From 90fe92d1f72b6bc46bb5c15ef6eabf06f2bc837a Mon Sep 17 00:00:00 2001
From: Kyle O'Connell <oconnellka@nih.gov>
Date: Fri, 24 May 2024 11:45:57 -0400
Subject: [PATCH] updated notebook formatting to align with standard format

---
 notebooks/GWAS/GWAS_coat_color.ipynb          |   92 +-
 notebooks/GenAI/Pubmed_RAG_chatbot.ipynb      |  170 +--
 notebooks/SRADownload/SRA-Download.ipynb      |  290 ++--
 .../SpleenSeg_Pretrained-4_27.ipynb           | 1202 ++---------------
 notebooks/pangolin/pangolin_pipeline.ipynb    |  124 +-
 .../RNAseq_pipeline.ipynb                     |  102 +-
 tutorials/README.md                           |   96 --
 .../notebooks/GenAI/Pubmed_RAG_chatbot.ipynb  |  868 ------------
 8 files changed, 566 insertions(+), 2378 deletions(-)
 delete mode 100644 tutorials/README.md
 delete mode 100644 tutorials/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb

diff --git a/notebooks/GWAS/GWAS_coat_color.ipynb b/notebooks/GWAS/GWAS_coat_color.ipynb
index 711a4d8..c36fa85 100644
--- a/notebooks/GWAS/GWAS_coat_color.ipynb
+++ b/notebooks/GWAS/GWAS_coat_color.ipynb
@@ -6,8 +6,36 @@
    "metadata": {},
    "source": [
     "# GWAS in the cloud\n",
+    "## Overview\n",
     "We adapted the NIH CFDE tutorial from [here](https://training.nih-cfde.org/en/latest/Bioinformatic-Analyses/GWAS-in-the-cloud/background/) and fit it to a notebook. We have greatly simplified the instructions, so if you need or want more details, look at the full tutorial to find out more.\n",
-    "Most of this notebook is bash, but expects that you are using a Python kernel, until step 3, plotting, you will need to switch your kernel to R."
+    "\n",
+    "Most of this notebook is written in Bash, but expects that you are using a Python kernel, until step 3, plotting where you will need to switch your kernel to R."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3edafe63",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "The goal is to learn how to execute a GWAS analysis in a cloud environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d7ef396",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "+ You only need access to a Sagemaker notebook environment to run this notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39ee9668",
+   "metadata": {},
+   "source": [
+    "## Get Started"
    ]
   },
   {
@@ -15,8 +43,9 @@
    "id": "8fbf6304",
    "metadata": {},
    "source": [
-    "## 1. Setup\n",
-    "### Download the data\n",
+    "### Install packages and set up environment\n",
+    "\n",
+    "#### Download the data\n",
     "use %%bash to denote a bash block. You can also use '!' to denote a single bash command within a Python notebook"
    ]
   },
@@ -68,7 +97,31 @@
     "tags": []
    },
    "source": [
-    "## 1. Install dependencies"
+    "### Install dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f5032d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install mamba\n",
+    "! curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh\n",
+    "! bash Mambaforge-$(uname)-$(uname -m).sh -b -p $HOME/mambaforge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a5bd340",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to your path\n",
+    "import os\n",
+    "os.environ[\"PATH\"] += os.pathsep + os.environ[\"HOME\"]+\"/mambaforge/bin\""
    ]
   },
   {
@@ -78,6 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# install everything else\n",
     "! mamba install -y -c bioconda plink vcftools"
    ]
   },
@@ -86,7 +140,7 @@
    "id": "3de2fc4c",
    "metadata": {},
    "source": [
-    "## 2. Analyze"
+    "## Analyze"
    ]
   },
   {
@@ -266,7 +320,7 @@
    "id": "1f52e97c",
    "metadata": {},
    "source": [
-    "## 3. Plotting\n",
+    "## Plotting\n",
     "In this tutorial, plotting is done in R, so at this point you can change your kernel to R in the top right. Wait for it to say 'idle' in the bottom left, then continue. You could also plot using Python native packages and maintain the Python notebook kernel."
    ]
   },
@@ -359,6 +413,32 @@
     "\n",
     "The top associated mutation is a nonsense SNP in the gene MC1R known to control pigment production. The MC1R allele encoding yellow coat color contains a single base change (from C to T) at the 916th nucleotide."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f6e1ef6",
+   "metadata": {},
+   "source": [
+    "### Conclusion\n",
+    "Here we learned how to run a simple GWAS analysis in the cloud"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "044a04d8",
+   "metadata": {},
+   "source": [
+    "## Clean up\n",
+    "Make sure you shut down this VM, or delete it if you don't plan to use if further.\n",
+    "\n",
+    "You can also [delete the buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) if you don't want to pay for the data: `aws s3 rb s3://bucket-name --force`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1e7be16",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb b/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb
index 09aa19a..81daf02 100644
--- a/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb
+++ b/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb
@@ -8,19 +8,50 @@
     "# Creating a PubMed Chatbot with Llama2"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "6824ed09",
+   "metadata": {},
+   "source": [
+    "## Overview"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3ecea2ad-7c65-4367-87e1-b021167c3a1d",
    "metadata": {},
    "source": [
-    "For this tutorial we are creating a PubMed chatbot that will answer questions by gathering information from documents we have provided via a index. The model we will be using today is a pretrained Llama2 model from Jumpstart.\n",
-    "\n",
-    "This tutorial will go over the following topics:\n",
+    "For this tutorial we are creating a PubMed chatbot that will answer questions by gathering information from documents we have provided via an index. The model we will be using today is a pretrained Llama2 model from Jumpstart."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df0a5b23",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
     "- Introduce langchain\n",
     "- Explain the differences between zero-shot, one-shot, and few-shot prompting\n",
     "- Practice using different document retrievers"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "41f7a40b",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "You need access to SageMaker, Model Jumpstart, S3, and Kendra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0da27877",
+   "metadata": {},
+   "source": [
+    "## Get Started"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4d01e74b-b5b4-4be9-b16e-ec55419318ef",
@@ -34,12 +65,12 @@
    "id": "9dbd13e7-afc9-416b-94dc-418a93e14587",
    "metadata": {},
    "source": [
-    "Identify which model we want to deploy from Jumpstart, in this case we are using Llama2."
+    "Identify which model we want to deploy from Jumpstart, in this case we are using Llama2 with 7 billion parameters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "6b51bf71-d2e5-4afc-8569-338767b43b9c",
    "metadata": {
     "tags": []
@@ -60,49 +91,17 @@
    "id": "624c9bb8-3ce2-4240-b2b8-6b1bb93bb9f2",
    "metadata": {},
    "source": [
-    "Create an endpoint so that we can communicate with our model, send inputs, and retrieve outputs."
+    "Create an endpoint to deploy our model locally, so that we can communicate with our model, send inputs, and retrieve outputs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "bf27747d-443f-47e7-9d2c-a8f5c5c6f3b8",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
-      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "For forward compatibility, pin to model_version='2.*' in your JumpStartModel or JumpStartEstimator definitions. Note that major version upgrades may have different EULA acceptance terms and input/output signatures.\n",
-      "For forward compatibility, pin to model_version='2.*' in your JumpStartModel or JumpStartEstimator definitions. Note that major version upgrades may have different EULA acceptance terms and input/output signatures.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-----------------!"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sagemaker.jumpstart.model import JumpStartModel\n",
     "\n",
@@ -120,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "1ad71f0d-3be5-4b03-9c1c-eb4585721fc8",
    "metadata": {
     "tags": []
@@ -143,7 +142,7 @@
    "id": "5a820eea-1538-4f40-86c4-eb14fe09e127",
    "metadata": {},
    "source": [
-    "Our chatbot will rely on documents to answer our questions to do so we are supplying it a **vector index**. A vector index or index is a data structure that enables fast and accurate search and retrieval of vector embeddings from a large dataset of objects. We will be working with two options for our index PubMed API vs Kendra Index."
+    "Our chatbot respond to prompts based on the documents we supplied. This occurs via a **vector index**. A vector index is a data structure composed of vectorized embeddings (generated from our inputs) that enables fast and accurate search and retrieval from a large dataset of objects. We will explore using two methods to generate our index: PubMed API vs Kendra Index."
    ]
   },
   {
@@ -153,9 +152,9 @@
    "source": [
     "**What is the difference?**\n",
     "\n",
-    "The **PubMed API** is provided free by langchain to connect your model to more than **35 million citations** for biomedical literature from MEDLINE, life science journals, and online books. **Kendra index** is a AWS product that allows the user more **security and control** on which documents you wish to supply to your model. \n",
+    "The **PubMed API** is provided free by langchain to connect your model to more than **35 million citations** for biomedical literature from MEDLINE, life science journals, and online books. **Kendra index** is an AWS product that allows the user more **security and control** on which documents you wish to supply to your model. \n",
     "\n",
-    "We will be exploring both methods to see which produces the best results!"
+    "We will explore both methods to see which produces the best results!"
    ]
   },
   {
@@ -179,7 +178,7 @@
    "id": "1d1c9de7-4a06-4f85-b9ff-c8c9e51f8c70",
    "metadata": {},
    "source": [
-    "AWS marketplace has PubMed database named **PubMed Central® (PMC)** that contains free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). We will be subsetting this database to add documents to our Kendra index. Ensure that you have the correct roles and policies to allow your environment to connect to S3 buckets, SageMaker, and Kendra."
+    "AWS marketplace provides a PubMed database named **PubMed Central® (PMC)** that contains free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). We will subset this database to add documents to our Kendra index. Ensure that you have the correct roles and policies to allow your environment to connect to S3 buckets, SageMaker, and Kendra."
    ]
   },
   {
@@ -199,7 +198,7 @@
    "source": [
     "#make bucket\n",
     "bucket = 'pubmed-chat-docs'\n",
-    "!aws s3 mb s3://{bucket}"
+    "! aws s3 mb s3://{bucket}"
    ]
   },
   {
@@ -207,7 +206,7 @@
    "id": "b6ad30ba-cee8-47f9-bc1e-ece8961ac66a",
    "metadata": {},
    "source": [
-    "We will then download the metadata file from the PMC bucket, this will list all of the articles within the PMC bucket and their bucket paths we will use this to subset the database into our own bucket."
+    "Next we will download the metadata file from the PMC bucket. This metadata file will list all of the articles within the PMC bucket and their paths. We will use these data to subset the database into our own bucket."
    ]
   },
   {
@@ -218,7 +217,7 @@
    "outputs": [],
    "source": [
     "#download the metadata file\n",
-    "!aws s3 cp  s3://pmc-oa-opendata/oa_comm/txt/metadata/txt/oa_comm.filelist.txt . --sse"
+    "! aws s3 cp  s3://pmc-oa-opendata/oa_comm/txt/metadata/txt/oa_comm.filelist.txt . --sse"
    ]
   },
   {
@@ -226,31 +225,24 @@
    "id": "93a8595a-767f-4cad-9273-62d8e2cf60d1",
    "metadata": {},
    "source": [
-    "We only want the metadata of the first 100 files."
+    "We only want the metadata of the first 100 files to keep this tutorial short."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "c26b0f29-2b07-43a6-800d-4aa5e957fe52",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
-      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#import the file as a dataframe\n",
     "import pandas as pd\n",
     "import os\n",
+    "\n",
     "df = pd.read_csv('oa_comm.filelist.csv')\n",
+    "\n",
     "#first 100 files\n",
     "first_100=df[0:101]\n",
     "#save new metadata\n",
@@ -262,7 +254,7 @@
    "id": "abd1ae93-450e-4c79-83cc-ea46a1b507c1",
    "metadata": {},
    "source": [
-    "Lets look at our metadata! We can see that the bucket path to the files are under the **Key** column this is what we will use to loop through the PMC bucket and copy the first 100 files to our bucket."
+    "Lets look at our metadata! We can see that the bucket path to the files are under the **Key** column. This column is what we will use to loop through the PMC bucket and copy the first 100 files to our bucket."
    ]
   },
   {
@@ -303,7 +295,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!aws s3 cp oa_comm.filelist_100.csv s3://{bucket}/docs/"
+    "! aws s3 cp oa_comm.filelist_100.csv s3://{bucket}/docs/"
    ]
   },
   {
@@ -329,13 +321,13 @@
    "id": "3ba2291e-109e-4120-ad10-5dbfd341a07b",
    "metadata": {},
    "source": [
-    "Inorder for us to fluidly send input and receive outputs from our chatbot we need to create a **inference script** that will format inputs in a way that the chatbot can understand and format outputs in a way we can understand. We will also be supplying instructions to the chatbot through the script.\n",
+    "For us to fluidly send input and receive outputs from our chatbot we need to create an [**inference script**](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-model.html#deploy-model-options) that will format inputs in a way that the chatbot can understand and format outputs in a way we can understand. We will also be supplying instructions to the chatbot through the script.\n",
     "\n",
-    "Our script will utilize **langchain** tools and packages to enable our model to:\n",
+    "Our script will utilize **LangChain** tools and packages to enable our model to:\n",
     "- **Connect to sources of context** (e.g. providing our model with tasks and examples)\n",
     "- **Rely on reason** (e.g. instruct our model how to answer based on provided context)\n",
     "\n",
-    "**Warning**: The over all inference script must be run on the terminal via the command `python YOUR_SCRIPT.py`."
+    "**Warning**: The inference script must be run on the terminal via the command `python YOUR_SCRIPT.py`."
    ]
   },
   {
@@ -734,28 +726,17 @@
    "id": "1abcbd48-bb84-4310-b8eb-ad87850a8649",
    "metadata": {},
    "source": [
-    "Running our script in the terminal will require us to export the following global variables then running our python script. Dont forget to run you python script on the terminal use the command `python NAME_OF_YOUR_SCRIPT.py`. For more guidence take a look at our **example inference scripts** for the [PubMed API](/example_scripts/langchain_chat_llama_2_zeroshot.py) and [Kendra](/example_scripts/kendra_chat_llama_2.py)."
+    "Running our script in the terminal will require us to export the following global variables before running the python script. Don't forget to run you python script on the terminal using the command `python NAME_OF_YOUR_SCRIPT.py`. For more guidence take a look at our **example inference scripts** for the [PubMed API](/example_scripts/langchain_chat_llama_2_zeroshot.py) and [Kendra](/example_scripts/kendra_chat_llama_2.py)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "ba97df23-6893-438d-8a67-cb7dbf83e407",
    "metadata": {
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'meta-textgeneration-llama-2-7b-f-2023-11-21-20-18-40-341'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#retreive our endpoint id\n",
     "endpoint_id"
@@ -768,7 +749,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#enter the global variables in your terminal\n",
+    "#enter these global variables in your terminal\n",
     "export AWS_REGION='<Enter_location>'\n",
     "export LLAMA_2_ENDPOINT='<Enter_endpoint_id>'\n",
     "export KENDRA_INDEX_ID='<Enter_kendra_index>'"
@@ -789,7 +770,16 @@
    "id": "80c8fb4b-e74f-4e8d-892b-0f913eff747d",
    "metadata": {},
    "source": [
-    "![PubMed Chatbot Results](../../../docs/images/PubMed_chatbot_results.png)"
+    "![PubMed Chatbot Results](../../docs/images/PubMed_chatbot_results.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddeee006",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "Here you learned how to deploy a model, create a vector database (index) from PubMed documents, and then interact with a model to product predictions using an inference script. "
    ]
   },
   {
@@ -810,22 +800,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "c307bb17-757a-4579-a0d8-698eb1bb3f2e",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'endpoint' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#Delete model and endpoint\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#model.delete()\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mendpoint\u001b[49m\u001b[38;5;241m.\u001b[39mdelete()\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'endpoint' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "#Delete model and endpoint\n",
     "model.delete()\n",
@@ -840,7 +818,7 @@
    "outputs": [],
    "source": [
     "#Delete bucket\n",
-    "!aws s3 rb s3://$bucket --force  "
+    "! aws s3 rb s3://$bucket --force  "
    ]
   }
  ],
diff --git a/notebooks/SRADownload/SRA-Download.ipynb b/notebooks/SRADownload/SRA-Download.ipynb
index 3f36742..0866d68 100644
--- a/notebooks/SRADownload/SRA-Download.ipynb
+++ b/notebooks/SRADownload/SRA-Download.ipynb
@@ -18,21 +18,49 @@
     "DNA sequence data are typically deposited into the NCBI Sequence Read Archive, and can be accessed through the SRA website, or via a collection of command line tools called SRA Toolkit. Individual sequence entries are assigned an Accession ID, which can be used to find and download a particular file. For example, if you go to the [SRA database](https://www.ncbi.nlm.nih.gov/sra) in a browser window, and search for `SRX15695630`, you should see an entry for _C. elegans_. Alternatively, you can search the SRA metadata using Amazon Athena and generate a list of accession numbers. Here we are going to generate a list of accessions using Athena, use tools from the SRA Toolkit to download a few fastq files, then copy those fastq files to a cloud bucket. We really only scratch the surface of how to search Athena using SQL. If you want more examples, you can also try the notebooks from [this SRA GitHub repo](https://github.com/ncbi/ASHG-Workshop-2021). "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "989d4270",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "+ Learn more about the Sequence Read Archive\n",
+    "+ Learn how to download SRA data locally\n",
+    "+ Learn how to interact with SRA metadata via Athena"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b19079d",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "+ Make sure you have access to SageMaker, Athena and Glue"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "877bcdc1",
+   "metadata": {},
+   "source": [
+    "## Get Started"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "39f62f42",
    "metadata": {},
    "source": [
-    "### 1) Set up your Athena Database\n",
-    "You need to set up your Athena database in the Athena console before you start this notebook. Follow our [guide](https://github.com/STRIDES/NIHCloudLabAWS/blob/main/docs/create_athena_database.md) to walk you through it."
+    "### Set up environment and install dependencies"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "7aed7098",
+   "id": "aadfbc50",
    "metadata": {},
    "source": [
-    "### 2) Install Dependencies"
+    "### Set up your Athena Database\n",
+    "You need to set up your Athena database in the Athena console before you start this notebook. Follow our [guide](https://github.com/STRIDES/NIHCloudLabAWS/blob/main/docs/create_athena_database.md) to walk you through it."
    ]
   },
   {
@@ -43,6 +71,30 @@
     "At the time of writing, the version of SRA tools available with the Anaconda distribution was v.2.11.0. If you want to install the latest version, download and install from [here](https://github.com/ncbi/sra-tools/wiki/01.-Downloading-SRA-Toolkit). If you do the direct install, you will also need to configure interactively following [this guide](https://github.com/ncbi/sra-tools/wiki/05.-Toolkit-Configuration), you can do that by opening a terminal and running the commands there."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5aa7fc7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install mamba\n",
+    "! curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh\n",
+    "! bash Mambaforge-$(uname)-$(uname -m).sh -b -p $HOME/mambaforge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e57ca51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to your path\n",
+    "import os\n",
+    "os.environ[\"PATH\"] += os.pathsep + os.environ[\"HOME\"]+\"/mambaforge/bin\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -50,6 +102,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# install everything else\n",
     "! mamba install -c bioconda -c conda-forge sra-tools==2.11.0 sql-magic pyathena -y"
    ]
   },
@@ -68,7 +121,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!fasterq-dump -h"
+    "! fasterq-dump -h"
    ]
   },
   {
@@ -76,7 +129,7 @@
    "id": "ddc46609",
    "metadata": {},
    "source": [
-    "### 3) Setup Directory Structure and Create a Staging Bucket"
+    "### Setup Directory Structure and Create a Staging Bucket"
    ]
   },
   {
@@ -91,18 +144,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "827f2447",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/SageMaker/NIHCloudLabAWS/tutorials/notebooks/SRADownload/data\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cd data/"
    ]
@@ -114,7 +159,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#make sure you change this name, it needs to be globally unique\n",
+    "# make sure you change this name, it needs to be globally unique\n",
     "%env BUCKET=sra-data-athena"
    ]
   },
@@ -135,7 +180,7 @@
    "id": "086a50c1",
    "metadata": {},
    "source": [
-    "### 4) Create Accession List using Athena"
+    "### Create a list of SRA accessions using Athena"
    ]
   },
   {
@@ -161,7 +206,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#import packages\n",
+    "# import packages\n",
     "from pyathena import connect\n",
     "import pandas as pd"
    ]
@@ -191,6 +236,7 @@
    "metadata": {},
    "source": [
     "**When you run the query in the next cell you may get this error**:\n",
+    "\n",
     "`An error occurred (AccessDeniedException) when calling the StartQueryExecution operation: User: arn:aws:sts::055102001469:assumed-role/sagemaker-notebook-instance-role/SageMaker is not authorized to perform: athena:StartQueryExecution on resource: arn:aws:athena:us-east-1:055102001469:workgroup/primary because no identity-based policy allows the athena:StartQueryExecution action`\n",
     "\n",
     "If you get this error, read our [IAM guide](https://github.com/STRIDES/NIHCloudLabAWS/blob/main/docs/update_sagemaker_role.md) to set up the correct policy for your Sagemaker role. \n"
@@ -201,7 +247,7 @@
    "id": "5830d8e3",
    "metadata": {},
    "source": [
-    "Now that the permissions are all set up, let's download bacterial samples. You could change the SQL query as you like, feel free to take a look at the generated df, and then play with different parameters. For more inspiration of what is possible with SQL queries, look at this [SRA tutorial](https://github.com/ncbi/ASHG-Workshop-2021/blob/main/3_Biology_Example_AWS_Demo.ipynb)."
+    "Now that the permissions are all set up, let's download bacterial samples from the [NIGMS Sandbox RNAseq tutorial](https://github.com/NIGMS/RNA-Seq-Differential-Expression-Analysis). You could change the SQL query as you like, feel free to take a look at the generated df, and then play with different parameters. For more inspiration of what is possible with SQL queries, look at this [SRA tutorial](https://github.com/ncbi/ASHG-Workshop-2021/blob/main/3_Biology_Example_AWS_Demo.ipynb)."
    ]
   },
   {
@@ -228,7 +274,7 @@
    "id": "d3511937",
    "metadata": {},
    "source": [
-    "As you can see, most of what you need to know is shown in this data frame. If you wanted to just show the accession, you could replace the * for acc in the SELECT command. One other thing to think about is how large are these files, and do you have space on your VM to download them? You can figure this out by looking at the 'jattr' column, and then converting the number of bites to GB, then add that for a few samples to get a ballpark figure. If you need more space, stop the VM, go Compute Engine and either [resize your disk](https://aws.amazon.com/blogs/machine-learning/customize-your-notebook-volume-size-up-to-16-tb-with-amazon-sagemaker/). Make sure you stop your notebook instance to Edit and resize it. You can see the amount of space on your disk from the command line using `!df -h .`"
+    "As you can see, most of what you need to know is shown in this data frame. If you wanted to just show the accession, you could replace the * for acc in the SELECT command. One other thing to think about is how large are these files, and do you have space on your VM to download them? You can figure this out by looking at the 'jattr' column, and then converting the number of bites to GB, then add that for a few samples to get a ballpark figure. If you need more space, stop the VM, go to SageMaker and [resize your disk](https://aws.amazon.com/blogs/machine-learning/customize-your-notebook-volume-size-up-to-16-tb-with-amazon-sagemaker/). Make sure you stop your notebook instance to Edit and resize it. You can see the amount of space on your disk from the command line using `! df -h .`"
    ]
   },
   {
@@ -256,7 +302,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!vdb-dump --info SRR13349124 "
+    "! vdb-dump --info SRR13349124 "
    ]
   },
   {
@@ -266,7 +312,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!srapath SRR13349124"
+    "! srapath SRR13349124"
    ]
   },
   {
@@ -296,7 +342,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "cat list_of_accessionIDS.txt"
+    "! cat list_of_accessionIDS.txt"
    ]
   },
   {
@@ -304,7 +350,7 @@
    "id": "01437b57",
    "metadata": {},
    "source": [
-    "### 5) Download FASTQ files with fasterq dump"
+    "### Download FASTQ files with fasterq dump"
    ]
   },
   {
@@ -317,18 +363,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "4764f355",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/SageMaker/NIHCloudLabAWS/tutorials/notebooks/SRADownload/data/fasterqdump\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cd fasterqdump/"
    ]
@@ -343,33 +381,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "80c2e3b4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "spots read      : 2,054,166\n",
-      "reads read      : 4,108,332\n",
-      "reads written   : 4,108,332\n",
-      "spots read      : 25,734,849\n",
-      "reads read      : 51,469,698\n",
-      "reads written   : 25,734,849\n",
-      "reads 0-length  : 25,734,849\n",
-      "spots read      : 18,624,005\n",
-      "reads read      : 37,248,010\n",
-      "reads written   : 18,624,005\n",
-      "reads 0-length  : 18,624,005\n",
-      "CPU times: user 6.18 s, sys: 1.26 s, total: 7.44 s\n",
-      "Wall time: 6min 36s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "!for x in `cat ../list_of_accessionIDS.txt`; do fasterq-dump -f -O raw_fastq -e 8 -m 4G $x ; done"
+    "! for x in `cat ../list_of_accessionIDS.txt`; do fasterq-dump -f -O raw_fastq -e 8 -m 4G $x ; done"
    ]
   },
   {
@@ -385,7 +403,7 @@
    "id": "55bd52cd",
    "metadata": {},
    "source": [
-    "### 6) Download FASTQ files with prefetch + fasterq dump"
+    "### Download FASTQ files with prefetch + fasterq dump"
    ]
   },
   {
@@ -398,76 +416,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "ddefec2d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/SageMaker/NIHCloudLabAWS/tutorials/notebooks/SRADownload/data/prefetch_fasterqdump\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cd ../prefetch_fasterqdump"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "935f6ca2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "2022-08-30T15:45:12 prefetch.2.11.0: 1) Downloading 'SRR3617061'...\n",
-      "2022-08-30T15:45:12 prefetch.2.11.0:  Downloading via HTTPS...\n",
-      "2022-08-30T15:45:16 prefetch.2.11.0:  HTTPS download succeed\n",
-      "2022-08-30T15:45:17 prefetch.2.11.0:  'SRR3617061' is valid\n",
-      "2022-08-30T15:45:17 prefetch.2.11.0: 1) 'SRR3617061' was downloaded successfully\n",
-      "\n",
-      "2022-08-30T15:45:17 prefetch.2.11.0: 2) Downloading 'SRR8435254'...\n",
-      "2022-08-30T15:45:17 prefetch.2.11.0:  Downloading via HTTPS...\n",
-      "2022-08-30T15:45:23 prefetch.2.11.0:  HTTPS download succeed\n",
-      "2022-08-30T15:45:24 prefetch.2.11.0:  'SRR8435254' is valid\n",
-      "2022-08-30T15:45:24 prefetch.2.11.0: 2) 'SRR8435254' was downloaded successfully\n",
-      "2022-08-30T15:45:24 prefetch.2.11.0: 'SRR8435254' has 0 dependencies\n",
-      "\n",
-      "2022-08-30T15:45:24 prefetch.2.11.0: 3) Downloading 'SRR8435252'...\n",
-      "2022-08-30T15:45:24 prefetch.2.11.0:  Downloading via HTTPS...\n",
-      "2022-08-30T15:45:28 prefetch.2.11.0:  HTTPS download succeed\n",
-      "2022-08-30T15:45:29 prefetch.2.11.0:  'SRR8435252' is valid\n",
-      "2022-08-30T15:45:29 prefetch.2.11.0: 3) 'SRR8435252' was downloaded successfully\n",
-      "2022-08-30T15:45:29 prefetch.2.11.0: 'SRR8435252' has 0 dependencies\n",
-      "CPU times: user 290 ms, sys: 37.5 ms, total: 327 ms\n",
-      "Wall time: 17 s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "!prefetch --option-file ../list_of_accessionIDS.txt -O raw_fastq -f yes"
+    "! prefetch --option-file ../list_of_accessionIDS.txt -O raw_fastq -f yes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "7eece75e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[0m\u001b[01;34mSRR3617061\u001b[0m/  \u001b[01;34mSRR8435252\u001b[0m/  \u001b[01;34mSRR8435254\u001b[0m/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ls raw_fastq/"
    ]
@@ -482,33 +455,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "1852a71a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "spots read      : 2,054,166\n",
-      "reads read      : 4,108,332\n",
-      "reads written   : 4,108,332\n",
-      "spots read      : 25,734,849\n",
-      "reads read      : 51,469,698\n",
-      "reads written   : 25,734,849\n",
-      "reads 0-length  : 25,734,849\n",
-      "spots read      : 18,624,005\n",
-      "reads read      : 37,248,010\n",
-      "reads written   : 18,624,005\n",
-      "reads 0-length  : 18,624,005\n",
-      "CPU times: user 1.49 s, sys: 308 ms, total: 1.8 s\n",
-      "Wall time: 1min 38s\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%%time\n",
-    "!for x in `cat ../list_of_accessionIDS.txt`; do fasterq-dump -f -O raw_fastq -e 8 -m 4G raw_fastq/$x; done"
+    "! for x in `cat ../list_of_accessionIDS.txt`; do fasterq-dump -f -O raw_fastq -e 8 -m 4G raw_fastq/$x; done"
    ]
   },
   {
@@ -524,7 +477,7 @@
    "id": "ea152fd7",
    "metadata": {},
    "source": [
-    "### Step 7) Copy Files to a Bucket"
+    "### Copy Files to a Bucket"
    ]
   },
   {
@@ -537,50 +490,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "ad73308f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "upload: raw_fastq/SRR3617061/SRR3617061.sra to s3://sra-data-athena/raw_fastq/SRR3617061/SRR3617061.sra\n",
-      "upload: raw_fastq/SRR8435252/SRR8435252.sra to s3://sra-data-athena/raw_fastq/SRR8435252/SRR8435252.sra\n",
-      "upload: raw_fastq/SRR3617061_2.fastq to s3://sra-data-athena/raw_fastq/SRR3617061_2.fastq\n",
-      "upload: raw_fastq/SRR3617061_1.fastq to s3://sra-data-athena/raw_fastq/SRR3617061_1.fastq\n",
-      "upload: raw_fastq/SRR8435254/SRR8435254.sra to s3://sra-data-athena/raw_fastq/SRR8435254/SRR8435254.sra\n",
-      "upload: raw_fastq/SRR8435252.fastq to s3://sra-data-athena/raw_fastq/SRR8435252.fastq\n",
-      "upload: raw_fastq/SRR8435254.fastq to s3://sra-data-athena/raw_fastq/SRR8435254.fastq\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!aws s3 cp raw_fastq/*.fastq s3://sra-data-athena/raw_fastq --recursive"
+    "! aws s3 cp raw_fastq/*.fastq s3://sra-data-athena/raw_fastq --recursive"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "072ebc9a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                           PRE SRR3617061/\n",
-      "                           PRE SRR8435252/\n",
-      "                           PRE SRR8435254/\n",
-      "2022-08-30 15:53:41  722868342 SRR3617061_1.fastq\n",
-      "2022-08-30 15:53:41  722868342 SRR3617061_2.fastq\n",
-      "2022-08-30 15:53:42 3903844648 SRR8435252.fastq\n",
-      "2022-08-30 15:53:56 5411343576 SRR8435254.fastq\n"
-     ]
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "! aws s3 ls s3://sra-data-athena/raw_fastq/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d5fda05",
+   "metadata": {},
    "source": [
-    "!aws s3 ls s3://sra-data-athena/raw_fastq/"
+    "## Conclusions \n",
+    "Here you learned how to generate a list of accessions using Athena and then use the SRA toolkit to download FASTQ files. We tested fasterq-dump on its own, but found that using prefetch then fasterq-dump is much faster. Finally you learned how to copy directories to S3 using the `--recursive` flag."
    ]
   },
   {
@@ -588,9 +522,17 @@
    "id": "a4026566",
    "metadata": {},
    "source": [
-    "### Step 8) Clean up\n",
-    "Make sure you shut down this VM, or delete it if you don't plan to use if further. You can also [delete the buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) if you don't want to pay for the data: `aws s3 rb s3://bucket-name --force`"
+    "## Clean up\n",
+    "Make sure you shut down this VM, or delete it if you don't plan to use if further.\n",
+    "\n",
+    "You can also [delete the buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) if you don't want to pay for the data: `aws s3 rb s3://bucket-name --force`"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f3bbfe3",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
@@ -601,9 +543,9 @@
    "uri": "gcr.io/deeplearning-platform-release/base-cpu:m93"
   },
   "kernelspec": {
-   "display_name": "conda_python3",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda_python3"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -615,7 +557,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/SpleenLiverSegmentation/SpleenSeg_Pretrained-4_27.ipynb b/notebooks/SpleenLiverSegmentation/SpleenSeg_Pretrained-4_27.ipynb
index 48b8141..166ebd1 100644
--- a/notebooks/SpleenLiverSegmentation/SpleenSeg_Pretrained-4_27.ipynb
+++ b/notebooks/SpleenLiverSegmentation/SpleenSeg_Pretrained-4_27.ipynb
@@ -5,33 +5,61 @@
    "id": "1452463e",
    "metadata": {},
    "source": [
-    "## Spleen Model With NVIDIA Pretrain\n",
-    "- Uses Unet architecture\n",
-    "- Pretrained model at: https://ngc.nvidia.com/catalog/models/nvidia:med:clara_pt_spleen_ct_segmentation"
+    "# Spleen Segmentation With NVIDIA Pretrained Model\n",
+    "\n",
+    "## Overview\n",
+    "NVIDIA offers pre-trained models that can be downloaded and applied to various tasks. Here we will do Spleen segmentation using a pretrained model with a Unet architecture found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/monaitoolkit/models/monai_spleen_ct_segmentation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "170d7954",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "+ Learn how to interact with NVIDIA pre trained models\n",
+    "+ Learn how to conduct medical image segmentation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb7c2472",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "You only need access to a Sagemaker environment, and make sure you are runnning a GPU. If not, stop your instance and resize to a T4 GPU machine."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "f59ba435",
+   "id": "93aa3269",
    "metadata": {},
    "source": [
-    "##### Uncomment below to install all dependencies"
+    "## Get Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a66cdaf2",
+   "metadata": {},
+   "source": [
+    "### Install packages"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "82db674f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install 'monai[all]'\n",
-    "#!pip install matplotlib "
+    "! pip install 'monai[all]'\n",
+    "! pip install matplotlib "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "bb1228b3",
    "metadata": {},
    "outputs": [],
@@ -41,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "540e5d47",
    "metadata": {},
    "outputs": [],
@@ -65,42 +93,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "07510582",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MONAI version: 0.8.1\n",
-      "Numpy version: 1.21.1\n",
-      "Pytorch version: 1.9.0\n",
-      "MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
-      "MONAI rev id: 71ff399a3ea07aef667b23653620a290364095b1\n",
-      "\n",
-      "Optional dependencies:\n",
-      "Pytorch Ignite version: 0.4.8\n",
-      "Nibabel version: 3.2.1\n",
-      "scikit-image version: 0.18.2\n",
-      "Pillow version: 8.3.1\n",
-      "Tensorboard version: 2.5.0\n",
-      "gdown version: 3.13.0\n",
-      "TorchVision version: 0.10.0+cu111\n",
-      "tqdm version: 4.61.2\n",
-      "lmdb version: 1.2.1\n",
-      "psutil version: 5.8.0\n",
-      "pandas version: 1.3.0\n",
-      "einops version: 0.3.0\n",
-      "transformers version: 4.18.0\n",
-      "mlflow version: 1.25.1\n",
-      "\n",
-      "For details about installing the optional dependencies, please visit:\n",
-      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import tempfile\n",
@@ -157,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "0be7401d",
    "metadata": {},
    "outputs": [],
@@ -175,18 +171,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "311c3282",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "monai_data/\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "directory = \"monai_data/\"\n",
     "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
@@ -203,20 +191,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "da7cfede",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-04-27 14:49:41,401 - INFO - Verified 'Task09_Spleen.tar', md5: 410d4a301da4e5b2f6f86ec3ddba524e.\n",
-      "2022-04-27 14:49:41,402 - INFO - File exists: monai_data/Task09_Spleen.tar, skipped downloading.\n",
-      "2022-04-27 14:49:41,403 - INFO - Non-empty folder exists in monai_data/Task09_Spleen, skipped extracting.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "resource = \"https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar\"\n",
     "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
@@ -236,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "2515b177",
    "metadata": {},
    "outputs": [],
@@ -262,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "2357d35d",
    "metadata": {},
    "outputs": [],
@@ -328,38 +306,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "ada5757a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'image': 'monai_data/Task09_Spleen/imagesTr/spleen_56.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_56.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_59.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_59.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_6.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_6.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_60.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_60.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_61.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_61.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_62.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_62.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_63.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_63.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_8.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_8.nii.gz'},\n",
-       " {'image': 'monai_data/Task09_Spleen/imagesTr/spleen_9.nii.gz',\n",
-       "  'label': 'monai_data/Task09_Spleen/labelsTr/spleen_9.nii.gz'}]"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "val_files"
    ]
@@ -374,30 +324,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "689eea4e",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "image shape: torch.Size([239, 239, 113]), label shape: torch.Size([239, 239, 113])\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "check_ds = Dataset(data=val_files, transform=val_transforms)\n",
     "check_loader = DataLoader(check_ds, batch_size=1)\n",
@@ -427,74 +357,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "fe3285d0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 32/32 [00:00<00:00, 57113.93it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accessing lmdb file: /home/jupyter/covid19det-kaggle/kaggle/MonaiTesting/monai_data/monai_cache.lmdb.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 32/32 [00:00<00:00, 47679.48it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'map_addr': 0, 'map_size': 1099511627776, 'last_pgno': 941102, 'last_txnid': 100, 'max_readers': 126, 'num_readers': 0, 'size': 32, 'filename': '/home/jupyter/covid19det-kaggle/kaggle/MonaiTesting/monai_data/monai_cache.lmdb'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 9/9 [00:00<00:00, 10999.05it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accessing lmdb file: /home/jupyter/covid19det-kaggle/kaggle/MonaiTesting/monai_data/monai_cache.lmdb.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 9/9 [00:00<00:00, 17739.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'map_addr': 0, 'map_size': 1099511627776, 'last_pgno': 941102, 'last_txnid': 100, 'max_readers': 126, 'num_readers': 0, 'size': 9, 'filename': '/home/jupyter/covid19det-kaggle/kaggle/MonaiTesting/monai_data/monai_cache.lmdb'}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "train_ds = LMDBDataset(data=train_files, transform=train_transforms, cache_dir=root_dir)\n",
     "# initialize cache and print meta information\n",
@@ -513,18 +379,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "455cbcdc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'map_addr': 0, 'map_size': 1099511627776, 'last_pgno': 941102, 'last_txnid': 100, 'max_readers': 126, 'num_readers': 0, 'size': 32, 'filename': '/home/jupyter/covid19det-kaggle/kaggle/MonaiTesting/monai_data/monai_cache.lmdb'}\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(train_ds.info())"
    ]
@@ -539,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "8539fb7d",
    "metadata": {},
    "outputs": [],
@@ -558,50 +416,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "de7fb262",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'clara_pt_spleen_ct_segmentation'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mmar['name']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "bf96f9f9",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "using a pretrained model.\n",
-      "2022-04-27 14:49:45,704 - INFO - Expected md5 is None, skip md5 check for file monai_data/clara_pt_spleen_ct_segmentation_2.zip.\n",
-      "2022-04-27 14:49:45,705 - INFO - File exists: monai_data/clara_pt_spleen_ct_segmentation_2.zip, skipped downloading.\n",
-      "2022-04-27 14:49:45,706 - INFO - Non-empty folder exists in monai_data/clara_pt_spleen_ct_segmentation, skipped extracting.\n",
-      "2022-04-27 14:49:45,707 - INFO - \n",
-      "*** \"clara_pt_spleen_ct_segmentation\" available at monai_data/clara_pt_spleen_ct_segmentation.\n",
-      "2022-04-27 14:49:49,353 - INFO - *** Model: <class 'monai.networks.nets.unet.UNet'>\n",
-      "2022-04-27 14:49:49,400 - INFO - *** Model params: {'dimensions': 3, 'in_channels': 1, 'out_channels': 2, 'channels': [16, 32, 64, 128, 256], 'strides': [2, 2, 2, 2], 'num_res_units': 2, 'norm': 'batch'}\n",
-      "2022-04-27 14:49:49,411 - INFO - \n",
-      "---\n",
-      "2022-04-27 14:49:49,412 - INFO - For more information, please visit https://ngc.nvidia.com/catalog/models/nvidia:med:clara_pt_spleen_ct_segmentation\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\") #torch.device(\"cpu\")\n",
     "if PRETRAINED:\n",
@@ -646,32 +474,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "4be7eb8f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 1/1 [00:00<00:00, 4639.72it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accessing lmdb file: /home/jupyter/covid19det-kaggle/kaggle/MonaiTesting/monai_data/monai_cache.lmdb.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "test_file = data_dicts[20:21]\n",
     "test_ds = LMDBDataset(data=test_file, transform=None, cache_dir=root_dir)"
@@ -687,7 +493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "16fd4e94",
    "metadata": {},
    "outputs": [],
@@ -712,33 +518,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "9782ec96",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fda23dde310>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(frameon=False, figsize=(7,7))\n",
     "plt.title('Actual Spleen')\n",
@@ -747,33 +530,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "76cd38e6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb91f78850>"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(frameon=False, figsize=(7,7))\n",
     "plt.title('Pretrained CalculatedSpleen')\n",
@@ -782,33 +542,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "65c68242",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb91f6bd10>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(frameon=False, figsize=(7,7))\n",
     "plt.title('Differences Between Actual and Model')\n",
@@ -837,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "id": "a8ad6aee",
    "metadata": {},
    "outputs": [],
@@ -848,526 +585,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "d91d340c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "----------\n",
-      "epoch 1/25\n",
-      "1/16, train_loss: 0.8680\n",
-      "2/16, train_loss: 0.3699\n",
-      "3/16, train_loss: 0.3849\n",
-      "4/16, train_loss: 0.1306\n",
-      "5/16, train_loss: 0.2781\n",
-      "6/16, train_loss: 0.3628\n",
-      "7/16, train_loss: 0.3609\n",
-      "8/16, train_loss: 0.1828\n",
-      "9/16, train_loss: 0.1493\n",
-      "10/16, train_loss: 0.5063\n",
-      "11/16, train_loss: 0.2929\n",
-      "12/16, train_loss: 0.2826\n",
-      "13/16, train_loss: 0.2017\n",
-      "14/16, train_loss: 0.2591\n",
-      "15/16, train_loss: 0.2568\n",
-      "16/16, train_loss: 0.2385\n",
-      "epoch 1 average loss: 0.3203\n",
-      "----------\n",
-      "epoch 2/25\n",
-      "1/16, train_loss: 0.3457\n",
-      "2/16, train_loss: 0.2234\n",
-      "3/16, train_loss: 0.3443\n",
-      "4/16, train_loss: 0.0816\n",
-      "5/16, train_loss: 0.2259\n",
-      "6/16, train_loss: 0.1580\n",
-      "7/16, train_loss: 0.2593\n",
-      "8/16, train_loss: 0.1651\n",
-      "9/16, train_loss: 0.1124\n",
-      "10/16, train_loss: 0.4822\n",
-      "11/16, train_loss: 0.2900\n",
-      "12/16, train_loss: 0.2571\n",
-      "13/16, train_loss: 0.1799\n",
-      "14/16, train_loss: 0.1984\n",
-      "15/16, train_loss: 0.2286\n",
-      "16/16, train_loss: 0.2216\n",
-      "epoch 2 average loss: 0.2359\n",
-      "saved new best metric model\n",
-      "current epoch: 2 current mean dice: 0.8615\n",
-      "best mean dice: 0.8615 at epoch: 2\n",
-      "----------\n",
-      "epoch 3/25\n",
-      "1/16, train_loss: 0.3400\n",
-      "2/16, train_loss: 0.2297\n",
-      "3/16, train_loss: 0.3453\n",
-      "4/16, train_loss: 0.0822\n",
-      "5/16, train_loss: 0.2285\n",
-      "6/16, train_loss: 0.1213\n",
-      "7/16, train_loss: 0.2370\n",
-      "8/16, train_loss: 0.1607\n",
-      "9/16, train_loss: 0.1065\n",
-      "10/16, train_loss: 0.4543\n",
-      "11/16, train_loss: 0.2848\n",
-      "12/16, train_loss: 0.2848\n",
-      "13/16, train_loss: 0.1763\n",
-      "14/16, train_loss: 0.1748\n",
-      "15/16, train_loss: 0.4361\n",
-      "16/16, train_loss: 0.2234\n",
-      "epoch 3 average loss: 0.2429\n",
-      "----------\n",
-      "epoch 4/25\n",
-      "1/16, train_loss: 0.3328\n",
-      "2/16, train_loss: 0.2447\n",
-      "3/16, train_loss: 0.3436\n",
-      "4/16, train_loss: 0.0723\n",
-      "5/16, train_loss: 0.2213\n",
-      "6/16, train_loss: 0.1676\n",
-      "7/16, train_loss: 0.2672\n",
-      "8/16, train_loss: 0.2121\n",
-      "9/16, train_loss: 0.1122\n",
-      "10/16, train_loss: 0.5265\n",
-      "11/16, train_loss: 0.2810\n",
-      "12/16, train_loss: 0.2688\n",
-      "13/16, train_loss: 0.1795\n",
-      "14/16, train_loss: 0.1853\n",
-      "15/16, train_loss: 0.2458\n",
-      "16/16, train_loss: 0.2314\n",
-      "epoch 4 average loss: 0.2433\n",
-      "saved new best metric model\n",
-      "current epoch: 4 current mean dice: 0.8744\n",
-      "best mean dice: 0.8744 at epoch: 4\n",
-      "----------\n",
-      "epoch 5/25\n",
-      "1/16, train_loss: 0.3378\n",
-      "2/16, train_loss: 0.2047\n",
-      "3/16, train_loss: 0.3350\n",
-      "4/16, train_loss: 0.0583\n",
-      "5/16, train_loss: 0.2161\n",
-      "6/16, train_loss: 0.1008\n",
-      "7/16, train_loss: 0.2325\n",
-      "8/16, train_loss: 0.1629\n",
-      "9/16, train_loss: 0.1037\n",
-      "10/16, train_loss: 0.4499\n",
-      "11/16, train_loss: 0.2763\n",
-      "12/16, train_loss: 0.2321\n",
-      "13/16, train_loss: 0.1702\n",
-      "14/16, train_loss: 0.1652\n",
-      "15/16, train_loss: 0.2206\n",
-      "16/16, train_loss: 0.2169\n",
-      "epoch 5 average loss: 0.2177\n",
-      "----------\n",
-      "epoch 6/25\n",
-      "1/16, train_loss: 0.3303\n",
-      "2/16, train_loss: 0.1888\n",
-      "3/16, train_loss: 0.3331\n",
-      "4/16, train_loss: 0.0535\n",
-      "5/16, train_loss: 0.2149\n",
-      "6/16, train_loss: 0.0962\n",
-      "7/16, train_loss: 0.2267\n",
-      "8/16, train_loss: 0.1555\n",
-      "9/16, train_loss: 0.0995\n",
-      "10/16, train_loss: 0.4476\n",
-      "11/16, train_loss: 0.2751\n",
-      "12/16, train_loss: 0.2215\n",
-      "13/16, train_loss: 0.1644\n",
-      "14/16, train_loss: 0.1603\n",
-      "15/16, train_loss: 0.2159\n",
-      "16/16, train_loss: 0.2141\n",
-      "epoch 6 average loss: 0.2123\n",
-      "saved new best metric model\n",
-      "current epoch: 6 current mean dice: 0.8952\n",
-      "best mean dice: 0.8952 at epoch: 6\n",
-      "----------\n",
-      "epoch 7/25\n",
-      "1/16, train_loss: 0.3286\n",
-      "2/16, train_loss: 0.1815\n",
-      "3/16, train_loss: 0.3317\n",
-      "4/16, train_loss: 0.0487\n",
-      "5/16, train_loss: 0.2127\n",
-      "6/16, train_loss: 0.0926\n",
-      "7/16, train_loss: 0.2236\n",
-      "8/16, train_loss: 0.1536\n",
-      "9/16, train_loss: 0.0955\n",
-      "10/16, train_loss: 0.4468\n",
-      "11/16, train_loss: 0.2730\n",
-      "12/16, train_loss: 0.2171\n",
-      "13/16, train_loss: 0.1616\n",
-      "14/16, train_loss: 0.1565\n",
-      "15/16, train_loss: 0.2147\n",
-      "16/16, train_loss: 0.2123\n",
-      "epoch 7 average loss: 0.2094\n",
-      "----------\n",
-      "epoch 8/25\n",
-      "1/16, train_loss: 0.3276\n",
-      "2/16, train_loss: 0.1800\n",
-      "3/16, train_loss: 0.3311\n",
-      "4/16, train_loss: 0.0459\n",
-      "5/16, train_loss: 0.2114\n",
-      "6/16, train_loss: 0.0853\n",
-      "7/16, train_loss: 0.2206\n",
-      "8/16, train_loss: 0.1529\n",
-      "9/16, train_loss: 0.0939\n",
-      "10/16, train_loss: 0.4467\n",
-      "11/16, train_loss: 0.2725\n",
-      "12/16, train_loss: 0.2171\n",
-      "13/16, train_loss: 0.1600\n",
-      "14/16, train_loss: 0.1502\n",
-      "15/16, train_loss: 0.2140\n",
-      "16/16, train_loss: 0.2115\n",
-      "epoch 8 average loss: 0.2075\n",
-      "saved new best metric model\n",
-      "current epoch: 8 current mean dice: 0.8957\n",
-      "best mean dice: 0.8957 at epoch: 8\n",
-      "----------\n",
-      "epoch 9/25\n",
-      "1/16, train_loss: 0.3275\n",
-      "2/16, train_loss: 0.1822\n",
-      "3/16, train_loss: 0.3309\n",
-      "4/16, train_loss: 0.0455\n",
-      "5/16, train_loss: 0.2110\n",
-      "6/16, train_loss: 0.0818\n",
-      "7/16, train_loss: 0.2194\n",
-      "8/16, train_loss: 0.1520\n",
-      "9/16, train_loss: 0.0917\n",
-      "10/16, train_loss: 0.4467\n",
-      "11/16, train_loss: 0.2723\n",
-      "12/16, train_loss: 0.2165\n",
-      "13/16, train_loss: 0.1593\n",
-      "14/16, train_loss: 0.1236\n",
-      "15/16, train_loss: 0.2136\n",
-      "16/16, train_loss: 0.2107\n",
-      "epoch 9 average loss: 0.2053\n",
-      "----------\n",
-      "epoch 10/25\n",
-      "1/16, train_loss: 0.3271\n",
-      "2/16, train_loss: 0.1726\n",
-      "3/16, train_loss: 0.3308\n",
-      "4/16, train_loss: 0.0439\n",
-      "5/16, train_loss: 0.2106\n",
-      "6/16, train_loss: 0.0886\n",
-      "7/16, train_loss: 0.2209\n",
-      "8/16, train_loss: 0.1518\n",
-      "9/16, train_loss: 0.0860\n",
-      "10/16, train_loss: 0.4452\n",
-      "11/16, train_loss: 0.2715\n",
-      "12/16, train_loss: 0.2150\n",
-      "13/16, train_loss: 0.1589\n",
-      "14/16, train_loss: 0.1150\n",
-      "15/16, train_loss: 0.2142\n",
-      "16/16, train_loss: 0.2095\n",
-      "epoch 10 average loss: 0.2038\n",
-      "saved new best metric model\n",
-      "current epoch: 10 current mean dice: 0.8958\n",
-      "best mean dice: 0.8958 at epoch: 10\n",
-      "----------\n",
-      "epoch 11/25\n",
-      "1/16, train_loss: 0.3271\n",
-      "2/16, train_loss: 0.1735\n",
-      "3/16, train_loss: 0.3314\n",
-      "4/16, train_loss: 0.0430\n",
-      "5/16, train_loss: 0.2099\n",
-      "6/16, train_loss: 0.0801\n",
-      "7/16, train_loss: 0.2201\n",
-      "8/16, train_loss: 0.1508\n",
-      "9/16, train_loss: 0.0721\n",
-      "10/16, train_loss: 0.4451\n",
-      "11/16, train_loss: 0.2714\n",
-      "12/16, train_loss: 0.2155\n",
-      "13/16, train_loss: 0.1592\n",
-      "14/16, train_loss: 0.1247\n",
-      "15/16, train_loss: 0.2139\n",
-      "16/16, train_loss: 0.2107\n",
-      "epoch 11 average loss: 0.2030\n",
-      "----------\n",
-      "epoch 12/25\n",
-      "1/16, train_loss: 0.3268\n",
-      "2/16, train_loss: 0.1712\n",
-      "3/16, train_loss: 0.3305\n",
-      "4/16, train_loss: 0.0453\n",
-      "5/16, train_loss: 0.2103\n",
-      "6/16, train_loss: 0.0783\n",
-      "7/16, train_loss: 0.2179\n",
-      "8/16, train_loss: 0.1529\n",
-      "9/16, train_loss: 0.0912\n",
-      "10/16, train_loss: 0.4469\n",
-      "11/16, train_loss: 0.2724\n",
-      "12/16, train_loss: 0.2162\n",
-      "13/16, train_loss: 0.1588\n",
-      "14/16, train_loss: 0.1072\n",
-      "15/16, train_loss: 0.2129\n",
-      "16/16, train_loss: 0.2091\n",
-      "epoch 12 average loss: 0.2030\n",
-      "saved new best metric model\n",
-      "current epoch: 12 current mean dice: 0.9008\n",
-      "best mean dice: 0.9008 at epoch: 12\n",
-      "----------\n",
-      "epoch 13/25\n",
-      "1/16, train_loss: 0.3266\n",
-      "2/16, train_loss: 0.1666\n",
-      "3/16, train_loss: 0.3304\n",
-      "4/16, train_loss: 0.0419\n",
-      "5/16, train_loss: 0.2105\n",
-      "6/16, train_loss: 0.0826\n",
-      "7/16, train_loss: 0.2195\n",
-      "8/16, train_loss: 0.1506\n",
-      "9/16, train_loss: 0.0553\n",
-      "10/16, train_loss: 0.4447\n",
-      "11/16, train_loss: 0.2715\n",
-      "12/16, train_loss: 0.2125\n",
-      "13/16, train_loss: 0.1575\n",
-      "14/16, train_loss: 0.1083\n",
-      "15/16, train_loss: 0.2135\n",
-      "16/16, train_loss: 0.2085\n",
-      "epoch 13 average loss: 0.2000\n",
-      "----------\n",
-      "epoch 14/25\n",
-      "1/16, train_loss: 0.3270\n",
-      "2/16, train_loss: 0.1647\n",
-      "3/16, train_loss: 0.3316\n",
-      "4/16, train_loss: 0.0405\n",
-      "5/16, train_loss: 0.2091\n",
-      "6/16, train_loss: 0.0686\n",
-      "7/16, train_loss: 0.2185\n",
-      "8/16, train_loss: 0.1499\n",
-      "9/16, train_loss: 0.0482\n",
-      "10/16, train_loss: 0.4443\n",
-      "11/16, train_loss: 0.2708\n",
-      "12/16, train_loss: 0.2106\n",
-      "13/16, train_loss: 0.1568\n",
-      "14/16, train_loss: 0.1043\n",
-      "15/16, train_loss: 0.2121\n",
-      "16/16, train_loss: 0.2079\n",
-      "epoch 14 average loss: 0.1978\n",
-      "saved new best metric model\n",
-      "current epoch: 14 current mean dice: 0.9015\n",
-      "best mean dice: 0.9015 at epoch: 14\n",
-      "----------\n",
-      "epoch 15/25\n",
-      "1/16, train_loss: 0.3259\n",
-      "2/16, train_loss: 0.1630\n",
-      "3/16, train_loss: 0.3303\n",
-      "4/16, train_loss: 0.0399\n",
-      "5/16, train_loss: 0.2085\n",
-      "6/16, train_loss: 0.0579\n",
-      "7/16, train_loss: 0.2165\n",
-      "8/16, train_loss: 0.1509\n",
-      "9/16, train_loss: 0.0487\n",
-      "10/16, train_loss: 0.4449\n",
-      "11/16, train_loss: 0.2704\n",
-      "12/16, train_loss: 0.2090\n",
-      "13/16, train_loss: 0.1557\n",
-      "14/16, train_loss: 0.1021\n",
-      "15/16, train_loss: 0.2118\n",
-      "16/16, train_loss: 0.2084\n",
-      "epoch 15 average loss: 0.1965\n",
-      "----------\n",
-      "epoch 16/25\n",
-      "1/16, train_loss: 0.3258\n",
-      "2/16, train_loss: 0.1620\n",
-      "3/16, train_loss: 0.3307\n",
-      "4/16, train_loss: 0.0394\n",
-      "5/16, train_loss: 0.2086\n",
-      "6/16, train_loss: 0.0699\n",
-      "7/16, train_loss: 0.2170\n",
-      "8/16, train_loss: 0.1516\n",
-      "9/16, train_loss: 0.0540\n",
-      "10/16, train_loss: 0.4444\n",
-      "11/16, train_loss: 0.2698\n",
-      "12/16, train_loss: 0.2102\n",
-      "13/16, train_loss: 0.1548\n",
-      "14/16, train_loss: 0.1016\n",
-      "15/16, train_loss: 0.2114\n",
-      "16/16, train_loss: 0.2078\n",
-      "epoch 16 average loss: 0.1974\n",
-      "current epoch: 16 current mean dice: 0.8994\n",
-      "best mean dice: 0.9015 at epoch: 14\n",
-      "----------\n",
-      "epoch 17/25\n",
-      "1/16, train_loss: 0.3255\n",
-      "2/16, train_loss: 0.1636\n",
-      "3/16, train_loss: 0.3300\n",
-      "4/16, train_loss: 0.0399\n",
-      "5/16, train_loss: 0.2085\n",
-      "6/16, train_loss: 0.0483\n",
-      "7/16, train_loss: 0.2150\n",
-      "8/16, train_loss: 0.1506\n",
-      "9/16, train_loss: 0.0446\n",
-      "10/16, train_loss: 0.4445\n",
-      "11/16, train_loss: 0.2692\n",
-      "12/16, train_loss: 0.2077\n",
-      "13/16, train_loss: 0.1515\n",
-      "14/16, train_loss: 0.0980\n",
-      "15/16, train_loss: 0.2110\n",
-      "16/16, train_loss: 0.2076\n",
-      "epoch 17 average loss: 0.1947\n",
-      "----------\n",
-      "epoch 18/25\n",
-      "1/16, train_loss: 0.3255\n",
-      "2/16, train_loss: 0.1614\n",
-      "3/16, train_loss: 0.3297\n",
-      "4/16, train_loss: 0.0381\n",
-      "5/16, train_loss: 0.2081\n",
-      "6/16, train_loss: 0.0422\n",
-      "7/16, train_loss: 0.2152\n",
-      "8/16, train_loss: 0.1485\n",
-      "9/16, train_loss: 0.0415\n",
-      "10/16, train_loss: 0.4442\n",
-      "11/16, train_loss: 0.2690\n",
-      "12/16, train_loss: 0.2070\n",
-      "13/16, train_loss: 0.1515\n",
-      "14/16, train_loss: 0.0980\n",
-      "15/16, train_loss: 0.2112\n",
-      "16/16, train_loss: 0.2068\n",
-      "epoch 18 average loss: 0.1936\n",
-      "current epoch: 18 current mean dice: 0.8991\n",
-      "best mean dice: 0.9015 at epoch: 14\n",
-      "----------\n",
-      "epoch 19/25\n",
-      "1/16, train_loss: 0.3254\n",
-      "2/16, train_loss: 0.1635\n",
-      "3/16, train_loss: 0.3297\n",
-      "4/16, train_loss: 0.0372\n",
-      "5/16, train_loss: 0.2078\n",
-      "6/16, train_loss: 0.0424\n",
-      "7/16, train_loss: 0.2145\n",
-      "8/16, train_loss: 0.1483\n",
-      "9/16, train_loss: 0.0402\n",
-      "10/16, train_loss: 0.4436\n",
-      "11/16, train_loss: 0.2695\n",
-      "12/16, train_loss: 0.2076\n",
-      "13/16, train_loss: 0.1514\n",
-      "14/16, train_loss: 0.1009\n",
-      "15/16, train_loss: 0.2116\n",
-      "16/16, train_loss: 0.2071\n",
-      "epoch 19 average loss: 0.1938\n",
-      "----------\n",
-      "epoch 20/25\n",
-      "1/16, train_loss: 0.3256\n",
-      "2/16, train_loss: 0.1616\n",
-      "3/16, train_loss: 0.3302\n",
-      "4/16, train_loss: 0.0376\n",
-      "5/16, train_loss: 0.2080\n",
-      "6/16, train_loss: 0.0756\n",
-      "7/16, train_loss: 0.2150\n",
-      "8/16, train_loss: 0.1476\n",
-      "9/16, train_loss: 0.0400\n",
-      "10/16, train_loss: 0.4440\n",
-      "11/16, train_loss: 0.2686\n",
-      "12/16, train_loss: 0.2071\n",
-      "13/16, train_loss: 0.1512\n",
-      "14/16, train_loss: 0.0990\n",
-      "15/16, train_loss: 0.2103\n",
-      "16/16, train_loss: 0.2066\n",
-      "epoch 20 average loss: 0.1955\n",
-      "current epoch: 20 current mean dice: 0.8984\n",
-      "best mean dice: 0.9015 at epoch: 14\n",
-      "----------\n",
-      "epoch 21/25\n",
-      "1/16, train_loss: 0.3253\n",
-      "2/16, train_loss: 0.1599\n",
-      "3/16, train_loss: 0.3295\n",
-      "4/16, train_loss: 0.0370\n",
-      "5/16, train_loss: 0.2074\n",
-      "6/16, train_loss: 0.0587\n",
-      "7/16, train_loss: 0.2138\n",
-      "8/16, train_loss: 0.1483\n",
-      "9/16, train_loss: 0.0479\n",
-      "10/16, train_loss: 0.4449\n",
-      "11/16, train_loss: 0.2684\n",
-      "12/16, train_loss: 0.2082\n",
-      "13/16, train_loss: 0.1520\n",
-      "14/16, train_loss: 0.1122\n",
-      "15/16, train_loss: 0.2110\n",
-      "16/16, train_loss: 0.2088\n",
-      "epoch 21 average loss: 0.1958\n",
-      "----------\n",
-      "epoch 22/25\n",
-      "1/16, train_loss: 0.3258\n",
-      "2/16, train_loss: 0.1628\n",
-      "3/16, train_loss: 0.3298\n",
-      "4/16, train_loss: 0.0395\n",
-      "5/16, train_loss: 0.2082\n",
-      "6/16, train_loss: 0.0614\n",
-      "7/16, train_loss: 0.2181\n",
-      "8/16, train_loss: 0.1566\n",
-      "9/16, train_loss: 0.0650\n",
-      "10/16, train_loss: 0.4442\n",
-      "11/16, train_loss: 0.2693\n",
-      "12/16, train_loss: 0.2118\n",
-      "13/16, train_loss: 0.1532\n",
-      "14/16, train_loss: 0.0998\n",
-      "15/16, train_loss: 0.2121\n",
-      "16/16, train_loss: 0.2076\n",
-      "epoch 22 average loss: 0.1978\n",
-      "saved new best metric model\n",
-      "current epoch: 22 current mean dice: 0.9054\n",
-      "best mean dice: 0.9054 at epoch: 22\n",
-      "----------\n",
-      "epoch 23/25\n",
-      "1/16, train_loss: 0.3266\n",
-      "2/16, train_loss: 0.1723\n",
-      "3/16, train_loss: 0.3315\n",
-      "4/16, train_loss: 0.0413\n",
-      "5/16, train_loss: 0.2091\n",
-      "6/16, train_loss: 0.0807\n",
-      "7/16, train_loss: 0.2143\n",
-      "8/16, train_loss: 0.1514\n",
-      "9/16, train_loss: 0.0432\n",
-      "10/16, train_loss: 0.4441\n",
-      "11/16, train_loss: 0.2704\n",
-      "12/16, train_loss: 0.2081\n",
-      "13/16, train_loss: 0.1532\n",
-      "14/16, train_loss: 0.0983\n",
-      "15/16, train_loss: 0.2106\n",
-      "16/16, train_loss: 0.2072\n",
-      "epoch 23 average loss: 0.1976\n",
-      "----------\n",
-      "epoch 24/25\n",
-      "1/16, train_loss: 0.3257\n",
-      "2/16, train_loss: 0.1711\n",
-      "3/16, train_loss: 0.3307\n",
-      "4/16, train_loss: 0.0376\n",
-      "5/16, train_loss: 0.2077\n",
-      "6/16, train_loss: 0.0705\n",
-      "7/16, train_loss: 0.2141\n",
-      "8/16, train_loss: 0.1482\n",
-      "9/16, train_loss: 0.0392\n",
-      "10/16, train_loss: 0.4439\n",
-      "11/16, train_loss: 0.2688\n",
-      "12/16, train_loss: 0.2070\n",
-      "13/16, train_loss: 0.1512\n",
-      "14/16, train_loss: 0.0969\n",
-      "15/16, train_loss: 0.2098\n",
-      "16/16, train_loss: 0.2062\n",
-      "epoch 24 average loss: 0.1955\n",
-      "saved new best metric model\n",
-      "current epoch: 24 current mean dice: 0.9060\n",
-      "best mean dice: 0.9060 at epoch: 24\n",
-      "----------\n",
-      "epoch 25/25\n",
-      "1/16, train_loss: 0.3251\n",
-      "2/16, train_loss: 0.1621\n",
-      "3/16, train_loss: 0.3298\n",
-      "4/16, train_loss: 0.0367\n",
-      "5/16, train_loss: 0.2075\n",
-      "6/16, train_loss: 0.0430\n",
-      "7/16, train_loss: 0.2132\n",
-      "8/16, train_loss: 0.1490\n",
-      "9/16, train_loss: 0.0390\n",
-      "10/16, train_loss: 0.4432\n",
-      "11/16, train_loss: 0.2699\n",
-      "12/16, train_loss: 0.2080\n",
-      "13/16, train_loss: 0.1520\n",
-      "14/16, train_loss: 0.0959\n",
-      "15/16, train_loss: 0.2101\n",
-      "16/16, train_loss: 0.2057\n",
-      "epoch 25 average loss: 0.1931\n",
-      "train completed, best_metric: 0.9060 at epoch: 24\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "max_epochs = 25\n",
     "val_interval = 2\n",
@@ -1443,23 +664,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "5cf1fd04",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.figure(\"train\", (12, 6))\n",
     "plt.subplot(1, 2, 1)\n",
@@ -1498,21 +706,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "29441405",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<All keys matched successfully>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.load_state_dict(torch.load('monai_data/best_metric_model_pretrained.pth'))"
    ]
@@ -1527,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "94615f38",
    "metadata": {},
    "outputs": [],
@@ -1552,33 +749,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "a3f78dd4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb920452d0>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(frameon=False, figsize=(7,7))\n",
     "plt.title('Trained Calculated Spleen')\n",
@@ -1587,33 +761,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "a67f89f2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb91eaaa10>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(frameon=False, figsize=(7,7))\n",
     "plt.title('Differences Between Actual and Model')\n",
@@ -1623,33 +774,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "382c7285",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb91b77f10>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "fig = plt.figure(frameon=False, figsize=(7,7))\n",
     "plt.title('Differences Between The Models')\n",
@@ -1675,33 +803,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "91e83d40",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fda24302310>"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "maskedspleen = np.ma.masked_where(test_outputsSpl[0].cpu().numpy()[1][:,:,200] == 0, test_outputsSpl[0].cpu().numpy()[1][:,:,200])\n",
     "fig = plt.figure(frameon=False, figsize=(10,10))\n",
@@ -1729,7 +834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "657e44a0",
    "metadata": {},
    "outputs": [],
@@ -1748,28 +853,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "a6fb0da7",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2022-04-27 15:06:54,404 - INFO - Expected md5 is None, skip md5 check for file monai_data/clara_pt_liver_and_tumor_ct_segmentation_1.zip.\n",
-      "2022-04-27 15:06:54,405 - INFO - File exists: monai_data/clara_pt_liver_and_tumor_ct_segmentation_1.zip, skipped downloading.\n",
-      "2022-04-27 15:06:54,425 - INFO - Non-empty folder exists in monai_data/clara_pt_liver_and_tumor_ct_segmentation, skipped extracting.\n",
-      "2022-04-27 15:06:54,426 - INFO - \n",
-      "*** \"clara_pt_liver_and_tumor_ct_segmentation\" available at monai_data/clara_pt_liver_and_tumor_ct_segmentation.\n",
-      "2022-04-27 15:06:54,889 - INFO - *** Model: <class 'monai.networks.nets.unet.UNet'>\n",
-      "2022-04-27 15:06:54,938 - INFO - *** Model params: {'dimensions': 3, 'in_channels': 1, 'out_channels': 3, 'channels': [16, 32, 64, 128, 256], 'strides': [2, 2, 2, 2], 'num_res_units': 2, 'norm': 'batch'}\n",
-      "2022-04-27 15:06:54,950 - INFO - \n",
-      "---\n",
-      "2022-04-27 15:06:54,951 - INFO - For more information, please visit https://ngc.nvidia.com/catalog/models/nvidia:med:clara_pt_liver_and_tumor_ct_segmentation\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "    try: #MONAI=0.8\n",
     "        unet_model = load_from_mmar(\n",
@@ -1789,29 +876,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "id": "55034354",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "using a pretrained model.\n",
-      "2022-04-27 15:06:55,931 - INFO - Expected md5 is None, skip md5 check for file monai_data/clara_pt_liver_and_tumor_ct_segmentation_1.zip.\n",
-      "2022-04-27 15:06:55,931 - INFO - File exists: monai_data/clara_pt_liver_and_tumor_ct_segmentation_1.zip, skipped downloading.\n",
-      "2022-04-27 15:06:55,932 - INFO - Non-empty folder exists in monai_data/clara_pt_liver_and_tumor_ct_segmentation, skipped extracting.\n",
-      "2022-04-27 15:06:55,933 - INFO - \n",
-      "*** \"clara_pt_liver_and_tumor_ct_segmentation\" available at monai_data/clara_pt_liver_and_tumor_ct_segmentation.\n",
-      "2022-04-27 15:06:55,962 - INFO - *** Model: <class 'monai.networks.nets.unet.UNet'>\n",
-      "2022-04-27 15:06:56,010 - INFO - *** Model params: {'dimensions': 3, 'in_channels': 1, 'out_channels': 3, 'channels': [16, 32, 64, 128, 256], 'strides': [2, 2, 2, 2], 'num_res_units': 2, 'norm': 'batch'}\n",
-      "2022-04-27 15:06:56,023 - INFO - \n",
-      "---\n",
-      "2022-04-27 15:06:56,024 - INFO - For more information, please visit https://ngc.nvidia.com/catalog/models/nvidia:med:clara_pt_liver_and_tumor_ct_segmentation\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
     "\n",
@@ -1834,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "id": "a79c1731",
    "metadata": {},
    "outputs": [],
@@ -1860,33 +928,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "id": "c0956706",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb91e2dd10>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sliceval = 215\n",
     "maskedliv = np.ma.masked_where(test_outputsliv[0].cpu().numpy()[1][:,:,sliceval] == 0, test_outputsliv[0].cpu().numpy()[1][:,:,sliceval])\n",
@@ -1900,33 +945,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "id": "5bdfdbe9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fdb91a53d10>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sliceval = 110\n",
     "maskedliv = np.ma.masked_where(test_outputsliv[0].cpu().numpy()[1][:,sliceval,:] == 0, test_outputsliv[0].cpu().numpy()[1][:,sliceval,:])\n",
@@ -1940,36 +962,28 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af1169b6",
+   "id": "51e2dc2b",
    "metadata": {},
    "source": [
-    "#### Continue including more models found at the NGC Catalog: \n",
-    "#### https://catalog.ngc.nvidia.com/models\n",
-    "##### - Recommend filtering by 'CT' "
+    "## Conclusions\n",
+    "Here you learned how to segment medical images using an NVIDIA pretrained model. To further explore NVIDIA models search the [NGC Catalog](https://catalog.ngc.nvidia.com/models) for 'CT'."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0dce4d55",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e17e6228",
+   "cell_type": "markdown",
+   "id": "af3e7ddb",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Clean up\n",
+    "Make sure you shut down this VM, or delete it if you don't plan to use if further.\n",
+    "\n",
+    "You can also [delete the buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) if you don't want to pay for the data: `aws s3 rb s3://bucket-name --force`"
+   ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7034135a",
+   "cell_type": "markdown",
+   "id": "e0b85a97",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],
diff --git a/notebooks/pangolin/pangolin_pipeline.ipynb b/notebooks/pangolin/pangolin_pipeline.ipynb
index 4ce0d5b..85ba27f 100644
--- a/notebooks/pangolin/pangolin_pipeline.ipynb
+++ b/notebooks/pangolin/pangolin_pipeline.ipynb
@@ -10,10 +10,44 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56a29212",
+   "id": "22f95828",
    "metadata": {},
    "source": [
-    "We are going to run a standard covid bioinformatics pipeline using the Pangolin workflow. https://cov-lineages.org/resources/pangolin/usage.html"
+    "## Overview"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25e25f08",
+   "metadata": {},
+   "source": [
+    "We are going to run a standard covid bioinformatics pipeline using the [Pangolin workflow](https://cov-lineages.org/resources/pangolin/usage.html). We will run the whole analysis within this notebook environment."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f67dfae",
+   "metadata": {},
+   "source": [
+    "## Learning Objectives\n",
+    "Learn how to run a simple bioinformatic workflow within a Jupyter notebook environment on AWS."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7a574ce",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "+ You only need access to a Sagemaker environment to run this notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2881a142",
+   "metadata": {},
+   "source": [
+    "## Get Started"
    ]
   },
   {
@@ -21,7 +55,7 @@
    "id": "03541941",
    "metadata": {},
    "source": [
-    "### Required software"
+    "### Install packages and set up environment"
    ]
   },
   {
@@ -33,8 +67,10 @@
    },
    "outputs": [],
    "source": [
-    "#change this depending on how many threads are available in your notebook\n",
-    "CPU=4"
+    "import os\n",
+    "\n",
+    "CPU = os.cpu_count()\n",
+    "print(f\"Number of threads available: {CPU}\")\n"
    ]
   },
   {
@@ -46,10 +82,34 @@
    },
    "outputs": [],
    "source": [
-    "#install biopython to import packages below\n",
+    "# install biopython to import packages below\n",
     "! pip install biopython"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70810fd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install mamba\n",
+    "! curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh\n",
+    "! bash Mambaforge-$(uname)-$(uname -m).sh -b -p $HOME/mambaforge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3379e83c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to your path\n",
+    "import os\n",
+    "os.environ[\"PATH\"] += os.pathsep + os.environ[\"HOME\"]+\"/mambaforge/bin\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -59,6 +119,7 @@
    },
    "outputs": [],
    "source": [
+    "# install everything else\n",
     "! mamba install -y -c conda-forge -c bioconda sra-tools  pangolin iqtree"
    ]
   },
@@ -96,6 +157,7 @@
    "source": [
     "if not os.path.exists('pangolin_analysis'):\n",
     "    os.mkdir('pangolin_analysis')\n",
+    "\n",
     "os.chdir('pangolin_analysis')"
    ]
   },
@@ -110,8 +172,9 @@
    "source": [
     "if os.path.exists('sarscov2_sequences.fasta'):\n",
     "    os.remove('sarscov2_sequences.fasta')\n",
-    "!rm sarscov2_*\n",
-    "!rm lineage_report.csv"
+    "\n",
+    "! rm sarscov2_*\n",
+    "! rm lineage_report.csv"
    ]
   },
   {
@@ -133,6 +196,7 @@
    "source": [
     "#give a list of accession number for sars sequences\n",
     "acc_nums=['NC_045512','LR757995','LR757996','OL698718','OL677199','OL672836','MZ914912','MZ916499','MZ908464','MW580573','MW580574','MW580576','MW991906','MW931310','MW932027','MW424864','MW453109','MW453110']\n",
+    "\n",
     "print('the number of sequences we will analyze = ',len(acc_nums))"
    ]
   },
@@ -153,10 +217,12 @@
    },
    "outputs": [],
    "source": [
-    "#use the bio.entrez toolkit within biopython to download the accession numbers\n",
-    "#save those sequences to a single fasta file\n",
+    "# use the bio.entrez toolkit within biopython to download the accession numbers\n",
+    "# save those sequences to a single fasta file\n",
     "Entrez.email = \"email@example.com\"  # ell NCBI who you are\n",
+    "\n",
     "filename = \"sarscov2_seqs.fasta\"\n",
+    "\n",
     "if not os.path.isfile(filename):\n",
     "    # Downloading...\n",
     "    for acc in acc_nums:\n",
@@ -179,8 +245,10 @@
    },
    "outputs": [],
    "source": [
-    "#make sure our fasta file has the same number of seqs as the acc_nums list\n",
+    "# make sure our fasta file has the same number of seqs as the acc_nums list\n",
+    "\n",
     "print('the number of seqs in our fasta file: ')\n",
+    "\n",
     "! grep '>' sarscov2_seqs.fasta | wc -l"
    ]
   },
@@ -193,7 +261,7 @@
    },
    "outputs": [],
    "source": [
-    "#let's peek at our new fasta file\n",
+    "# let's peek at our new fasta file\n",
     "! head sarscov2_seqs.fasta"
    ]
   },
@@ -205,7 +273,7 @@
    },
    "source": [
     "### Run pangolin to identify lineages and output alignment\n",
-    "Here we call pangolin, give it our input sequences and the number of threads. We also tell it to output the alignment. The full list of pangolin parameters can be found in the [docs](https://cov-lineages.org/resources/pangolin/usage.html)."
+    "Here we call pangolin, give it our input sequences and the number of threads. We also tell it to output the alignment. The full list of pangolin parameters can be found in the pangolin [docs](https://cov-lineages.org/resources/pangolin/usage.html)."
    ]
   },
   {
@@ -246,7 +314,7 @@
    },
    "outputs": [],
    "source": [
-    "#run iqtree with threads = $CPU variable.\n",
+    "# run iqtree with threads = $CPU variable.\n",
     "# if you exclude the -m it will do a phylogenetic model search before tree search\n",
     "! iqtree -s sequences.aln.fasta -nt $CPU -m HKY --prefix sarscov2_tree --redo-tree"
    ]
@@ -256,7 +324,16 @@
    "id": "c7197dd4",
    "metadata": {},
    "source": [
-    "### Download the tree and view in tree viewer like FigTree! "
+    "### Download the tree and view in tree viewer like [FigTree](http://tree.bio.ed.ac.uk/software/figtree/)! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a5b8a1b",
+   "metadata": {},
+   "source": [
+    "### Conclusions\n",
+    "That's it! Now you know how to run a simple workflow using a Sagemaker notebook environment"
    ]
   },
   {
@@ -264,15 +341,16 @@
    "id": "88457512",
    "metadata": {},
    "source": [
-    "And that is all! You now know how to run workflows in notebooks in Cloud Lab"
+    "## Clean up\n",
+    "Make sure you shut down this VM, or delete it if you don't plan to use if further.\n",
+    "\n",
+    "You can also [delete the buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) if you don't want to pay for the data: `aws s3 rb s3://bucket-name --force`"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "05b918cf-e78a-4691-aeaf-8a85c6e6d831",
+   "cell_type": "markdown",
+   "id": "4eb73656",
    "metadata": {},
-   "outputs": [],
    "source": []
   }
  ],
@@ -284,9 +362,9 @@
    "uri": "gcr.io/deeplearning-platform-release/r-cpu.4-1:m87"
   },
   "kernelspec": {
-   "display_name": "conda_python3",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda_python3"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -298,7 +376,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.6"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/rnaseq-myco-tutorial-main/RNAseq_pipeline.ipynb b/notebooks/rnaseq-myco-tutorial-main/RNAseq_pipeline.ipynb
index e7ea5c0..6288040 100644
--- a/notebooks/rnaseq-myco-tutorial-main/RNAseq_pipeline.ipynb
+++ b/notebooks/rnaseq-myco-tutorial-main/RNAseq_pipeline.ipynb
@@ -20,6 +20,10 @@
    "source": [
     "This short tutorial demonstrates how to run an RNA-Seq workflow using a prokaryotic data set. Steps in the workflow include read trimming, read QC, read mapping, and counting mapped reads per gene to quantitate gene expression.\n",
     "\n",
+    "This tutorials uses example sequence data procured from the Sally Molloy labratory at the University of Maine; which investigates the transcriptome changes in prophage infected, versus non-prophage infected M. chelonae bacteria. The respective article can be found [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191103/).\n",
+    "\n",
+    "\n",
+    "\n",
     "![RNA-Seq workflow](images/rnaseq-workflow.png)"
    ]
   },
@@ -27,7 +31,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 1: Setup Environment"
+    "## Learning Objectives\n",
+    "Learn how to run a simple RNAseq analysis in a Sagemaker environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "+ You only need access to a SageMaker environment to run this notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get Started"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set up environment and install packages"
    ]
   },
   {
@@ -62,6 +89,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# install mamba\n",
+    "! curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh\n",
+    "! bash Mambaforge-$(uname)-$(uname -m).sh -b -p $HOME/mambaforge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add to your path\n",
+    "import os\n",
+    "os.environ[\"PATH\"] += os.pathsep + os.environ[\"HOME\"]+\"/mambaforge/bin\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# install everything else\n",
     "! mamba install -c conda-forge -c bioconda -c defaults -y sra-tools  pigz=2.6 pbzip2=1.1 trimmomatic=0.36 fastqc=0.11.9 multiqc=1.10.1 salmon=1.5.1 "
    ]
   },
@@ -78,21 +128,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%bash\n",
-    "mkdir -p data\n",
-    "mkdir -p data/raw_fastq\n",
-    "mkdir -p data/trimmed\n",
-    "mkdir -p data/fastqc\n",
-    "mkdir -p data/aligned\n",
-    "mkdir -p data/reference"
+    "! mkdir -p data data/raw_fastq data/trimmed data/fastqc data/aligned data/reference"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 2: Copy FASTQ Files\n",
-    "In order for this tutorial to run quickly, we will only analyze 50,000 reads from a sample from both sample groups instead of analyzing all the reads from all six samples. These files have been posted on a Google Storage Bucket that we made publicly accessible."
+    "### Copy FASTQ Files\n",
+    "So that this tutorial runs quickly, we will only analyze 50,000 reads from one sample from two treatment groups instead of analyzing all the reads from all six samples. These files are hosted in a public Google storage bucket."
    ]
   },
   {
@@ -112,7 +156,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 3: Copy reference transcriptome files that will be used by Salmon\n",
+    "### Copy reference transcriptome files that will be used by Salmon\n",
     "Salmon is a tool that aligns RNA-Seq reads to a set of transcripts rather than the entire genome."
    ]
   },
@@ -130,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 4: Copy data file for Trimmomatic"
+    "### Copy data file for Trimmomatic"
    ]
   },
   {
@@ -146,7 +190,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 5: Run Trimmomatic\n",
+    "### Run Trimmomatic\n",
     "Trimmomatic will trim off any adapter sequences or low quality sequence it detects in the FASTQ files."
    ]
   },
@@ -165,7 +209,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 6: Run FastQC\n",
+    "### Run FastQC\n",
     "FastQC is an invaluable tool that allows you to evaluate whether there are problems with a set of reads. For example, it will provide a report of whether there is any bias in the sequence composition of the reads."
    ]
   },
@@ -191,8 +235,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 7: Run MultiQC\n",
-    "MultiQC reads in the FastQQ reports and generate a compiled report for all the analyzed FASTQ files."
+    "### Run MultiQC\n",
+    "MultiQC reads in the FastQC reports and generates a compiled report for all the analyzed FASTQ files."
    ]
   },
   {
@@ -217,7 +261,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 8: Index the Transcriptome so that Trimmed Reads Can Be Mapped Using Salmon"
+    "### Index the Transcriptome so that trimmed reads can be mapped using salmon"
    ]
   },
   {
@@ -233,7 +277,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 9: Run Salmon to Map Reads to Transcripts and Quantify Expression Levels\n",
+    "### Run salmon to map reads to transcripts and quantify expression levels\n",
     "Salmon aligns the trimmed reads to the reference transcriptome and generates the read counts per transcript. In this analysis, each gene has a single transcript."
    ]
   },
@@ -252,7 +296,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 10: Report the top 10 most highly expressed genes in the samples"
+    "### Report the top 10 most highly expressed genes in the samples"
    ]
   },
   {
@@ -291,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### STEP 11: Report the expression of a putative acyl-ACP desaturase (BB28_RS16545) that was downregulated in the double lysogen relative to wild-type\n",
+    "### Report the expression of a putative acyl-ACP desaturase (BB28_RS16545) that was downregulated in the double lysogen relative to wild-type\n",
     "A acyl-transferase was reported to be downregulated in the double lysogen as shown in the table of the top 20 upregulated and downregulated genes from the paper describing the study."
    ]
   },
@@ -333,8 +377,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### That's it! "
+    "## Conclusions\n",
+    "Here you worked through a simple RNAseq analysis within a Sagemaker environment. For more RNAseq examples, check out the [NIGMS Sandbox RNAseq module}(https://github.com/NIGMS/RNA-Seq-Differential-Expression-Analysis). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up\n",
+    "Make sure you shut down this VM, or delete it if you don't plan to use if further.\n",
+    "\n",
+    "You can also [delete the buckets](https://docs.aws.amazon.com/AmazonS3/latest/userguide/delete-bucket.html) if you don't want to pay for the data: `aws s3 rb s3://bucket-name --force`"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {
diff --git a/tutorials/README.md b/tutorials/README.md
deleted file mode 100644
index 3be38d5..0000000
--- a/tutorials/README.md
+++ /dev/null
@@ -1,96 +0,0 @@
-# AWS Tutorial Resources
-
----------------------------------
-## Overview of Page Contents
-
-+ [Biomedical Workflows on AWS](#bio)
-+ [Artificial Intelligence](#ai)
-+ [Clinical Informatics](#ci)
-+ [Download SRA Data](#sra)
-+ [GWAS](#gwas)
-+ [Medical Imaging](#im)
-+ [RNAseq](#rna)
-+ [scRNAseq](#sc)
-+ [BLAST](#bl)
-+ [Protein Folding](#af)
-+ [Long Read Sequencing Analysis](#long)
-+ [Drug Discovery](#atom)
-+ [CryoEM](#cryoem)
-+ [Open Data](#open)
-
----------------------------------
-## **Biomedical Workflows on AWS** <a name="bio"></a>
-
-There are a lot of ways to run workflows on AWS. Here we list a few possibilities each of which may work for different research aims. As you walk through the various tutorials below, think about how you could possibly run that workflow more efficiently using one of the other methods listed here. If you are unfamiliar with any of the terms or concepts here, please review the [AWS Jumpstart](https://cloud.nih.gov/resources/cloudlab/aws-jumpstart/) page. 
-
-- The most simple is probably to spin up an EC2 instance, and run your command interactively, or using `screen` or, as a [startup script](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html) attached as metadata. See the [GWAS tutorial](https://training.nih-cfde.org/en/latest/Bioinformatic-Analyses/GWAS-in-the-cloud) below for more info on how to run a pipeline using EC2. 
-- You could also run your pipeline via a SageMaker notebook, either by splitting out each command as a different block, or by running a workflow manager (Nextflow etc.). See [here](https://aws.amazon.com/blogs/machine-learning/scheduling-jupyter-notebooks-on-sagemaker-ephemeral-instances/) about scheduling a notebook to let it run longer. You can find some example notebooks in the [tutorials below](/notebooks/).
-- If you are running bioinformatic workflows, you can leverage the serverless functionality of AWS using [Amazon HealthOmics](https://aws.amazon.com/healthomics/). Read [this blog](https://aws.amazon.com/blogs/industries/automated-end-to-end-genomics-data-storage-and-analysis-using-amazon-omics/) for more detailed information and also see if any new blogs have come out. If you want to get some hands on experience with HealthOmics using Cloud Lab, follow [this on-demand workshop](https://catalog.workshops.aws/amazon-omics-end-to-end/en-US/001-getting-started/010-self-directed-workshop) from Amazon! Since you already have an account set up, skip directly to the _Workshop_ section and then you can decide if you want to complete the tutorial via the console, the CLI, or via Notebooks. If you go the notebook route, just spin up a notebook via [Sagemaker](/docs/Jupyter_notebook.md). If you want to create a private workflow using Nextflow, you will need to migrate your containers to a private Amazon Elastic Container Registry (ECR). You can follow [this workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/76d4a4ff-fe6f-436a-a1c2-f7ce44bc5d17/en-US) to learn how that process works.
--  If you are using a workflow manager other than WDL, Nextflow, or CWL (e. g. Snakemake), use [AWS Genomics CLI](https://aws.amazon.com/genomics-cli/), which is a wrapper for genomics workflow managers and AWS Batch (serverless computing cluster). See our [docs](/docs/agc.md) on how to set up the AGC CLI for Cloud Lab. You can also just run Snakemake locally within a VM. See our [Pangolin tutorial](/notebooks/pangolin) for one example. 
-- Finally, one benefit of the cloud is access to GPUs for workflow acceleration. While a lot of focus on GPU implementation will focus on AI/ML workflows, NVIDIA has software called Parabricks that will accelerate genomic workflows for pretty low costs. See the full list of command line options [here](https://docs.nvidia.com/clara/parabricks/3.7.0/index.html) to see if your specific workflow is accelerated. The easiest way to run Parabricks right now is via AWS HealthOmics [Ready2Run workflows](https://docs.aws.amazon.com/omics/latest/dev/service-workflows.html), but to run it via EC2 see our [guide](/docs/parabricks.md).
-
-**For many of these tutorials, you will need Short Term Access Keys to create and use resources, particularly whenever a tutorial calls for "access key ID" and "secret key." Use [this guide](/docs/Intramural_STAKs.md) for an explanation of how to obtain and use Short Term Access Keys. If you are an NIH-affiliated researcher, in other words, you don't work at the NIH but have a Cloud Lab account, you will not have access to keys. If there is a tutorial you are unable to complete, reach out to us for help at CloudLab@nih.gov**
-
- **Please also note, GPU machines cost more than most CPU machines, so be sure to shut these machines down after use, or apply an EC2 [lifecycle configuration](/docs/auto-shutdown-instance.md). You may also encounter service quotas to protect you from the accidental use of expensive machine types. If that happens, and you still want to use a certain instance type, follow these [instructions](/docs/service_quotas.md).**
-
-## **Artificial Intelligence** <a name="ai"></a>
-Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. Artificial intelligence and machine learning algorithms are being applied to a variety of biomedical research questions, ranging from image classification to genomic variant calling. AWS has a long list of AI/ML tutorials available and we have compiled a list here. Most recent development focuses on generative AI including use cases such as extracting information from text, transforming speech to text, and generating images from text. Sagemaker Studio allows the user to rapidly create, test, and train generative AI models and has ready to use models all contained with [JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html). These models range from foundation models, fine-tunable models, and task-specific solutions. 
-+ For examples of generative AI, view our [GenAI tutorials](/notebooks/GenAI) that use several AWS products such as [Bedrock](/notebooks/GenAI/AWS_Bedrock_Intro.ipynb) and [Jumpstart](/notebooks/GenAI/AWS_GenAI_Jumpstart.ipynb) and utilizes other tools like [Langchain](/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb) and [Huggingface](/notebooks/GenAI/AWS_GenAI_Huggingface.ipynb) to deploy, train, prompt, and implement techniques like [Retrieval-Augmented Generation (RAG)](/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb) to GenAI models. Also take a look at the [AWS GitHub repo](https://github.com/aws-samples/amazon-sagemaker-generativeai) for more Gen AI tutorials.
-+ For other AI use cases, we recommend you start with this comprehensive [on-demand workshop](https://catalog.workshops.aws/hcls-aiml/en-US/breast-cancer-classification) on how to use SageMaker Studio for a variety of AI/ML use cases including applying a classifier to RNAseq data, classifying tabular breast cancer data, buiding graph neural nets on HIV data, training a medical imaging model on chest scans, summarize scientific literature using foundation models, MLOps using gene expression data, and finally, performing antibody structure prediction.
-+ To learn more about Bedrock check out this [on-demand workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/a4bdb007-5600-4368-81c5-ff5b4154f518/en-US) featuring uses cases for prompt engineering, summarization, Q/A, chatbot, and image, code, and text generation within Bedrock.
-+ AWS has a very general tutorial [here](https://aws.amazon.com/getting-started/hands-on/build-train-deploy-machine-learning-model-sagemaker/) on how to build out an AI pipeline on SageMaker.
-+ These [general examples](https://github.com/aws/amazon-sagemaker-examples/tree/main/introduction_to_applying_machine_learning) will teach you how to use Sagemaker tools more broadly.
-+ You can also submit a training job to SageMaker, and have your final model uploaded to S3 using [PyTorch](https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#train-a-model-with-pytorch), [Tensorflow](https://docs.aws.amazon.com/sagemaker/latest/dg/tf.html) or [Apache MXNet](https://docs.aws.amazon.com/sagemaker/latest/dg/mxnet.html).
-
-## **Clinical Informatics** <a name="ci"></a>
-Clinical informatics, also known as healthcare informatics or medical informatics, is an interdisciplinary field that applies data science to healthcare data to improve patient care, enhance clinical processes, and facilitate medical research. It often involves integrating diverse data types including electronic health records, demographic, or environmental data. AWS offers two on demand workshops that walk you through AWS HealthLake for Population Health data analysis. [This first workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/4849824d-084a-4a64-a237-f05027f54abc/en-US) shows you how to ingest data to HealthLake, query those data using Athena, visualize these data using QuickSight, then join FHIR data with environmental data and visualize the combined dataset. [The second workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/498fdbc5-46e1-4cb0-97a0-f4ec3a30f26a/en-US/activity-streaming-data) also ingests data into HealthLake, then visualizes medical device data, uses AI to summarize clinical notes, and then transcribes clinical audio files and summarizes them. 
-
-## **Download Data From the Sequence Read Archive (SRA)** <a name="sra"></a>
-Next Generation genetic sequence data is housed in the NCBI Sequence Read Archive (SRA). You can access these data using the SRA Toolkit. We walk you through this using [this notebook](/notebooks/SRADownload), which also walks you through how to set up and search Athena tables to generate an accession list. You can also read [this guide](https://www.ncbi.nlm.nih.gov/sra/docs/sra-aws-download/) for more information on available dataset tables. Additional example notebooks can be found at this [NCBI repo](https://github.com/ncbi/ASHG-Workshop-2021). In particular, we recommend this notebook(https://github.com/ncbi/ASHG-Workshop-2021/blob/main/3_Biology_Example_AWS_Demo.ipynb), which goes into more detail on using Athena to access the results of the SRA Taxonomic Analysis Tool, which often differ from the user input species name due to contamination, error, or due to samples being metagenomic in nature.
-
-## **Genome Wide Association Studies** <a name="gwas"></a>
-Genome-wide association studies (GWAS) are large-scale investigations that analyze the genomes of many individuals to identify common genetic variants associated with traits, diseases, or other phenotypes.
-- This [NIH CFDE written tutorial](https://training.nih-cfde.org/en/latest/Bioinformatic-Analyses/GWAS-in-the-cloud
-) walks you through running a simple GWAS using EC2. The tutorials asks you to select the Ohio region, make sure you change your region to N. Virginia otherwise you will have network issues. Note that the CFDE page has a few other bioinformatics related tutorials like BLAST and Illumina read simulation. We also converted the GWAS tutorial to a simplified [notebook version](/notebooks/GWAS) if you prefer that format. See our [notebook guide](/docs/Jupyter_notebook.md) for help with setting up a Jupyter environment.
-
-## **Medical Imaging Analysis** <a name="im"></a>
-Medical imaging analysis requires the analysis of large image files and often requires elastic storage and accelerated computing.
-- Most medical imaging analyses are done using notebooks, so we would recommend accessing this [Jupyter Notebook](/notebooks/SpleenLiverSegmentation) and cloning it into SageMaker. The tutorial walks through image segmentation.
-- [This Sagemaker Studio on-demand workshop](https://catalog.workshops.aws/hcls-aiml/en-US/chest-xrays-object-detection) has a nice section on building a model on medical imaging data. 
-- You can also view this [AWS blog](https://aws.amazon.com/blogs/machine-learning/annotate-dicom-images-and-build-an-ml-model-using-the-monai-framework-on-amazon-sagemaker/) on how to annotate DICOM images and build a custom AI model with the data.
-- You can learn to deidentify medical images following this AWS [tutorial](https://aws.amazon.com/blogs/machine-learning/de-identify-medical-images-with-the-help-of-amazon-comprehend-medical-and-amazon-rekognition/).
-
-## **RNAseq** <a name="rna"></a>
-RNA-seq analysis is a high-throughput sequencing method that allows the measurement and characterization of gene expression levels and transcriptome dynamics. Workflows are typically run using workflow managers, and final results can often be visualized in notebooks.
-- You can run this [Nextflow tutorial](https://nf-co.re/rnaseq/3.7) for RNAseq a variety of ways on AWS. Following the instructions outlined above, you could use EC2, SageMaker, or AWS Batch(/docs/Genomics_Workflows.md).
-- [This AWS on-demand workshop](https://catalog.workshops.aws/hcls-aiml/en-US/breast-cancer-classification) shows how to analyze gene expression data using Amazon Sagemaker Studio. 
-- For a notebook version of a complete RNAseq pipeline from Fastq to Salmon quantification from the King Lab of the University of Maine INBRE use this [notebook](/notebooks/rnaseq-myco-tutorial-main), which we re-wrote to work on AWS.
-
-## **Single Cell RNAseq** <a name="sc"></a>
-Single-cell RNA sequencing (scRNA-seq) is a technique that enables the analysis of gene expression at the individual cell level, providing insights into cellular heterogeneity, identifying rare cell types, and revealing cellular dynamics and functional states within complex biological systems.
-- This [AWS blog](https://aws.amazon.com/blogs/publicsector/driving-innovation-single-cell-analysis-aws/) lays out a potential method that integrates a lot of the AWS native tools for running an scRNAseq pipeline. It is less of a tutorial, and more of a demo of what is possible.
--  This [NVIDIA blog](https://developer.nvidia.com/blog/accelerating-single-cell-genomic-analysis-using-rapids/) details how to run an accelerated scRNAseq pipeline using RAPIDS. You can find a link to the GitHub that has lots of example notebooks [here](https://github.com/clara-parabricks/rapids-single-cell-examples). For each example use case they show some nice benchmarking data with time and cost for each machine type. You will see that most runs cost less than $1.00 with GPU machines. If you want a CPU version that users Scanpy you can use this [notebook](https://github.com/clara-parabricks/rapids-single-cell-examples/blob/master/notebooks/hlca_lung_cpu_analysis.ipynb). Pay careful attention to the environment setup as there are a lot of dependencies for these notebooks. Create a conda environment in the terminal, then run the notebook. Consider using [mamba](https://github.com/mamba-org/mamba) to speed up environment creation. We created a [guide](/docs/create_conda_env.md) for conda environment set up as well.
-
-## **ElasticBLAST** <a name="bl"></a>
-NCBI BLAST (Basic Local Alignment Search Tool) is a widely used bioinformatics program provided by the National Center for Biotechnology Information (NCBI) that compares nucleotide or protein sequences against a large database to identify similar sequences and infer evolutionary relationships, functional annotations, and structural information. The NCBI team has written a version of BLAST for the cloud called ElasticBLAST, and you can read all about it [here](https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/index.html). Essentially, ElasticBLAST helps you submit BLAST jobs to AWS Batch and write the results back to S3. Feel free to experiment with the example tutorial in Cloud Shell, or try our [notebook version](/notebooks/ElasticBLAST/run_elastic_blast.ipynb).
-
-## **Protein Folding** <a name="af"></a>
-You can run several protein folding algorithms including Alpha Fold on AWS. Because the databases are so large, the setup is normally pretty difficult, but AWS has created a StackFormation stack that automates spinning up all the resources necessary for running Alpha Fold and other protein folding algorithms. You can read about the AWS resources [here](https://aws.amazon.com/solutions/guidance/protein-folding-on-aws/), and view the GitHub page [here](https://github.com/aws-solutions-library-samples/aws-batch-arch-for-protein-folding). To get this to work, you will need to modify your security groups following [these instructions](https://docs.aws.amazon.com/fsx/latest/LustreGuide/limit-access-security-groups.html). You will also likely have to [grant additional permissions to the Role](/docs/update_sagemaker_role.md) that CloudFormation is using. If you get stuck, reach out to CloudLab@nih.gov. You can also run ESMFold [using this tutorial](https://catalog.workshops.aws/hcls-aiml/en-US/protein-analysis/esmfold).
-
-## **Long Read Sequence Analysis** <a name="long"></a>
-Long read DNA sequence analysis involves analyzing sequencing reads typically longer than 10 thousand base pairs (bp) in length, compared with short read sequencing where reads are about 150 bp in length.
-Oxford Nanopore has a pretty complete offering of notebook tutorials for handling long read data to do a variety of things including variant calling, RNAseq, Sars-Cov-2 analysis and much more. Access the notebooks [here](https://labs.epi2me.io/nbindex/). These notebooks expect you are running locally and accessing the epi2me notebook server. To run them in Cloud Lab, skip the first cell that connects to the server and then the rest of the notebook should run correctly, with a few tweaks. If you are just looking to try out notebooks, don't start with these. If you are interested in long read sequence analysis, then some troubleshooting may be needed to adapt these to the Cloud Lab environment. You may even need to rewrite them in a fresh notebook by adapting the commands. Feel free to reach out to our support team for help.
-
-## **Drug Discovery** <a name="atom"></a>
-The [Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium](https://atomscience.org/) created a series of [Jupyter notebooks](https://github.com/ATOMScience-org/AMPL/tree/master/atomsci/ddm/examples/tutorials) that walk you through the ATOM approach to Drug Discovery. 
-
-These notebooks were created to run in Google Colab, so if you run them in AWS, you will need to make a few modification. First, we recommend you use a [Sagemaker Studio Notebook](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated.html) rather than a User-Managed notebook simply because it will have Tensorflow and other dependencies installed. Be sure to attach a GPU to your instance (T4 is fine). Also, you will need to comment out `%tensorflow_version 2.x` since that is a Colab-specific command. You will also need to `pip install` a few packages as needed. If you get errors with `deepchem`, try running `pip install --pre deepchem[tensorflow]` and/or `pip install --pre deepchem[torch]`. Also, some notebooks will require a Tensorflow kernel, while others require Pytorch. You may also run into a Pandas error, reach out to the ATOM GitHub developers for the best solution, or review their issues.
-
-## **CryoEM** <a name="cryoem"></a>
-Cryo-Electron Microscopy (cryoEM), is a powerful imaging technique used in structural biology to visualize the structures of biological macromolecules, such as proteins, nucleic acids, and large molecular complexes, at near-atomic or even atomic resolution. It has revolutionized the field of structural biology by providing detailed three-dimensional structures of biomolecules, which is crucial for understanding their functions.
-
-+ AWS created a [hands-on workshop](https://catalog.us-east-1.prod.workshops.aws/workshops/056181d4-c084-47be-954c-20ec22350b02/en-US) for you to stand up a cryoEM environment using RELION.
-+ You can also read [this blog on how to set up cryoSPARC](https://aws.amazon.com/blogs/hpc/how-thermo-fisher-scientific-accelerated-cryo-em-using-aws-parallelcluster/), as well as [docs from cryoSPARC](https://guide.cryosparc.com/setup-configuration-and-management/cryosparc-on-aws). 
-
-## **Open Data** <a name="open"></a>
-AWS has a lot of public data that you can integrate into your testing or use in your own research. You can access these datasets at the [Registry of Open Data on AWS](https://registry.opendata.aws/). There you can click on any of the datasets to view the S3 path to the data, as well as publications that have used those data and tutorials if available. To demonstrate, we can click the [gnomad dataset](https://registry.opendata.aws/broad-gnomad/), then get the S3 path and view the files at the command line by pasting `https://registry.opendata.aws/broad-gnomad/`. 
diff --git a/tutorials/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb b/tutorials/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb
deleted file mode 100644
index 09aa19a..0000000
--- a/tutorials/notebooks/GenAI/Pubmed_RAG_chatbot.ipynb
+++ /dev/null
@@ -1,868 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "2edc6187-82ae-44e2-852f-2ad2712c93aa",
-   "metadata": {},
-   "source": [
-    "# Creating a PubMed Chatbot with Llama2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ecea2ad-7c65-4367-87e1-b021167c3a1d",
-   "metadata": {},
-   "source": [
-    "For this tutorial we are creating a PubMed chatbot that will answer questions by gathering information from documents we have provided via a index. The model we will be using today is a pretrained Llama2 model from Jumpstart.\n",
-    "\n",
-    "This tutorial will go over the following topics:\n",
-    "- Introduce langchain\n",
-    "- Explain the differences between zero-shot, one-shot, and few-shot prompting\n",
-    "- Practice using different document retrievers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4d01e74b-b5b4-4be9-b16e-ec55419318ef",
-   "metadata": {},
-   "source": [
-    "### Deploy the Model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9dbd13e7-afc9-416b-94dc-418a93e14587",
-   "metadata": {},
-   "source": [
-    "Identify which model we want to deploy from Jumpstart, in this case we are using Llama2."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "6b51bf71-d2e5-4afc-8569-338767b43b9c",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "(\n",
-    "    model_id,\n",
-    "    model_version,\n",
-    ") = (\n",
-    "    \"meta-textgeneration-llama-2-7b-f\",\n",
-    "    \"*\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "624c9bb8-3ce2-4240-b2b8-6b1bb93bb9f2",
-   "metadata": {},
-   "source": [
-    "Create an endpoint so that we can communicate with our model, send inputs, and retrieve outputs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "bf27747d-443f-47e7-9d2c-a8f5c5c6f3b8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
-      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
-      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "For forward compatibility, pin to model_version='2.*' in your JumpStartModel or JumpStartEstimator definitions. Note that major version upgrades may have different EULA acceptance terms and input/output signatures.\n",
-      "For forward compatibility, pin to model_version='2.*' in your JumpStartModel or JumpStartEstimator definitions. Note that major version upgrades may have different EULA acceptance terms and input/output signatures.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-----------------!"
-     ]
-    }
-   ],
-   "source": [
-    "from sagemaker.jumpstart.model import JumpStartModel\n",
-    "\n",
-    "model = JumpStartModel(model_id=model_id)\n",
-    "predictor = model.deploy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "163fabcb-a267-4279-a5ae-c93d0143c139",
-   "metadata": {},
-   "source": [
-    "Next we will print the endpoint name which we will need later to run our chatbot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "1ad71f0d-3be5-4b03-9c1c-eb4585721fc8",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "endpoint_id=predictor.endpoint_name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f3e3ab1-5f7e-4028-a66f-9619926a2afd",
-   "metadata": {},
-   "source": [
-    "### PubMed API vs Kendra Index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a820eea-1538-4f40-86c4-eb14fe09e127",
-   "metadata": {},
-   "source": [
-    "Our chatbot will rely on documents to answer our questions to do so we are supplying it a **vector index**. A vector index or index is a data structure that enables fast and accurate search and retrieval of vector embeddings from a large dataset of objects. We will be working with two options for our index PubMed API vs Kendra Index."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7314b115-9433-460d-b275-78aa50f0a858",
-   "metadata": {},
-   "source": [
-    "**What is the difference?**\n",
-    "\n",
-    "The **PubMed API** is provided free by langchain to connect your model to more than **35 million citations** for biomedical literature from MEDLINE, life science journals, and online books. **Kendra index** is a AWS product that allows the user more **security and control** on which documents you wish to supply to your model. \n",
-    "\n",
-    "We will be exploring both methods to see which produces the best results!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bcf1690d-e93d-4cd3-89c6-8d06b5a071a8",
-   "metadata": {},
-   "source": [
-    "#### Setting up a Kendra Index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "02053f4d-fad7-44ab-a7c3-cfa1c218240f",
-   "metadata": {},
-   "source": [
-    "If you choose to use a Kendra index to supply documents to your model follow the instructions below:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d1c9de7-4a06-4f85-b9ff-c8c9e51f8c70",
-   "metadata": {},
-   "source": [
-    "AWS marketplace has PubMed database named **PubMed Central® (PMC)** that contains free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). We will be subsetting this database to add documents to our Kendra index. Ensure that you have the correct roles and policies to allow your environment to connect to S3 buckets, SageMaker, and Kendra."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "78418da3-f806-4b19-9980-1fa41083a75a",
-   "metadata": {},
-   "source": [
-    "The first step will be to create a bucket that we will later use as our data source for our index."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "99d49432-cf03-4f19-aa82-ef7f8bad5bde",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#make bucket\n",
-    "bucket = 'pubmed-chat-docs'\n",
-    "!aws s3 mb s3://{bucket}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6ad30ba-cee8-47f9-bc1e-ece8961ac66a",
-   "metadata": {},
-   "source": [
-    "We will then download the metadata file from the PMC bucket, this will list all of the articles within the PMC bucket and their bucket paths we will use this to subset the database into our own bucket."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7b395e34-062d-4f77-afee-3601d471954a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#download the metadata file\n",
-    "!aws s3 cp  s3://pmc-oa-opendata/oa_comm/txt/metadata/txt/oa_comm.filelist.txt . --sse"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93a8595a-767f-4cad-9273-62d8e2cf60d1",
-   "metadata": {},
-   "source": [
-    "We only want the metadata of the first 100 files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "c26b0f29-2b07-43a6-800d-4aa5e957fe52",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/ec2-user/anaconda3/envs/python3/lib/python3.10/site-packages/pandas/core/computation/expressions.py:21: UserWarning: Pandas requires version '2.8.0' or newer of 'numexpr' (version '2.7.3' currently installed).\n",
-      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n"
-     ]
-    }
-   ],
-   "source": [
-    "#import the file as a dataframe\n",
-    "import pandas as pd\n",
-    "import os\n",
-    "df = pd.read_csv('oa_comm.filelist.csv')\n",
-    "#first 100 files\n",
-    "first_100=df[0:101]\n",
-    "#save new metadata\n",
-    "first_100.to_csv('oa_comm.filelist_100.csv', index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "abd1ae93-450e-4c79-83cc-ea46a1b507c1",
-   "metadata": {},
-   "source": [
-    "Lets look at our metadata! We can see that the bucket path to the files are under the **Key** column this is what we will use to loop through the PMC bucket and copy the first 100 files to our bucket."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ff77b2aa-ed1b-4d27-8163-fdaa7a304582",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first_100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d63a7e2-dbf1-49ec-bc84-b8c2c8bde62d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "#gather path to files in bucket\n",
-    "for i in first_100['Key']:\n",
-    "    os.system(f'aws s3 cp s3://pmc-oa-opendata/{i} s3://{bucket}/docs/ --sse')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c1b396c8-baa9-44d6-948c-2326dc514839",
-   "metadata": {},
-   "source": [
-    "We will also save our new metadata file to our bucket to help Kendra index our files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79e7076a-65c6-4e06-b84b-795ee7d4de00",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!aws s3 cp oa_comm.filelist_100.csv s3://{bucket}/docs/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "79fb7fe8-4904-4b19-a411-95bca27ea87d",
-   "metadata": {},
-   "source": [
-    "Now we can create our Kendra index, use the following instructions to create a index via the console or command line [Creating a Kendra index](https://docs.aws.amazon.com/kendra/latest/dg/create-index.html). To connect our bucket as a data source follow the instructions provided [here](https://docs.aws.amazon.com/kendra/latest/dg/data-source-s3.html) to do so via the console or AWS Python SDK."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07b3bc6b-8c43-476f-a662-abda830dc2da",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "### Creating a Inference Script "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ba2291e-109e-4120-ad10-5dbfd341a07b",
-   "metadata": {},
-   "source": [
-    "Inorder for us to fluidly send input and receive outputs from our chatbot we need to create a **inference script** that will format inputs in a way that the chatbot can understand and format outputs in a way we can understand. We will also be supplying instructions to the chatbot through the script.\n",
-    "\n",
-    "Our script will utilize **langchain** tools and packages to enable our model to:\n",
-    "- **Connect to sources of context** (e.g. providing our model with tasks and examples)\n",
-    "- **Rely on reason** (e.g. instruct our model how to answer based on provided context)\n",
-    "\n",
-    "**Warning**: The over all inference script must be run on the terminal via the command `python YOUR_SCRIPT.py`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "538f42ee-a502-4e56-9e85-4f6e3726ef7a",
-   "metadata": {},
-   "source": [
-    "**Warning:** The following tools must be installed via your terminal"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "58582571-eae0-4440-afda-be1af6750e11",
-   "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    },
-    "tags": []
-   },
-   "source": [
-    "`pip install \"langchain\" \"xmltodict\"`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ad374085-c4b1-4083-85a5-90cba35846d6",
-   "metadata": {},
-   "source": [
-    "The first part of our script will be to list all the tools that are required. \n",
-    "-  **PubMedRetriever:** Utilizes the langchain retriever tool to specifically retrieve PubMed documents from the PubMed API.\n",
-    "- **AmazonKendraRetriever:** Utilizes the langchain retriever tool to specifically retrieve documents stored in your Kendra index.\n",
-    "- **ConversationalRetrievalChain:** Allows the user to construct a conversation with the model and retrieves the outputs while sending inputs to the model.\n",
-    "- **PromptTemplate:** Allows the user to prompt the model to provide instructions, best method for zero and few shot prompting\n",
-    "- **LLMContentHandler:** Handles the content to and from the model by transforming the input to a format that model can accept and transforms the output from the model to string that the LLM class expects.\n",
-    "- **SagemakerEndpoint**: Connects to our endpoint in SageMaker and allows all of the tools listed above to connect to our model.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f0ad48d-c6c8-421a-a48b-88e979d15b57",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```python\n",
-    "from langchain.retrievers import PubMedRetriever\n",
-    "from langchain.retrievers import AmazonKendraRetriever\n",
-    "from langchain.llms import SagemakerEndpoint\n",
-    "from langchain.chains import ConversationalRetrievalChain\n",
-    "from langchain.prompts import PromptTemplate\n",
-    "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n",
-    "import sys\n",
-    "import json\n",
-    "import os\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "900f4c31-71cd-4f39-8bfc-de098bdbaafc",
-   "metadata": {},
-   "source": [
-    "Second will build a class that will hold the functions we need to send inputs and retrieve outputs from our model. For the beginning of our class we will establish some colors to our text conversation with our chatbot which we will utilize later."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "decbb901-f811-4b8e-a956-4c8c7f914ae2",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```python\n",
-    "class bcolors:\n",
-    "    HEADER = '\\033[95m'\n",
-    "    OKBLUE = '\\033[94m'\n",
-    "    OKCYAN = '\\033[96m'\n",
-    "    OKGREEN = '\\033[92m'\n",
-    "    WARNING = '\\033[93m'\n",
-    "    FAIL = '\\033[91m'\n",
-    "    ENDC = '\\033[0m'\n",
-    "    BOLD = '\\033[1m'\n",
-    "    UNDERLINE = '\\033[4m'\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba36d057-5189-4075-a243-18996c6fc932",
-   "metadata": {},
-   "source": [
-    "Next is to create a function that will gather the necessary information to connect to our model, which will be the:\n",
-    "- Location\n",
-    "- Kendra Index ID **(only if you are using Kendra instead of the PubMed API)**\n",
-    "- Endpoint_name or ID"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f7a244a-7e71-40d3-ae78-8e166dd3c7ee",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "def build_chain():\n",
-    "  region = os.environ[\"AWS_REGION\"]\n",
-    "  kendra_index_id = os.environ[\"KENDRA_INDEX_ID\"] #only needed is using a Kendra index instead of Pubmed API\n",
-    "  endpoint_name = os.environ[\"LLAMA_2_ENDPOINT\"]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4e681908-df7a-4cfd-924d-5350eb06e770",
-   "metadata": {},
-   "source": [
-    "Next we will create a class named **'ContentHandeler'** that will transforms our inputs and outputs into a json format. For Llama2 to understand and accept our inputs we need to structure in a specific manner, this is done by the **transform_input** function:\n",
-    "```\n",
-    "{\n",
-    "\"inputs\": \n",
-    "    [[\n",
-    "        {\"role\": \"user\", \"content\": prompt},\n",
-    "    ]],\n",
-    "    **model_kwargs\n",
-    "                                  }\n",
-    "```\n",
-    "Where `prompt` will be our instructions to our model (what the model is expected to do with our input) and `**model_kwargs` is where we provide our parameters.\n",
-    "\n",
-    "Our input is then encoded in a **UTF-8** format to convert our string into 0s and 1s. \n",
-    "\n",
-    "The next function in our class is named **transform_output**, this function will take the outputs sent from our model and decode them from 0s and 1s to strings."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d63f9b4c-202a-4170-9988-369bbe52fe6b",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```python\n",
-    "class ContentHandler(LLMContentHandler):\n",
-    "      content_type = \"application/json\"\n",
-    "      accepts = \"application/json\"\n",
-    "\n",
-    "      def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:\n",
-    "          input_str = json.dumps({\"inputs\": \n",
-    "                                  [[\n",
-    "                                    {\"role\": \"user\", \"content\": prompt},\n",
-    "                                  ]],\n",
-    "                                  **model_kwargs\n",
-    "                                  })\n",
-    "          \n",
-    "          return input_str.encode('utf-8')\n",
-    "      \n",
-    "      def transform_output(self, output: bytes) -> str:\n",
-    "          response_json = json.loads(output.read().decode(\"utf-8\"))\n",
-    "          \n",
-    "          return response_json[0]['generation']['content']\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dab1012f-ed20-47b9-9162-924e03e836d5",
-   "metadata": {},
-   "source": [
-    "Now that we have a class that handles our input and outputs in a format that our model can understand we use **SageMakerEndpoint** tool to connect to our endpoint that we made in SageMaker. \n",
-    "\n",
-    "Notice that we set our class as the **content_handler** and we specified other **model_kwargs** as our parameters to control the temperature, top p, and max number of new tokens the model should generate to process our output.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cadb1af-2c46-4ab1-92f9-6e0861f83324",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```python\n",
-    "content_handler = ContentHandler()\n",
-    "\n",
-    "  llm=SagemakerEndpoint(\n",
-    "          endpoint_name=endpoint_name, \n",
-    "          region_name=region, \n",
-    "          model_kwargs={\"parameters\": {\"max_new_tokens\": 1000, \"top_p\": 0.9,\"temperature\":0.6}},\n",
-    "          endpoint_kwargs={\"CustomAttributes\":\"accept_eula=true\"},\n",
-    "          content_handler=content_handler,\n",
-    "      )\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c44b4f91-0c64-459b-a6e9-8a955c0797c7",
-   "metadata": {},
-   "source": [
-    "We specify what our retriever both the PubMed and Kendra retriever are list, please only add one per script."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21c61724-23d3-4b49-8c72-cbd208bdb5df",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```python\n",
-    "retriever= PubMedRetriever()\n",
-    "retriever = AmazonKendraRetriever(index_id=kendra_index_id,region_name=region) #only use if using Kendra Index\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ec8e464a-0931-444a-aa58-09ee0c4c9884",
-   "metadata": {},
-   "source": [
-    "Here we are constructing our **prompt_template**, this is where we can try zero-shot or few-shot prompting. Only add one method per script."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4431051e-0e84-408e-9821-f50a9b88c9c1",
-   "metadata": {},
-   "source": [
-    "#### Zero-shot prompting\n",
-    "\n",
-    "Zero-shot prompting does not require any additional training more so it gives a pre-trained language model a task or query to generate text (our output). The model relies on its general language understanding and the patterns it has learned during its training to produce relevant output. In our script we have connect our model to a **retriever** to make sure it gathers information from that retriever (this can be the PubMed API or Kendra). \n",
-    "\n",
-    "See below that the task is more like instructions notifying our model they will be asked questions which it will answer based on the info of the scientific documents provided from the index provided (this can be the PubMed API or Kendra index). All of this information is established as a **prompt template** for our model to receive."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0316dc5-6274-4a5e-92e4-3d266ed6a4df",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "```python\n",
-    "prompt_template = \"\"\"\n",
-    "  Ignore everything before.\n",
-    "  \n",
-    "  Instruction:\n",
-    "  Instructions:\n",
-    "  I will provide you with research papers on a specific topic in English, and you will create a cumulative summary. \n",
-    "  The summary should be concise and should accurately and objectively communicate the takeaway of the papers related to the topic. \n",
-    "  You should not include any personal opinions or interpretations in your summary, but rather focus on objectively presenting the information from the papers. \n",
-    "  Your summary should be written in your own words and ensure that your summary is clear, concise, and accurately reflects the content of the original papers. First, provide a concise summary then citations at the end.\n",
-    "  \n",
-    "  {question} Answer \"don't know\" if not present in the document. \n",
-    "  {context}\n",
-    "  Solution:\"\"\"\n",
-    "  PROMPT = PromptTemplate(\n",
-    "      template=prompt_template, input_variables=[\"context\", \"question\"],\n",
-    "  )\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "edbe7032-8507-4d07-baab-1b3bf0e92074",
-   "metadata": {},
-   "source": [
-    "#### One-shot and Few-shot Prompting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5614ea04-e1f8-4941-ae16-4359f718f98f",
-   "metadata": {},
-   "source": [
-    "One and few shot prompting are similar to one-shot prompting, in addition to giving our model a task just like before we have also supplied an example of how the our model structure our output.\n",
-    "\n",
-    "See below that we have implemented one-shot prompting to our script.  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5ffb9669-5b77-4d9b-9f4e-a0d3a18b0fae",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "prompt_template = \"\"\"\n",
-    "  Instructions:\n",
-    "  I will provide you with research papers on a specific topic in English, and you will create a cumulative summary. \n",
-    "  The summary should be concise and should accurately and objectively communicate the takeaway of the papers related to the topic. \n",
-    "  You should not include any personal opinions or interpretations in your summary, but rather focus on objectively presenting the information from the papers. \n",
-    "  Your summary should be written in your own words and ensure that your summary is clear, concise, and accurately reflects the content of the original papers. First, provide a concise summary then citations at the end. \n",
-    "  Examples:\n",
-    "  Question: What is a cell?\n",
-    "  Answer: '''\n",
-    "  Cell, in biology, the basic membrane-bound unit that contains the fundamental molecules of life and of which all living things are composed. \n",
-    "  Sources: \n",
-    "  Chow, Christopher , Laskey, Ronald A. , Cooper, John A. , Alberts, Bruce M. , Staehelin, L. Andrew , \n",
-    "  Stein, Wilfred D. , Bernfield, Merton R. , Lodish, Harvey F. , Cuffe, Michael and Slack, Jonathan M.W.. \n",
-    "  \"cell\". Encyclopedia Britannica, 26 Sep. 2023, https://www.britannica.com/science/cell-biology. Accessed 9 November 2023.\n",
-    "  '''\n",
-    "  \n",
-    "  {question} Answer \"don't know\" if not present in the document. \n",
-    "  {context}\n",
-    "  \n",
-    "\n",
-    "  \n",
-    "  Solution:\"\"\"\n",
-    "  PROMPT = PromptTemplate(\n",
-    "      template=prompt_template, input_variables=[\"context\", \"question\"],\n",
-    "  )\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "82c66d53-97b2-46dc-a466-70a3d3bee4a7",
-   "metadata": {},
-   "source": [
-    "The following set of commands control the chat history essentially telling the model to expect another question after it finishes answering the previous one. Follow up questions can contain references to past chat history so the **ConversationalRetrievalChain** combines the chat history and the followup question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response.\n",
-    "\n",
-    "All of these pieces such as our conversational chain, prompt, and chat history are passed through a function called **run_chain** so that our model can return is response. We have also set the length of our chat history to one meaning that our model can only refer to the pervious conversation as a reference."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fda4d33b-60f2-4462-a8e6-bbce7f8a7b07",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "condense_qa_template = \"\"\"\n",
-    "  Chat History:\n",
-    "  {chat_history}\n",
-    "  Here is a new question for you: {question}\n",
-    "  Standalone question:\"\"\"\n",
-    "  standalone_question_prompt = PromptTemplate.from_template(condense_qa_template)\n",
-    " \n",
-    "    qa = ConversationalRetrievalChain.from_llm(\n",
-    "        llm=llm, \n",
-    "        retriever=retriever, \n",
-    "        condense_question_prompt=standalone_question_prompt, \n",
-    "        return_source_documents=True, \n",
-    "        combine_docs_chain_kwargs={\"prompt\":PROMPT},\n",
-    "        )\n",
-    "      return qa\n",
-    "\n",
-    "def run_chain(chain, prompt: str, history=[]):\n",
-    "    print(prompt)\n",
-    "    return chain({\"question\": prompt, \"chat_history\": history})\n",
-    "\n",
-    "MAX_HISTORY_LENGTH = 1 #increase to refer to more pervious chats\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b8f1ef8d-66fe-4f84-933b-af2d730bd114",
-   "metadata": {},
-   "source": [
-    "The final part of our script utilizes our class and incorporates colors to add a bit of flare to our conversation with our model. The model when first initialized should greet the user asking **\"Hello! How can I help you?\"** then instructs the user to ask a question or exit the session **\"Ask a question, start a New search: or CTRL-D to exit.\"**. With every question submitted to the model it is labeled as a **new search** we then run the run_chain function to get the models response or answer and add the response to the **chat history**. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1aa6ef65-ced4-445e-875c-7fee3483b81d",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "if __name__ == \"__main__\":\n",
-    "  chat_history = []\n",
-    "  qa = build_chain()\n",
-    "  print(bcolors.OKBLUE + \"Hello! How can I help you?\" + bcolors.ENDC)\n",
-    "  print(bcolors.OKCYAN + \"Ask a question, start a New search: or CTRL-D to exit.\" + bcolors.ENDC)\n",
-    "  print(\">\", end=\" \", flush=True)\n",
-    "  for query in sys.stdin:\n",
-    "    if (query.strip().lower().startswith(\"new search:\")):\n",
-    "      query = query.strip().lower().replace(\"new search:\",\"\")\n",
-    "      chat_history = []\n",
-    "    elif (len(chat_history) == MAX_HISTORY_LENGTH):\n",
-    "      chat_history.pop(0)\n",
-    "    result = run_chain(qa, query, chat_history)\n",
-    "    chat_history.append((query, result[\"answer\"]))\n",
-    "    print(bcolors.OKGREEN + result['answer'] + bcolors.ENDC)\n",
-    "    ###this if statment is not needed for PubMed Retreiver users\n",
-    "    if 'source_documents' in result: \n",
-    "      print(bcolors.OKGREEN + 'Sources:')\n",
-    "      for d in result['source_documents']:\n",
-    "        print(d.metadata['source'])\n",
-    "    ###\n",
-    "    print(bcolors.ENDC)\n",
-    "    print(bcolors.OKCYAN + \"Ask a question, start a New search: or CTRL-D to exit.\" + bcolors.ENDC)\n",
-    "    print(\">\", end=\" \", flush=True)\n",
-    "  print(bcolors.OKBLUE + \"Bye\" + bcolors.ENDC)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1abcbd48-bb84-4310-b8eb-ad87850a8649",
-   "metadata": {},
-   "source": [
-    "Running our script in the terminal will require us to export the following global variables then running our python script. Dont forget to run you python script on the terminal use the command `python NAME_OF_YOUR_SCRIPT.py`. For more guidence take a look at our **example inference scripts** for the [PubMed API](/example_scripts/langchain_chat_llama_2_zeroshot.py) and [Kendra](/example_scripts/kendra_chat_llama_2.py)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "ba97df23-6893-438d-8a67-cb7dbf83e407",
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'meta-textgeneration-llama-2-7b-f-2023-11-21-20-18-40-341'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#retreive our endpoint id\n",
-    "endpoint_id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7eab00a3-54ff-4873-8d25-eaf8bd18a2e6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#enter the global variables in your terminal\n",
-    "export AWS_REGION='<Enter_location>'\n",
-    "export LLAMA_2_ENDPOINT='<Enter_endpoint_id>'\n",
-    "export KENDRA_INDEX_ID='<Enter_kendra_index>'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bbe127e6-c0b1-4e07-ad56-38c30a9bf858",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "You should see similar results on the terminal. In this example we ask the chatbot to explain brain cancer!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80c8fb4b-e74f-4e8d-892b-0f913eff747d",
-   "metadata": {},
-   "source": [
-    "![PubMed Chatbot Results](../../../docs/images/PubMed_chatbot_results.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a178c1c6-368a-48c5-8beb-278443b685a2",
-   "metadata": {},
-   "source": [
-    "### Clean Up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7ec06a34-dc47-453f-b519-424804fa2748",
-   "metadata": {},
-   "source": [
-    "**Warning:** Dont forget to delete the resources we just made to avoid accruing additional costs!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "c307bb17-757a-4579-a0d8-698eb1bb3f2e",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'endpoint' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#Delete model and endpoint\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#model.delete()\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mendpoint\u001b[49m\u001b[38;5;241m.\u001b[39mdelete()\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'endpoint' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "#Delete model and endpoint\n",
-    "model.delete()\n",
-    "endpoint.delete()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "280cea0a-a8fc-494e-8ce4-afb65847a222",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Delete bucket\n",
-    "!aws s3 rb s3://$bucket --force  "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "conda_python3",
-   "language": "python",
-   "name": "conda_python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}