,\n",
      "                         'DesiredWeight': 1.0,\n",
      "                         'VariantName': 'XGB-Model'}],\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '1161',\n",
      "                                      'content-type': 'application/x-amz-json-1.1',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:29:07 GMT',\n",
      "                                      'x-amzn-requestid': 'ccd3ea70-4187-4c18-9842-7b97611e2dfd'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'ccd3ea70-4187-4c18-9842-7b97611e2dfd',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "pprint(session.sagemaker_client.describe_endpoint(EndpointName=endpoint_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we now wait for the update process to complete, and then ask SageMaker to describe the endpoint, it will return the characteristics of the new endpoint configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "endpoint_dec = session.wait_for_endpoint(endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'CreationTime': datetime.datetime(2019, 1, 16, 10, 22, 46, 168000, tzinfo=tzlocal()),\n",
      " 'EndpointArn': 'arn:aws:sagemaker:eu-west-1:177473201433:endpoint/boston-update-endpoint-2019-01-16-10-22-46',\n",
      " 'EndpointConfigName': 'boston-linear-endpoint-config-2019-01-16-10-16-18',\n",
      " 'EndpointName': 'boston-update-endpoint-2019-01-16-10-22-46',\n",
      " 'EndpointStatus': 'InService',\n",
      " 'LastModifiedTime': datetime.datetime(2019, 1, 16, 10, 35, 23, 160000, tzinfo=tzlocal()),\n",
      " 'ProductionVariants': [{'CurrentInstanceCount': 1,\n",
      "                         'CurrentWeight': 1.0,\n",
      "                         'DeployedImages': [{'ResolutionTime': datetime.datetime(2019, 1, 16, 10, 29, 10, 186000, tzinfo=tzlocal()),\n",
      "                                             'ResolvedImage': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner@sha256:f02018ed0f268981463ada8374ec20641120891cd22fc334f5c94af4a1673ba4',\n",
      "                                             'SpecifiedImage': '438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:1'}],\n",
      "                         'DesiredInstanceCount': 1,\n",
      "                         'DesiredWeight': 1.0,\n",
      "                         'VariantName': 'Linear-Model'}],\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'content-length': '769',\n",
      "                                      'content-type': 'application/x-amz-json-1.1',\n",
      "                                      'date': 'Wed, 16 Jan 2019 10:35:23 GMT',\n",
      "                                      'x-amzn-requestid': '5ab7c23a-c511-4829-94d0-ce5bf58a3d2f'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': '5ab7c23a-c511-4829-94d0-ce5bf58a3d2f',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "pprint(session.sagemaker_client.describe_endpoint(EndpointName=endpoint_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shut down the endpoint\n",
    "\n",
    "Now that we've finished, we need to make sure to shut down the endpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': 'b8815705-08ab-4c58-9851-0c970844a6c0',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': 'b8815705-08ab-4c58-9851-0c970844a6c0',\n",
       "   'content-type': 'application/x-amz-json-1.1',\n",
       "   'content-length': '0',\n",
       "   'date': 'Wed, 16 Jan 2019 10:35:23 GMT'},\n",
       "  'RetryAttempts': 0}}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "session.sagemaker_client.delete_endpoint(EndpointName = endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional: Clean up\n",
    "\n",
    "The default notebook instance on SageMaker doesn't have a lot of excess disk space available. As you continue to complete and execute notebooks you will eventually fill up this disk space, leading to errors which can be difficult to diagnose. Once you are completely finished using a notebook it is a good idea to remove the files that you created along the way. Of course, you can do this from the terminal or from the notebook hub if you would like. The cell below contains some commands to clean up the created files from within the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we will remove all of the files contained in the data_dir directory\n",
    "!rm $data_dir/*\n",
    "\n",
    "# And then we delete the directory itself\n",
    "!rmdir $data_dir"
   ]
  }
 ],
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   "codemirror_mode": {
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