test-iris-linear-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First SageMaker Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define IAM role\n",
    "import boto3\n",
    "from sagemaker import get_execution_role"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'arn:aws:iam::553243691765:role/service-role/AmazonSageMaker-ExecutionRole-20190304T180164'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket = 'sagemaker-test-mdebbich'\n",
    "# If you don't have any specific bucket you can automatically create one using :\n",
    "# bucket = Session().default_bucket()\n",
    "\n",
    "prefix = 'test-iris/linear-model'\n",
    "role = get_execution_role()\n",
    "role"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Iris Data :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = datasets.load_iris()\n",
    "iris_data = pd.DataFrame(iris[\"data\"], columns= iris[\"feature_names\"])\n",
    "iris_data[\"target\"] = iris[\"target\"]\n",
    "iris_data[\"species\"] = iris[\"target\"]\n",
    "iris_data[\"species\"] = iris_data[\"species\"].astype(\"category\").cat.rename_categories(iris[\"target_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   target species  \n",
       "0       0  setosa  \n",
       "1       0  setosa  \n",
       "2       0  setosa  \n",
       "3       0  setosa  \n",
       "4       0  setosa  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Sepal width')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training points\n",
    "plt.scatter(iris_data.iloc[:, 0], iris_data.iloc[:, 1], c=iris_data[\"target\"], cmap=plt.cm.Set1, edgecolor='k')\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import LinearLearner\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(iris_data, train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.reset_index(drop=True)\n",
    "test = test.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.8               2.6                4.0               1.2   \n",
       "1                4.9               2.5                4.5               1.7   \n",
       "2                5.5               3.5                1.3               0.2   \n",
       "3                4.8               3.0                1.4               0.3   \n",
       "4                4.9               3.0                1.4               0.2   \n",
       "\n",
       "   target     species  \n",
       "0       1  versicolor  \n",
       "1       2   virginica  \n",
       "2       0      setosa  \n",
       "3       0      setosa  \n",
       "4       0      setosa  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Training job:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training data will be uploaded to: s3://sagemaker-test-mdebbich/test-iris/linear-model/train_data\n",
      "training artifacts will be uploaded to: s3://sagemaker-test-mdebbich/test-iris/linear-model/output\n"
     ]
    }
   ],
   "source": [
    "data_location = 's3://{}/{}/train_data'.format(bucket, prefix)\n",
    "output_location = 's3://{}/{}/output'.format(bucket, prefix)\n",
    "\n",
    "print('training data will be uploaded to: {}'.format(data_location))\n",
    "print('training artifacts will be uploaded to: {}'.format(output_location))\n",
    "\n",
    "linear = LinearLearner(\n",
    "    role=role,\n",
    "    train_instance_count=1,\n",
    "    train_instance_type='ml.m5.large',\n",
    "    output_path=output_location,\n",
    "    data_location=data_location,\n",
    "    predictor_type=\"multiclass_classifier\",\n",
    "    num_classes=3\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_input = np.array(train)\n",
    "\n",
    "vectors = train_input[:, :4].astype(\"float32\")\n",
    "labels = train_input[:, 4].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the training job:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "recorded_train_set = linear.record_set(vectors, labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: linear-learner-2019-03-07-17-09-02-166\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-03-07 17:09:02 Starting - Starting the training job...\n",
      "2019-03-07 17:09:03 Starting - Launching requested ML instances......\n",
      "2019-03-07 17:10:07 Starting - Preparing the instances for training......\n",
      "2019-03-07 17:11:29 Downloading - Downloading input data...\n",
      "2019-03-07 17:11:56 Training - Training image download completed. Training in progress.\n",
      "2019-03-07 17:11:56 Uploading - Uploading generated training model\n",
      "\u001b[31mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] 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[03/07/2019 17:11:51 INFO 140277105186624] Reading provided configuration from /opt/ml/input/config/hyperparameters.json: {u'feature_dim': u'4', u'mini_batch_size': u'120', u'predictor_type': u'multiclass_classifier', u'num_classes': u'3'}\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Final configuration: {u'loss_insensitivity': u'0.01', u'epochs': u'15', u'feature_dim': u'4', 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'120', u'huber_delta': u'1.0', u'num_classes': u'3', u'predictor_type': u'multiclass_classifier', 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[03/07/2019 17:11:51 WARNING 140277105186624] Loggers have already been setup.\u001b[0m\n",
      "\u001b[31mProcess 1 is a worker.\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Using default worker.\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.652] [tensorio] [info] batch={\"data_pipeline\": \"/opt/ml/input/data/train\", \"num_examples\": 120, \"features\": [{\"name\": \"label_values\", \"shape\": [1], \"storage_type\": \"dense\"}, {\"name\": \"values\", \"shape\": [4], \"storage_type\": \"dense\"}]}\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.684] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 0, \"duration\": 31, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Create Store: local\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.702] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 1, \"duration\": 17, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Scaler algorithm parameters\n",
      " <algorithm.scaler.ScalerAlgorithmStable object at 0x7f94815d3a50>\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Scaling model computed with parameters:\n",
      " {'stdev_weight': \u001b[0m\n",
      "\u001b[31m[ 0.82904059  0.43430659  1.78836024  0.78443009]\u001b[0m\n",
      "\u001b[31m<NDArray 4 @cpu(0)>, 'stdev_label': None, 'mean_label': None, 'mean_weight': \u001b[0m\n",
      "\u001b[31m[ 5.80499983  3.07333326  3.70416665  1.19833338]\u001b[0m\n",
      "\u001b[31m<NDArray 4 @cpu(0)>}\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] nvidia-smi took: 0.0251631736755 secs to identify 0 gpus\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Number of GPUs being used: 0\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 0, \"sum\": 0.0, \"min\": 0}, \"Total Batches Seen\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}, \"Total Records Seen\": {\"count\": 1, \"max\": 240, \"sum\": 240.0, \"min\": 240}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 2, \"sum\": 2.0, \"min\": 2}}, \"EndTime\": 1551978711.833567, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"init_train_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1551978711.833516}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0991263071695963, \"sum\": 1.0991263071695963, \"min\": 1.0991263071695963}}, \"EndTime\": 1551978711.877097, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877031}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.129562505086263, \"sum\": 1.129562505086263, \"min\": 1.129562505086263}}, \"EndTime\": 1551978711.877188, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877174}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0943311055501301, \"sum\": 1.0943311055501301, \"min\": 1.0943311055501301}}, \"EndTime\": 1551978711.877374, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877355}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0658355712890626, \"sum\": 1.0658355712890626, \"min\": 1.0658355712890626}}, \"EndTime\": 1551978711.877425, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877412}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1028786977132161, \"sum\": 1.1028786977132161, \"min\": 1.1028786977132161}}, \"EndTime\": 1551978711.87747, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877459}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1085497538248699, \"sum\": 1.1085497538248699, \"min\": 1.1085497538248699}}, \"EndTime\": 1551978711.877513, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877503}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1479015350341797, \"sum\": 1.1479015350341797, \"min\": 1.1479015350341797}}, \"EndTime\": 1551978711.877554, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877544}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.089081064860026, \"sum\": 1.089081064860026, \"min\": 1.089081064860026}}, \"EndTime\": 1551978711.877597, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877586}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0700941721598307, \"sum\": 1.0700941721598307, \"min\": 1.0700941721598307}}, \"EndTime\": 1551978711.877636, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877626}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0767133076985678, \"sum\": 1.0767133076985678, \"min\": 1.0767133076985678}}, \"EndTime\": 1551978711.877677, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877667}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1109848022460938, \"sum\": 1.1109848022460938, \"min\": 1.1109848022460938}}, \"EndTime\": 1551978711.877716, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877706}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0212421417236328, \"sum\": 1.0212421417236328, \"min\": 1.0212421417236328}}, \"EndTime\": 1551978711.877757, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877747}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1029935201009116, \"sum\": 1.1029935201009116, \"min\": 1.1029935201009116}}, \"EndTime\": 1551978711.877795, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877786}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0630151748657226, \"sum\": 1.0630151748657226, \"min\": 1.0630151748657226}}, \"EndTime\": 1551978711.877854, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877826}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1023078918457032, \"sum\": 1.1023078918457032, \"min\": 1.1023078918457032}}, \"EndTime\": 1551978711.8779, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.877887}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1308619181315105, \"sum\": 1.1308619181315105, \"min\": 1.1308619181315105}}, \"EndTime\": 1551978711.87794, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.87793}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #quality_metric: host=algo-1, epoch=0, train multiclass_cross_entropy_objective <loss>=1.09912630717\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=0, criteria=multiclass_cross_entropy_objective, value=1.02124214172\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Epoch 0: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #progress_metric: host=algo-1, completed 6 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}, \"Total Records Seen\": {\"count\": 1, \"max\": 360, \"sum\": 360.0, \"min\": 360}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 3, \"sum\": 3.0, \"min\": 3}}, \"EndTime\": 1551978711.882427, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 0}, \"StartTime\": 1551978711.833767}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=2460.18271134 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.885] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 2, \"duration\": 51, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0533242543538412, \"sum\": 1.0533242543538412, \"min\": 1.0533242543538412}}, \"EndTime\": 1551978711.906483, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.906428}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9155138015747071, \"sum\": 0.9155138015747071, \"min\": 0.9155138015747071}}, \"EndTime\": 1551978711.90665, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.906548}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0569833755493163, \"sum\": 1.0569833755493163, \"min\": 1.0569833755493163}}, \"EndTime\": 1551978711.906787, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.906704}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.981155268351237, \"sum\": 0.981155268351237, \"min\": 0.981155268351237}}, \"EndTime\": 1551978711.906917, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.906838}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0529654184977213, \"sum\": 1.0529654184977213, \"min\": 1.0529654184977213}}, \"EndTime\": 1551978711.907042, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.906965}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0647138595581054, \"sum\": 1.0647138595581054, \"min\": 1.0647138595581054}}, \"EndTime\": 1551978711.907165, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.90709}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0368561426798502, \"sum\": 1.0368561426798502, \"min\": 1.0368561426798502}}, \"EndTime\": 1551978711.907288, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.907212}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0222090403238933, \"sum\": 1.0222090403238933, \"min\": 1.0222090403238933}}, \"EndTime\": 1551978711.907408, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.907334}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1303297678629558, \"sum\": 1.1303297678629558, \"min\": 1.1303297678629558}}, \"EndTime\": 1551978711.907561, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.907454}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7672908782958985, \"sum\": 0.7672908782958985, \"min\": 0.7672908782958985}}, \"EndTime\": 1551978711.907699, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.907612}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9986252466837565, \"sum\": 0.9986252466837565, \"min\": 0.9986252466837565}}, \"EndTime\": 1551978711.907825, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.907747}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8586914698282878, \"sum\": 0.8586914698282878, \"min\": 0.8586914698282878}}, \"EndTime\": 1551978711.907955, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.907872}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0525946299235025, \"sum\": 1.0525946299235025, \"min\": 1.0525946299235025}}, \"EndTime\": 1551978711.908085, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.908004}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.010486857096354, \"sum\": 1.010486857096354, \"min\": 1.010486857096354}}, \"EndTime\": 1551978711.908207, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.908131}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6089460372924804, \"sum\": 0.6089460372924804, \"min\": 0.6089460372924804}}, \"EndTime\": 1551978711.90833, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.908255}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.120211664835612, \"sum\": 1.120211664835612, \"min\": 1.120211664835612}}, \"EndTime\": 1551978711.908453, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.908376}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #quality_metric: host=algo-1, epoch=1, train multiclass_cross_entropy_objective <loss>=1.05332425435\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=1, criteria=multiclass_cross_entropy_objective, value=0.608946037292\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Epoch 1: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #progress_metric: host=algo-1, completed 13 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}, \"Total Records Seen\": {\"count\": 1, \"max\": 480, \"sum\": 480.0, \"min\": 480}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 4, \"sum\": 4.0, \"min\": 4}}, \"EndTime\": 1551978711.911356, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 1}, \"StartTime\": 1551978711.88575}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=4658.91422065 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.911] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 3, \"duration\": 25, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9887774785359701, \"sum\": 0.9887774785359701, \"min\": 0.9887774785359701}}, \"EndTime\": 1551978711.944487, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944379}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.810200309753418, \"sum\": 0.810200309753418, \"min\": 0.810200309753418}}, \"EndTime\": 1551978711.944692, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944671}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8564261754353841, \"sum\": 0.8564261754353841, \"min\": 0.8564261754353841}}, \"EndTime\": 1551978711.944738, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944727}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9070738474527995, \"sum\": 0.9070738474527995, \"min\": 0.9070738474527995}}, \"EndTime\": 1551978711.944778, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944766}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9720340092976888, \"sum\": 0.9720340092976888, \"min\": 0.9720340092976888}}, \"EndTime\": 1551978711.944816, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944806}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0232247670491537, \"sum\": 1.0232247670491537, \"min\": 1.0232247670491537}}, \"EndTime\": 1551978711.944851, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944842}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9408403396606445, \"sum\": 0.9408403396606445, \"min\": 0.9408403396606445}}, \"EndTime\": 1551978711.944888, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944879}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8881240844726562, \"sum\": 0.8881240844726562, \"min\": 0.8881240844726562}}, \"EndTime\": 1551978711.944924, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944914}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7140886942545573, \"sum\": 0.7140886942545573, \"min\": 0.7140886942545573}}, \"EndTime\": 1551978711.944964, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944954}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5996683756510417, \"sum\": 0.5996683756510417, \"min\": 0.5996683756510417}}, \"EndTime\": 1551978711.945003, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.944993}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9320740381876628, \"sum\": 0.9320740381876628, \"min\": 0.9320740381876628}}, \"EndTime\": 1551978711.94504, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.94503}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7395117441813152, \"sum\": 0.7395117441813152, \"min\": 0.7395117441813152}}, \"EndTime\": 1551978711.945115, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.945103}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9776299158732097, \"sum\": 0.9776299158732097, \"min\": 0.9776299158732097}}, \"EndTime\": 1551978711.945175, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.945162}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9267814636230469, \"sum\": 0.9267814636230469, \"min\": 0.9267814636230469}}, \"EndTime\": 1551978711.945217, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.945206}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.48241984049479164, \"sum\": 0.48241984049479164, \"min\": 0.48241984049479164}}, \"EndTime\": 1551978711.945256, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.945246}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.1096837361653646, \"sum\": 1.1096837361653646, \"min\": 1.1096837361653646}}, \"EndTime\": 1551978711.945297, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.945288}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #quality_metric: host=algo-1, epoch=2, train multiclass_cross_entropy_objective <loss>=0.988777478536\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=2, criteria=multiclass_cross_entropy_objective, value=0.482419840495\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Epoch 2: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #progress_metric: host=algo-1, completed 20 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}, \"Total Records Seen\": {\"count\": 1, \"max\": 600, \"sum\": 600.0, \"min\": 600}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 5, \"sum\": 5.0, \"min\": 5}}, \"EndTime\": 1551978711.949477, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 2}, \"StartTime\": 1551978711.911871}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=3161.81372734 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.949] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 4, \"duration\": 38, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9399804433186849, \"sum\": 0.9399804433186849, \"min\": 0.9399804433186849}}, \"EndTime\": 1551978711.979715, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.97966}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7421524047851562, \"sum\": 0.7421524047851562, \"min\": 0.7421524047851562}}, \"EndTime\": 1551978711.97983, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.979815}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7692263285319011, \"sum\": 0.7692263285319011, \"min\": 0.7692263285319011}}, \"EndTime\": 1551978711.979874, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.979863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8428349177042643, \"sum\": 0.8428349177042643, \"min\": 0.8428349177042643}}, \"EndTime\": 1551978711.979911, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.979901}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9159940083821615, \"sum\": 0.9159940083821615, \"min\": 0.9159940083821615}}, \"EndTime\": 1551978711.979997, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.979984}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9842267990112304, \"sum\": 0.9842267990112304, \"min\": 0.9842267990112304}}, \"EndTime\": 1551978711.980034, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980024}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8595736185709636, \"sum\": 0.8595736185709636, \"min\": 0.8595736185709636}}, \"EndTime\": 1551978711.980071, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980061}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8341687520345052, \"sum\": 0.8341687520345052, \"min\": 0.8341687520345052}}, \"EndTime\": 1551978711.980106, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980097}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6316084543863932, \"sum\": 0.6316084543863932, \"min\": 0.6316084543863932}}, \"EndTime\": 1551978711.980189, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980134}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5014050165812175, \"sum\": 0.5014050165812175, \"min\": 0.5014050165812175}}, \"EndTime\": 1551978711.980233, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980222}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.883549690246582, \"sum\": 0.883549690246582, \"min\": 0.883549690246582}}, \"EndTime\": 1551978711.980269, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980259}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.647892951965332, \"sum\": 0.647892951965332, \"min\": 0.647892951965332}}, \"EndTime\": 1551978711.980305, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980296}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9239062627156576, \"sum\": 0.9239062627156576, \"min\": 0.9239062627156576}}, \"EndTime\": 1551978711.980341, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980332}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.869739023844401, \"sum\": 0.869739023844401, \"min\": 0.869739023844401}}, \"EndTime\": 1551978711.980425, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980411}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4278705596923828, \"sum\": 0.4278705596923828, \"min\": 0.4278705596923828}}, \"EndTime\": 1551978711.980467, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980457}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0992799123128256, \"sum\": 1.0992799123128256, \"min\": 1.0992799123128256}}, \"EndTime\": 1551978711.980503, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.980494}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #quality_metric: host=algo-1, epoch=3, train multiclass_cross_entropy_objective <loss>=0.939980443319\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=3, criteria=multiclass_cross_entropy_objective, value=0.427870559692\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] Epoch 3: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #progress_metric: host=algo-1, completed 26 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}, \"Total Records Seen\": {\"count\": 1, \"max\": 720, \"sum\": 720.0, \"min\": 720}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 6, \"sum\": 6.0, \"min\": 6}}, \"EndTime\": 1551978711.984088, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 3}, \"StartTime\": 1551978711.950034}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:51 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=3507.11419871 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:51.984] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 5, \"duration\": 34, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8983671188354492, \"sum\": 0.8983671188354492, \"min\": 0.8983671188354492}}, \"EndTime\": 1551978712.005583, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005543}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6923073450724284, \"sum\": 0.6923073450724284, \"min\": 0.6923073450724284}}, \"EndTime\": 1551978712.005638, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005625}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7207681655883789, \"sum\": 0.7207681655883789, \"min\": 0.7207681655883789}}, \"EndTime\": 1551978712.005682, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005672}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7863758087158204, \"sum\": 0.7863758087158204, \"min\": 0.7863758087158204}}, \"EndTime\": 1551978712.005724, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005713}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8702343622843425, \"sum\": 0.8702343622843425, \"min\": 0.8702343622843425}}, \"EndTime\": 1551978712.005763, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005753}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9476902643839519, \"sum\": 0.9476902643839519, \"min\": 0.9476902643839519}}, \"EndTime\": 1551978712.005799, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.00579}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7909313837687174, \"sum\": 0.7909313837687174, \"min\": 0.7909313837687174}}, \"EndTime\": 1551978712.005897, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005827}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7906784057617188, \"sum\": 0.7906784057617188, \"min\": 0.7906784057617188}}, \"EndTime\": 1551978712.005937, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005927}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5960397084554037, \"sum\": 0.5960397084554037, \"min\": 0.5960397084554037}}, \"EndTime\": 1551978712.005973, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.005964}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4452917734781901, \"sum\": 0.4452917734781901, \"min\": 0.4452917734781901}}, \"EndTime\": 1551978712.00601, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.006}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8450523376464844, \"sum\": 0.8450523376464844, \"min\": 0.8450523376464844}}, \"EndTime\": 1551978712.006049, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.006039}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5769404729207357, \"sum\": 0.5769404729207357, \"min\": 0.5769404729207357}}, \"EndTime\": 1551978712.006085, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.006076}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8784384409586589, \"sum\": 0.8784384409586589, \"min\": 0.8784384409586589}}, \"EndTime\": 1551978712.006124, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.006114}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8230558395385742, \"sum\": 0.8230558395385742, \"min\": 0.8230558395385742}}, \"EndTime\": 1551978712.006162, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.006153}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3960882186889648, \"sum\": 0.3960882186889648, \"min\": 0.3960882186889648}}, \"EndTime\": 1551978712.006199, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.00619}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0890040079752603, \"sum\": 1.0890040079752603, \"min\": 1.0890040079752603}}, \"EndTime\": 1551978712.006235, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978712.006226}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=4, train multiclass_cross_entropy_objective <loss>=0.898367118835\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=4, criteria=multiclass_cross_entropy_objective, value=0.396088218689\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 4: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 33 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}, \"Total Records Seen\": {\"count\": 1, \"max\": 840, \"sum\": 840.0, \"min\": 840}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 7, \"sum\": 7.0, \"min\": 7}}, \"EndTime\": 1551978712.007955, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 4}, \"StartTime\": 1551978711.984379}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=5061.15297596 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.008] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 6, \"duration\": 23, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8552950541178386, \"sum\": 0.8552950541178386, \"min\": 0.8552950541178386}}, \"EndTime\": 1551978712.019972, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.019943}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6532505671183269, \"sum\": 0.6532505671183269, \"min\": 0.6532505671183269}}, \"EndTime\": 1551978712.020022, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020011}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6599225362141927, \"sum\": 0.6599225362141927, \"min\": 0.6599225362141927}}, \"EndTime\": 1551978712.020065, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020054}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7364533742268881, \"sum\": 0.7364533742268881, \"min\": 0.7364533742268881}}, \"EndTime\": 1551978712.020106, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020095}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8215782165527343, \"sum\": 0.8215782165527343, \"min\": 0.8215782165527343}}, \"EndTime\": 1551978712.020145, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020135}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9135064442952474, \"sum\": 0.9135064442952474, \"min\": 0.9135064442952474}}, \"EndTime\": 1551978712.020184, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020173}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7327850341796875, \"sum\": 0.7327850341796875, \"min\": 0.7327850341796875}}, \"EndTime\": 1551978712.02022, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.02021}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7535924275716146, \"sum\": 0.7535924275716146, \"min\": 0.7535924275716146}}, \"EndTime\": 1551978712.020257, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020248}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4753353436787923, \"sum\": 0.4753353436787923, \"min\": 0.4753353436787923}}, \"EndTime\": 1551978712.020296, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020287}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4157819112141927, \"sum\": 0.4157819112141927, \"min\": 0.4157819112141927}}, \"EndTime\": 1551978712.020333, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020323}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8129917780558268, \"sum\": 0.8129917780558268, \"min\": 0.8129917780558268}}, \"EndTime\": 1551978712.020368, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020359}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5219026883443196, \"sum\": 0.5219026883443196, \"min\": 0.5219026883443196}}, \"EndTime\": 1551978712.020405, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020396}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8313897450764974, \"sum\": 0.8313897450764974, \"min\": 0.8313897450764974}}, \"EndTime\": 1551978712.020441, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020432}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7712608973185221, \"sum\": 0.7712608973185221, \"min\": 0.7712608973185221}}, \"EndTime\": 1551978712.020479, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020469}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.36915566126505533, \"sum\": 0.36915566126505533, \"min\": 0.36915566126505533}}, \"EndTime\": 1551978712.020517, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020508}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0788593292236328, \"sum\": 1.0788593292236328, \"min\": 1.0788593292236328}}, \"EndTime\": 1551978712.020555, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.020546}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=5, train multiclass_cross_entropy_objective <loss>=0.855295054118\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=5, criteria=multiclass_cross_entropy_objective, value=0.369155661265\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 5: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 40 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}, \"Total Records Seen\": {\"count\": 1, \"max\": 960, \"sum\": 960.0, \"min\": 960}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 8, \"sum\": 8.0, \"min\": 8}}, \"EndTime\": 1551978712.022428, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 5}, \"StartTime\": 1551978712.008222}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=8379.25116953 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.022] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 7, \"duration\": 14, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.813670539855957, \"sum\": 0.813670539855957, \"min\": 0.813670539855957}}, \"EndTime\": 1551978712.035292, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035263}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6213181177775066, \"sum\": 0.6213181177775066, \"min\": 0.6213181177775066}}, \"EndTime\": 1551978712.035343, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035331}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5950245539347331, \"sum\": 0.5950245539347331, \"min\": 0.5950245539347331}}, \"EndTime\": 1551978712.035404, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035391}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6922738393147786, \"sum\": 0.6922738393147786, \"min\": 0.6922738393147786}}, \"EndTime\": 1551978712.035518, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035491}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7744428634643554, \"sum\": 0.7744428634643554, \"min\": 0.7744428634643554}}, \"EndTime\": 1551978712.035564, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035552}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8815326690673828, \"sum\": 0.8815326690673828, \"min\": 0.8815326690673828}}, \"EndTime\": 1551978712.035668, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035652}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6830983479817708, \"sum\": 0.6830983479817708, \"min\": 0.6830983479817708}}, \"EndTime\": 1551978712.035777, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035763}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7228040059407552, \"sum\": 0.7228040059407552, \"min\": 0.7228040059407552}}, \"EndTime\": 1551978712.03583, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035805}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4238746960957845, \"sum\": 0.4238746960957845, \"min\": 0.4238746960957845}}, \"EndTime\": 1551978712.035883, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.035871}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4032552719116211, \"sum\": 0.4032552719116211, \"min\": 0.4032552719116211}}, \"EndTime\": 1551978712.036004, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.03599}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.785427983601888, \"sum\": 0.785427983601888, \"min\": 0.785427983601888}}, \"EndTime\": 1551978712.036108, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.036047}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4786747296651204, \"sum\": 0.4786747296651204, \"min\": 0.4786747296651204}}, \"EndTime\": 1551978712.036193, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.036154}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7869524637858073, \"sum\": 0.7869524637858073, \"min\": 0.7869524637858073}}, \"EndTime\": 1551978712.036292, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.036277}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7236960093180339, \"sum\": 0.7236960093180339, \"min\": 0.7236960093180339}}, \"EndTime\": 1551978712.036416, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.036378}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.34821653366088867, \"sum\": 0.34821653366088867, \"min\": 0.34821653366088867}}, \"EndTime\": 1551978712.036517, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.036479}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0688482920328777, \"sum\": 1.0688482920328777, \"min\": 1.0688482920328777}}, \"EndTime\": 1551978712.036619, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.036596}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=6, train multiclass_cross_entropy_objective <loss>=0.813670539856\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=6, criteria=multiclass_cross_entropy_objective, value=0.348216533661\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 6: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 46 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}, \"Total Records Seen\": {\"count\": 1, \"max\": 1080, \"sum\": 1080.0, \"min\": 1080}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 9, \"sum\": 9.0, \"min\": 9}}, \"EndTime\": 1551978712.03839, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 6}, \"StartTime\": 1551978712.022673}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=7584.86512553 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.038] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 8, \"duration\": 15, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7773737589518229, \"sum\": 0.7773737589518229, \"min\": 0.7773737589518229}}, \"EndTime\": 1551978712.054015, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.05398}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5944395065307617, \"sum\": 0.5944395065307617, \"min\": 0.5944395065307617}}, \"EndTime\": 1551978712.054081, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054068}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.541653823852539, \"sum\": 0.541653823852539, \"min\": 0.541653823852539}}, \"EndTime\": 1551978712.054132, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.05412}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.65316162109375, \"sum\": 0.65316162109375, \"min\": 0.65316162109375}}, \"EndTime\": 1551978712.054292, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054275}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7343975067138672, \"sum\": 0.7343975067138672, \"min\": 0.7343975067138672}}, \"EndTime\": 1551978712.054399, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054383}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8516191482543946, \"sum\": 0.8516191482543946, \"min\": 0.8516191482543946}}, \"EndTime\": 1551978712.054497, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054482}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6401575724283854, \"sum\": 0.6401575724283854, \"min\": 0.6401575724283854}}, \"EndTime\": 1551978712.054592, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054577}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6965428034464518, \"sum\": 0.6965428034464518, \"min\": 0.6965428034464518}}, \"EndTime\": 1551978712.054686, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054671}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4189273516337077, \"sum\": 0.4189273516337077, \"min\": 0.4189273516337077}}, \"EndTime\": 1551978712.054818, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054769}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4027654965718587, \"sum\": 0.4027654965718587, \"min\": 0.4027654965718587}}, \"EndTime\": 1551978712.054894, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.05488}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7611984252929688, \"sum\": 0.7611984252929688, \"min\": 0.7611984252929688}}, \"EndTime\": 1551978712.054967, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.054927}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.44429855346679686, \"sum\": 0.44429855346679686, \"min\": 0.44429855346679686}}, \"EndTime\": 1551978712.055018, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.055006}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7496308008829753, \"sum\": 0.7496308008829753, \"min\": 0.7496308008829753}}, \"EndTime\": 1551978712.055152, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.055136}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6870265960693359, \"sum\": 0.6870265960693359, \"min\": 0.6870265960693359}}, \"EndTime\": 1551978712.055198, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.055186}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32991676330566405, \"sum\": 0.32991676330566405, \"min\": 0.32991676330566405}}, \"EndTime\": 1551978712.055277, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.055264}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.058972994486491, \"sum\": 1.058972994486491, \"min\": 1.058972994486491}}, \"EndTime\": 1551978712.055322, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.05531}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=7, train multiclass_cross_entropy_objective <loss>=0.777373758952\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=7, criteria=multiclass_cross_entropy_objective, value=0.329916763306\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 7: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 53 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}, \"Total Records Seen\": {\"count\": 1, \"max\": 1200, \"sum\": 1200.0, \"min\": 1200}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 10, \"sum\": 10.0, \"min\": 10}}, \"EndTime\": 1551978712.057214, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 7}, \"StartTime\": 1551978712.038578}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=6392.45681772 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.057] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 9, \"duration\": 18, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7463033040364583, \"sum\": 0.7463033040364583, \"min\": 0.7463033040364583}}, \"EndTime\": 1551978712.068777, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.068747}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5713159561157226, \"sum\": 0.5713159561157226, \"min\": 0.5713159561157226}}, \"EndTime\": 1551978712.068827, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.068815}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5041853586832682, \"sum\": 0.5041853586832682, \"min\": 0.5041853586832682}}, \"EndTime\": 1551978712.068869, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.068859}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6184466044108073, \"sum\": 0.6184466044108073, \"min\": 0.6184466044108073}}, \"EndTime\": 1551978712.06895, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.068937}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7015323638916016, \"sum\": 0.7015323638916016, \"min\": 0.7015323638916016}}, \"EndTime\": 1551978712.068993, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.068982}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.8236244837443034, \"sum\": 0.8236244837443034, \"min\": 0.8236244837443034}}, \"EndTime\": 1551978712.069073, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069061}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.602711804707845, \"sum\": 0.602711804707845, \"min\": 0.602711804707845}}, \"EndTime\": 1551978712.069113, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069102}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6736584981282552, \"sum\": 0.6736584981282552, \"min\": 0.6736584981282552}}, \"EndTime\": 1551978712.069192, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.06918}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3924531936645508, \"sum\": 0.3924531936645508, \"min\": 0.3924531936645508}}, \"EndTime\": 1551978712.069232, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069222}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.40907964706420896, \"sum\": 0.40907964706420896, \"min\": 0.40907964706420896}}, \"EndTime\": 1551978712.069316, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069304}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7395442962646485, \"sum\": 0.7395442962646485, \"min\": 0.7395442962646485}}, \"EndTime\": 1551978712.069356, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069346}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.41651010513305664, \"sum\": 0.41651010513305664, \"min\": 0.41651010513305664}}, \"EndTime\": 1551978712.069439, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069427}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7186754226684571, \"sum\": 0.7186754226684571, \"min\": 0.7186754226684571}}, \"EndTime\": 1551978712.069478, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069468}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6570931116739909, \"sum\": 0.6570931116739909, \"min\": 0.6570931116739909}}, \"EndTime\": 1551978712.06956, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069549}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.31382064819335936, \"sum\": 0.31382064819335936, \"min\": 0.31382064819335936}}, \"EndTime\": 1551978712.069599, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069589}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0492355346679687, \"sum\": 1.0492355346679687, \"min\": 1.0492355346679687}}, \"EndTime\": 1551978712.069677, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.069665}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=8, train multiclass_cross_entropy_objective <loss>=0.746303304036\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=8, criteria=multiclass_cross_entropy_objective, value=0.313820648193\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 8: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 60 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}, \"Total Records Seen\": {\"count\": 1, \"max\": 1320, \"sum\": 1320.0, \"min\": 1320}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 11, \"sum\": 11.0, \"min\": 11}}, \"EndTime\": 1551978712.071403, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 8}, \"StartTime\": 1551978712.05757}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=8560.38642084 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.071] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 10, \"duration\": 14, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7174837748209636, \"sum\": 0.7174837748209636, \"min\": 0.7174837748209636}}, \"EndTime\": 1551978712.092175, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092122}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5510806401570638, \"sum\": 0.5510806401570638, \"min\": 0.5510806401570638}}, \"EndTime\": 1551978712.092255, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.09224}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47840213775634766, \"sum\": 0.47840213775634766, \"min\": 0.47840213775634766}}, \"EndTime\": 1551978712.092306, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092293}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5875644683837891, \"sum\": 0.5875644683837891, \"min\": 0.5875644683837891}}, \"EndTime\": 1551978712.092401, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092386}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6719775517781575, \"sum\": 0.6719775517781575, \"min\": 0.6719775517781575}}, \"EndTime\": 1551978712.092447, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092435}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7974086125691732, \"sum\": 0.7974086125691732, \"min\": 0.7974086125691732}}, \"EndTime\": 1551978712.092492, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092481}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5698493321736654, \"sum\": 0.5698493321736654, \"min\": 0.5698493321736654}}, \"EndTime\": 1551978712.092534, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092523}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6533986409505208, \"sum\": 0.6533986409505208, \"min\": 0.6533986409505208}}, \"EndTime\": 1551978712.092578, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092567}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.34979562759399413, \"sum\": 0.34979562759399413, \"min\": 0.34979562759399413}}, \"EndTime\": 1551978712.092622, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092609}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4186460812886556, \"sum\": 0.4186460812886556, \"min\": 0.4186460812886556}}, \"EndTime\": 1551978712.092664, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092654}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7199471155802409, \"sum\": 0.7199471155802409, \"min\": 0.7199471155802409}}, \"EndTime\": 1551978712.092706, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092696}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3936609268188477, \"sum\": 0.3936609268188477, \"min\": 0.3936609268188477}}, \"EndTime\": 1551978712.092748, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092737}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6901418685913085, \"sum\": 0.6901418685913085, \"min\": 0.6901418685913085}}, \"EndTime\": 1551978712.092789, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092778}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6277786254882812, \"sum\": 0.6277786254882812, \"min\": 0.6277786254882812}}, \"EndTime\": 1551978712.092831, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.09282}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2992397944132487, \"sum\": 0.2992397944132487, \"min\": 0.2992397944132487}}, \"EndTime\": 1551978712.092874, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092863}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0396380106608072, \"sum\": 1.0396380106608072, \"min\": 1.0396380106608072}}, \"EndTime\": 1551978712.092915, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.092904}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=9, train multiclass_cross_entropy_objective <loss>=0.717483774821\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=9, criteria=multiclass_cross_entropy_objective, value=0.299239794413\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 9: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 66 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}, \"Total Records Seen\": {\"count\": 1, \"max\": 1440, \"sum\": 1440.0, \"min\": 1440}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 12, \"sum\": 12.0, \"min\": 12}}, \"EndTime\": 1551978712.095433, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 9}, \"StartTime\": 1551978712.071741}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=5034.42340585 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.095] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 11, \"duration\": 23, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.68926575978597, \"sum\": 0.68926575978597, \"min\": 0.68926575978597}}, \"EndTime\": 1551978712.115624, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.115576}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5331238110860189, \"sum\": 0.5331238110860189, \"min\": 0.5331238110860189}}, \"EndTime\": 1551978712.115705, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.115678}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.45849335988362633, \"sum\": 0.45849335988362633, \"min\": 0.45849335988362633}}, \"EndTime\": 1551978712.115801, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.115742}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5600701649983724, \"sum\": 0.5600701649983724, \"min\": 0.5600701649983724}}, \"EndTime\": 1551978712.115895, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.115853}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6431121190388998, \"sum\": 0.6431121190388998, \"min\": 0.6431121190388998}}, \"EndTime\": 1551978712.115999, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.115946}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7728307088216145, \"sum\": 0.7728307088216145, \"min\": 0.7728307088216145}}, \"EndTime\": 1551978712.116089, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.116035}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5408933003743489, \"sum\": 0.5408933003743489, \"min\": 0.5408933003743489}}, \"EndTime\": 1551978712.116164, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.11615}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6351846694946289, \"sum\": 0.6351846694946289, \"min\": 0.6351846694946289}}, \"EndTime\": 1551978712.116249, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.11621}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3233705838521322, \"sum\": 0.3233705838521322, \"min\": 0.3233705838521322}}, \"EndTime\": 1551978712.116358, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.116327}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.43166027069091795, \"sum\": 0.43166027069091795, \"min\": 0.43166027069091795}}, \"EndTime\": 1551978712.116459, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.116435}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7020336786905924, \"sum\": 0.7020336786905924, \"min\": 0.7020336786905924}}, \"EndTime\": 1551978712.116562, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.116538}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3745992660522461, \"sum\": 0.3745992660522461, \"min\": 0.3745992660522461}}, \"EndTime\": 1551978712.116663, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.11664}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6619195938110352, \"sum\": 0.6619195938110352, \"min\": 0.6619195938110352}}, \"EndTime\": 1551978712.116763, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.11674}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5983872095743815, \"sum\": 0.5983872095743815, \"min\": 0.5983872095743815}}, \"EndTime\": 1551978712.116869, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.116845}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.28589213689168297, \"sum\": 0.28589213689168297, \"min\": 0.28589213689168297}}, \"EndTime\": 1551978712.116969, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.116945}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0301822026570637, \"sum\": 1.0301822026570637, \"min\": 1.0301822026570637}}, \"EndTime\": 1551978712.117072, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.117048}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=10, train multiclass_cross_entropy_objective <loss>=0.689265759786\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=10, criteria=multiclass_cross_entropy_objective, value=0.285892136892\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 10: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 73 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}, \"Total Records Seen\": {\"count\": 1, \"max\": 1560, \"sum\": 1560.0, \"min\": 1560}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 13, \"sum\": 13.0, \"min\": 13}}, \"EndTime\": 1551978712.119576, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 10}, \"StartTime\": 1551978712.095682}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=4996.04419166 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.119] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 12, \"duration\": 24, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6628746668497721, \"sum\": 0.6628746668497721, \"min\": 0.6628746668497721}}, \"EndTime\": 1551978712.137651, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.137583}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5170018831888835, \"sum\": 0.5170018831888835, \"min\": 0.5170018831888835}}, \"EndTime\": 1551978712.137705, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.137693}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.44094854990641275, \"sum\": 0.44094854990641275, \"min\": 0.44094854990641275}}, \"EndTime\": 1551978712.137748, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.137737}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5355913798014323, \"sum\": 0.5355913798014323, \"min\": 0.5355913798014323}}, \"EndTime\": 1551978712.137787, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.137777}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6153270721435546, \"sum\": 0.6153270721435546, \"min\": 0.6153270721435546}}, \"EndTime\": 1551978712.137824, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.137815}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7497551600138347, \"sum\": 0.7497551600138347, \"min\": 0.7497551600138347}}, \"EndTime\": 1551978712.137893, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.137883}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5153721491495769, \"sum\": 0.5153721491495769, \"min\": 0.5153721491495769}}, \"EndTime\": 1551978712.13794, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.13793}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6186560948689779, \"sum\": 0.6186560948689779, \"min\": 0.6186560948689779}}, \"EndTime\": 1551978712.137989, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.13797}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3239093462626139, \"sum\": 0.3239093462626139, \"min\": 0.3239093462626139}}, \"EndTime\": 1551978712.138027, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.138018}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.44879376093546547, \"sum\": 0.44879376093546547, \"min\": 0.44879376093546547}}, \"EndTime\": 1551978712.138063, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.138054}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6855288823445638, \"sum\": 0.6855288823445638, \"min\": 0.6855288823445638}}, \"EndTime\": 1551978712.138099, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.13809}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.35846786499023436, \"sum\": 0.35846786499023436, \"min\": 0.35846786499023436}}, \"EndTime\": 1551978712.138137, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.138127}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6347478230794271, \"sum\": 0.6347478230794271, \"min\": 0.6347478230794271}}, \"EndTime\": 1551978712.138184, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.138162}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5717206954956054, \"sum\": 0.5717206954956054, \"min\": 0.5717206954956054}}, \"EndTime\": 1551978712.138223, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.138214}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.27359628677368164, \"sum\": 0.27359628677368164, \"min\": 0.27359628677368164}}, \"EndTime\": 1551978712.138261, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.138252}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0208693186442057, \"sum\": 1.0208693186442057, \"min\": 1.0208693186442057}}, \"EndTime\": 1551978712.138299, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.13829}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=11, train multiclass_cross_entropy_objective <loss>=0.66287466685\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=11, criteria=multiclass_cross_entropy_objective, value=0.273596286774\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 11: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 80 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}, \"Total Records Seen\": {\"count\": 1, \"max\": 1680, \"sum\": 1680.0, \"min\": 1680}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 14, \"sum\": 14.0, \"min\": 14}}, \"EndTime\": 1551978712.140201, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 11}, \"StartTime\": 1551978712.119987}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=5882.06432311 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.140] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 13, \"duration\": 20, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6389352798461914, \"sum\": 0.6389352798461914, \"min\": 0.6389352798461914}}, \"EndTime\": 1551978712.156177, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.15614}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5023812294006348, \"sum\": 0.5023812294006348, \"min\": 0.5023812294006348}}, \"EndTime\": 1551978712.156239, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156227}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.4247290293375651, \"sum\": 0.4247290293375651, \"min\": 0.4247290293375651}}, \"EndTime\": 1551978712.156282, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156272}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5138022422790527, \"sum\": 0.5138022422790527, \"min\": 0.5138022422790527}}, \"EndTime\": 1551978712.156322, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156312}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5901207605997721, \"sum\": 0.5901207605997721, \"min\": 0.5901207605997721}}, \"EndTime\": 1551978712.156358, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156349}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7280598322550456, \"sum\": 0.7280598322550456, \"min\": 0.7280598322550456}}, \"EndTime\": 1551978712.156393, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156385}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.49294223785400393, \"sum\": 0.49294223785400393, \"min\": 0.49294223785400393}}, \"EndTime\": 1551978712.156428, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.15642}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6035251617431641, \"sum\": 0.6035251617431641, \"min\": 0.6035251617431641}}, \"EndTime\": 1551978712.156464, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156455}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32845077514648435, \"sum\": 0.32845077514648435, \"min\": 0.32845077514648435}}, \"EndTime\": 1551978712.1565, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156492}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.46945107777913414, \"sum\": 0.46945107777913414, \"min\": 0.46945107777913414}}, \"EndTime\": 1551978712.156536, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156527}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6702213923136393, \"sum\": 0.6702213923136393, \"min\": 0.6702213923136393}}, \"EndTime\": 1551978712.156571, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156562}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.34454158147176106, \"sum\": 0.34454158147176106, \"min\": 0.34454158147176106}}, \"EndTime\": 1551978712.156606, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156597}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6100744247436524, \"sum\": 0.6100744247436524, \"min\": 0.6100744247436524}}, \"EndTime\": 1551978712.15664, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156632}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5499372482299805, \"sum\": 0.5499372482299805, \"min\": 0.5499372482299805}}, \"EndTime\": 1551978712.156676, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156668}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2621593475341797, \"sum\": 0.2621593475341797, \"min\": 0.2621593475341797}}, \"EndTime\": 1551978712.156709, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156701}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0117002487182618, \"sum\": 1.0117002487182618, \"min\": 1.0117002487182618}}, \"EndTime\": 1551978712.156742, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.156734}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=12, train multiclass_cross_entropy_objective <loss>=0.638935279846\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=12, criteria=multiclass_cross_entropy_objective, value=0.262159347534\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 12: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 86 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}, \"Total Records Seen\": {\"count\": 1, \"max\": 1800, \"sum\": 1800.0, \"min\": 1800}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1551978712.158875, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 12}, \"StartTime\": 1551978712.14052}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=6493.14945494 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.159] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 14, \"duration\": 18, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6178718566894531, \"sum\": 0.6178718566894531, \"min\": 0.6178718566894531}}, \"EndTime\": 1551978712.175118, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175089}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.48900636037190753, \"sum\": 0.48900636037190753, \"min\": 0.48900636037190753}}, \"EndTime\": 1551978712.175168, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175157}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.41010042826334636, \"sum\": 0.41010042826334636, \"min\": 0.41010042826334636}}, \"EndTime\": 1551978712.175211, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175201}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.49440803527832033, \"sum\": 0.49440803527832033, \"min\": 0.49440803527832033}}, \"EndTime\": 1551978712.175253, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175242}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5683236440022786, \"sum\": 0.5683236440022786, \"min\": 0.5683236440022786}}, \"EndTime\": 1551978712.17529, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175281}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.7076383590698242, \"sum\": 0.7076383590698242, \"min\": 0.7076383590698242}}, \"EndTime\": 1551978712.175327, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175318}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47331167856852213, \"sum\": 0.47331167856852213, \"min\": 0.47331167856852213}}, \"EndTime\": 1551978712.175363, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175354}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.589611307779948, \"sum\": 0.589611307779948, \"min\": 0.589611307779948}}, \"EndTime\": 1551978712.175402, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175392}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3155112902323405, \"sum\": 0.3155112902323405, \"min\": 0.3155112902323405}}, \"EndTime\": 1551978712.17544, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175431}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.491968313852946, \"sum\": 0.491968313852946, \"min\": 0.491968313852946}}, \"EndTime\": 1551978712.175476, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175467}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6559472401936849, \"sum\": 0.6559472401936849, \"min\": 0.6559472401936849}}, \"EndTime\": 1551978712.175512, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175503}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.33217391967773435, \"sum\": 0.33217391967773435, \"min\": 0.33217391967773435}}, \"EndTime\": 1551978712.175548, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175538}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5885320663452148, \"sum\": 0.5885320663452148, \"min\": 0.5885320663452148}}, \"EndTime\": 1551978712.175588, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175577}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5322479565938314, \"sum\": 0.5322479565938314, \"min\": 0.5322479565938314}}, \"EndTime\": 1551978712.175627, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175617}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2515485604604085, \"sum\": 0.2515485604604085, \"min\": 0.2515485604604085}}, \"EndTime\": 1551978712.175665, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175655}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 1.0026753743489583, \"sum\": 1.0026753743489583, \"min\": 1.0026753743489583}}, \"EndTime\": 1551978712.1757, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.175691}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=13, train multiclass_cross_entropy_objective <loss>=0.617871856689\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=13, criteria=multiclass_cross_entropy_objective, value=0.25154856046\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 13: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 93 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}, \"Total Records Seen\": {\"count\": 1, \"max\": 1920, \"sum\": 1920.0, \"min\": 1920}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 16, \"sum\": 16.0, \"min\": 16}}, \"EndTime\": 1551978712.177369, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 13}, \"StartTime\": 1551978712.159178}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=6534.20158903 records/second\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.177] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 15, \"duration\": 18, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5991350173950195, \"sum\": 0.5991350173950195, \"min\": 0.5991350173950195}}, \"EndTime\": 1551978712.192545, \"Dimensions\": {\"model\": 0, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192505}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47667751312255857, \"sum\": 0.47667751312255857, \"min\": 0.47667751312255857}}, \"EndTime\": 1551978712.192605, \"Dimensions\": {\"model\": 1, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.19259}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.3976254463195801, \"sum\": 0.3976254463195801, \"min\": 0.3976254463195801}}, \"EndTime\": 1551978712.192655, \"Dimensions\": {\"model\": 2, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192643}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.47713569005330403, \"sum\": 0.47713569005330403, \"min\": 0.47713569005330403}}, \"EndTime\": 1551978712.192704, \"Dimensions\": {\"model\": 3, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.19269}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5496013005574544, \"sum\": 0.5496013005574544, \"min\": 0.5496013005574544}}, \"EndTime\": 1551978712.192749, \"Dimensions\": {\"model\": 4, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192737}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6883989969889323, \"sum\": 0.6883989969889323, \"min\": 0.6883989969889323}}, \"EndTime\": 1551978712.192794, \"Dimensions\": {\"model\": 5, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192783}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.45620110829671223, \"sum\": 0.45620110829671223, \"min\": 0.45620110829671223}}, \"EndTime\": 1551978712.192838, \"Dimensions\": {\"model\": 6, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192825}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5767693201700846, \"sum\": 0.5767693201700846, \"min\": 0.5767693201700846}}, \"EndTime\": 1551978712.192884, \"Dimensions\": {\"model\": 7, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192872}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.2901382764180501, \"sum\": 0.2901382764180501, \"min\": 0.2901382764180501}}, \"EndTime\": 1551978712.192926, \"Dimensions\": {\"model\": 8, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192916}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.514265791575114, \"sum\": 0.514265791575114, \"min\": 0.514265791575114}}, \"EndTime\": 1551978712.192968, \"Dimensions\": {\"model\": 9, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192957}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.6425748189290365, \"sum\": 0.6425748189290365, \"min\": 0.6425748189290365}}, \"EndTime\": 1551978712.193009, \"Dimensions\": {\"model\": 10, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.192999}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.32083326975504556, \"sum\": 0.32083326975504556, \"min\": 0.32083326975504556}}, \"EndTime\": 1551978712.193052, \"Dimensions\": {\"model\": 11, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.193041}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5696324666341146, \"sum\": 0.5696324666341146, \"min\": 0.5696324666341146}}, \"EndTime\": 1551978712.193097, \"Dimensions\": {\"model\": 12, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.193084}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.5160272916158041, \"sum\": 0.5160272916158041, \"min\": 0.5160272916158041}}, \"EndTime\": 1551978712.193141, \"Dimensions\": {\"model\": 13, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.19313}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.24160165786743165, \"sum\": 0.24160165786743165, \"min\": 0.24160165786743165}}, \"EndTime\": 1551978712.193184, \"Dimensions\": {\"model\": 14, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.193173}\n",
      "\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"train_multiclass_cross_entropy_objective\": {\"count\": 1, \"max\": 0.9937948226928711, \"sum\": 0.9937948226928711, \"min\": 0.9937948226928711}}, \"EndTime\": 1551978712.193225, \"Dimensions\": {\"model\": 15, \"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.193215}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, epoch=14, train multiclass_cross_entropy_objective <loss>=0.599135017395\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #early_stopping_criteria_metric: host=algo-1, epoch=14, criteria=multiclass_cross_entropy_objective, value=0.241601657867\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Epoch 14: Loss improved. Updating best model\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #progress_metric: host=algo-1, completed 100 % of epochs\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"Max Batches Seen Between Resets\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Batches Since Last Reset\": {\"count\": 1, \"max\": 1, \"sum\": 1.0, \"min\": 1}, \"Number of Records Since Last Reset\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Total Batches Seen\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}, \"Total Records Seen\": {\"count\": 1, \"max\": 2040, \"sum\": 2040.0, \"min\": 2040}, \"Max Records Seen Between Resets\": {\"count\": 1, \"max\": 120, \"sum\": 120.0, \"min\": 120}, \"Reset Count\": {\"count\": 1, \"max\": 17, \"sum\": 17.0, \"min\": 17}}, \"EndTime\": 1551978712.195203, \"Dimensions\": {\"Host\": \"algo-1\", \"Meta\": \"training_data_iter\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\", \"epoch\": 14}, \"StartTime\": 1551978712.177609}\n",
      "\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #throughput_metric: host=algo-1, train throughput=6777.39523861 records/second\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 WARNING 140277105186624] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 WARNING 140277105186624] wait_for_all_workers will not sync workers since the kv store is not running distributed\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.195] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 16, \"duration\": 17, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.196] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 17, \"duration\": 0, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('multiclass_cross_entropy_objective', 0.23236282666524252)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('multiclass_accuracy', 0.95833333333333337)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('multiclass_top_k_accuracy_3', 1.0)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('dcg', 0.98462206522623696)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('macro_recall', 0.95745611)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('macro_precision', 0.95726496)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #train_score (algo-1) : ('macro_f_1.000', 0.95725793)\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train multiclass_cross_entropy_objective <loss>=0.232362826665\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train multiclass_accuracy <score>=0.958333333333\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train multiclass_top_k_accuracy_3 <score>=1.0\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train dcg <score>=0.984622065226\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train macro_recall <score>=0.957456111908\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train macro_precision <score>=0.957264959812\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] #quality_metric: host=algo-1, train macro_f_1.000 <score>=0.957257926464\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Best model found for hyperparameters: {\"lr_scheduler_step\": 86, \"wd\": 0.001434532164232352, \"optimizer\": \"rmsprop\", \"lr_scheduler_factor\": 0.971971636421263, \"l1\": 0.022797154344820547, \"learning_rate\": 0.11741784914926481, \"lr_scheduler_minimum_lr\": 0.0006136045802294309}\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Saved checkpoint to \"/tmp/tmpVI9s5a/mx-mod-0000.params\"\u001b[0m\n",
      "\u001b[31m[03/07/2019 17:11:52 INFO 140277105186624] Test data is not provided.\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.242] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"epoch\": 18, \"duration\": 43, \"num_examples\": 1}\u001b[0m\n",
      "\u001b[31m[2019-03-07 17:11:52.242] [tensorio] [info] data_pipeline_stats={\"name\": \"/opt/ml/input/data/train\", \"duration\": 443, \"num_epochs\": 19, \"num_examples\": 19}\u001b[0m\n",
      "\u001b[31m#metrics {\"Metrics\": {\"totaltime\": {\"count\": 1, \"max\": 690.593957901001, \"sum\": 690.593957901001, \"min\": 690.593957901001}, \"finalize.time\": {\"count\": 1, \"max\": 38.645029067993164, \"sum\": 38.645029067993164, \"min\": 38.645029067993164}, \"initialize.time\": {\"count\": 1, \"max\": 179.7480583190918, \"sum\": 179.7480583190918, \"min\": 179.7480583190918}, \"check_early_stopping.time\": {\"count\": 15, \"max\": 2.6159286499023438, \"sum\": 14.710187911987305, \"min\": 0.5910396575927734}, \"setuptime\": {\"count\": 1, \"max\": 12.382984161376953, \"sum\": 12.382984161376953, \"min\": 12.382984161376953}, \"update.time\": {\"count\": 15, \"max\": 48.33984375, \"sum\": 350.9705066680908, \"min\": 13.689041137695312}, \"epochs\": {\"count\": 1, \"max\": 15, \"sum\": 15.0, \"min\": 15}}, \"EndTime\": 1551978712.243098, \"Dimensions\": {\"Host\": \"algo-1\", \"Operation\": \"training\", \"Algorithm\": \"Linear Learner\"}, \"StartTime\": 1551978711.651499}\n",
      "\u001b[0m\n",
      "\n",
      "2019-03-07 17:12:03 Completed - Training job completed\n",
      "Billable seconds: 35\n"
     ]
    }
   ],
   "source": [
    "linear.fit(recorded_train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: linear-learner-2019-03-07-17-24-38-590\n",
      "INFO:sagemaker:Creating endpoint with name linear\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------------------------------------------------------!"
     ]
    }
   ],
   "source": [
    "linear_predictor = linear.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type='ml.t2.medium',\n",
    "    endpoint_name='linear'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Endpoint Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data_location = 's3://{}/{}/test_data'.format(bucket, prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-test-mdebbich/test-iris/linear-model/test_data'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_input = np.array(test)\n",
    "\n",
    "test_vectors = test_input[:, :4].astype(\"float32\")\n",
    "test_labels = test_input[:, 4].astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = linear_predictor.predict(\n",
    "    test_vectors\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method predict in module sagemaker.predictor:\n",
      "\n",
      "predict(data, initial_args=None) method of sagemaker.amazon.linear_learner.LinearLearnerPredictor instance\n",
      "    Return the inference from the specified endpoint.\n",
      "    \n",
      "    Args:\n",
      "        data (object): Input data for which you want the model to provide inference.\n",
      "            If a serializer was specified when creating the RealTimePredictor, the result of the\n",
      "            serializer is sent as input data. Otherwise the data must be sequence of bytes, and\n",
      "            the predict method then sends the bytes in the request body as is.\n",
      "        initial_args (dict[str,str]): Optional. Default arguments for boto3\n",
      "            ``invoke_endpoint`` call. Default is None (no default arguments).\n",
      "    \n",
      "    Returns:\n",
      "        object: Inference for the given input. If a deserializer was specified when creating\n",
      "            the RealTimePredictor, the result of the deserializer is returned. Otherwise the response\n",
      "            returns the sequence of bytes as is.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(linear_predictor.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "classes = np.array([res.label[\"predicted_label\"].float32_tensor.values[0] for res in results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(classes == test_labels).sum() / len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the endpoint:\n",
    "# linear_predictor.delete_endpoint()"
   ]
  }
 ],
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