bring-custom-container.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab: Bring your own custom container with Amazon SageMaker\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "⚠️ In order to run this notebook, please ensure Docker is enabled on your SageMaker Studio Domain. If running this notebook at an AWS facilitated event, <b>you can skip this part</b>. If you have provisioned your own SageMaker Studio Domain, please  <a href=\"https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-local.html#studio-updated-local-enable\">read here</a> about how to enable Docker via the AWS CLI on an existing SageMaker Studio Domain. After running this command, <b>you must restart your JupyterApp for the changes to take effect</b>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "### Background\n",
    "Here, we'll show how to bring your docker cotainer that packages your environment and code. We showcase the [decision tree](http://scikit-learn.org/stable/modules/tree.html) algorithm from the widely used [scikit-learn](http://scikit-learn.org/stable/) machine learning package. The example is purposefully fairly trivial since the point is to show the surrounding structure that you'll want to add to your own container so you can bring it to Amazon SageMaker for training and hosting.\n",
    "\n",
    "\n",
    "### High-level overview\n",
    "\n",
    "The following diagram shows how you typically train and deploy a model with Amazon SageMaker:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "<img src=\"https://docs.aws.amazon.com/sagemaker/latest/dg/images/sagemaker-architecture.png\" width=\"900\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The area labeled SageMaker highlights the two components of SageMaker: model training and model deployment. The area labeled [EC2 container registry](https://aws.amazon.com/ecr/) is where we store, manage, and deploy our Docker container images. The training data and model artifacts are stored in S3 bucket. \n",
    "\n",
    "In this lab, we use a single image to support both model training and hosting for simplicity. Sometimes you’ll want separate images for training and hosting because they have different requirements. \n",
    "\n",
    "The high-level steps include:\n",
    "1. **Building the container** - We walk through the different components of the containers and inspect the docker file. Then we build and push the container to ECR. \n",
    "2. **Setup & Upload Data** - Once our container is built and registered. We ready sagemaker and upload the data to S3. \n",
    "3. **Model Training** - Create a training job using SageMaker Python SDK. It will pull data from S3 and use the container we built.  \n",
    "4. **Model Deployment** - Once training is complete, deploy our model to a HTTP endpoint using SageMaker Python SDK. \n",
    "5. **Run Inferences** - Run predictions to test our model.\n",
    "6. **Cleanup**\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the container\n",
    "[Docker](https://aws.amazon.com/docker/#:~:text=Docker%20is%20a%20software%20platform,test%2C%20and%20deploy%20applications%20quickly.&text=Running%20Docker%20on%20AWS%20provides,distributed%20applications%20at%20any%20scale.) packages software into standardized units called [containers](https://aws.amazon.com/containers/) that have everything the software needs to run including libraries, system tools, code, and runtime. Using Docker, you can quickly deploy and scale applications into any environment and know your code will run.\n",
    "\n",
    "\n",
    "Amazon SageMaker uses Docker to allow users to train and deploy arbitrary algorithms. More details on [how to use docker containers with sagemaker](https://docs.aws.amazon.com/sagemaker/latest/dg/docker-containers.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Walkthrough of the container directory\n",
    "You can find the source code of the sample container we are using in [this GitHub repository](https://github.com/aws/amazon-sagemaker-examples/tree/main/advanced_functionality/scikit_bring_your_own). \n",
    "\n",
    "The container directory contains all the components you need to package for SageMaker:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    ".\n",
    "|-- Dockerfile\n",
    "|-- build_and_push.sh\n",
    "|-- local_test\n",
    "`-- decision_trees\n",
    "    |-- nginx.conf\n",
    "    |-- predictor.py\n",
    "    |-- serve\n",
    "    |-- train\n",
    "    `-- wsgi.py\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s discuss each of these in turn:\n",
    "\n",
    "- `Dockerfile` describes how to build your Docker container image. More details below.\n",
    "- `build_and_push.sh` is a script that uses the Dockerfile to build your container images and then pushes it to ECR. We’ll invoke the commands directly later in this notebook, but you can just copy and run the script for your own algorithms.\n",
    "- `local_test` is a directory that shows how to test your new container on any computer that can run Docker, including an Amazon SageMaker notebook instance. Using this method, you can quickly iterate using small datasets to eliminate any structural bugs before you use the container with Amazon SageMaker. Testing is not the focus of this lab, but feel free to checkout the example at your own time.  \n",
    "- `decision_trees` is the directory which contains the files that will be installed in the container.\n",
    "\n",
    "In this simple application, we only install five files in the container. These five show the standard structure of our Python containers, although you are free to choose a different toolset or programming language and therefore could have a different layout.\n",
    "\n",
    "The files that we’ll put in the container are:\n",
    "\n",
    "- `nginx.conf` is the configuration file for the nginx front-end. Generally, you should be able to take this file as-is.\n",
    "- `predictor.py` is the program that actually implements the Flask web server and the decision tree predictions for this app. You’ll want to customize the actual prediction parts to your application. Since this algorithm is simple, we do all the processing here in this file, but you may choose to have separate files for implementing your custom logic.\n",
    "- `serve` is the program started when the container is started for hosting. It simply launches the gunicorn server which runs multiple instances of the Flask app defined in predictor.py. You should be able to take this file as-is.\n",
    "- `train` is the program that is invoked when the container is run for training. You will modify this program to implement your training algorithm.\n",
    "- `wsgi.py` is a small wrapper used to invoke the Flask app. You should be able to take this file as-is.\n",
    "\n",
    "In summary, the two files you will probably want to change for your application are `train` and `predictor.py`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install packages\n",
    "Please choose `Python 3 (ipykernel)` kernel to proceed.\n",
    "\n",
    "We will first install the prerequisite packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.11/site-packages (24.2)\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "autogluon-multimodal 1.1.1 requires nvidia-ml-py3==7.352.0, which is not installed.\n",
      "amazon-sagemaker-sql-magic 0.1.3 requires sqlparse==0.5.0, but you have sqlparse 0.5.1 which is incompatible.\n",
      "autogluon-core 1.1.1 requires scikit-learn<1.4.1,>=1.3.0, but you have scikit-learn 1.4.2 which is incompatible.\n",
      "autogluon-features 1.1.1 requires scikit-learn<1.4.1,>=1.3.0, but you have scikit-learn 1.4.2 which is incompatible.\n",
      "autogluon-multimodal 1.1.1 requires omegaconf<2.3.0,>=2.1.1, but you have omegaconf 2.3.0 which is incompatible.\n",
      "autogluon-multimodal 1.1.1 requires scikit-learn<1.4.1,>=1.3.0, but you have scikit-learn 1.4.2 which is incompatible.\n",
      "autogluon-tabular 1.1.1 requires scikit-learn<1.4.1,>=1.3.0, but you have scikit-learn 1.4.2 which is incompatible.\n",
      "autogluon-timeseries 1.1.1 requires gluonts==0.15.1, but you have gluonts 0.14.3 which is incompatible.\n",
      "autovizwidget 0.21.0 requires pandas<2.0.0,>=0.20.1, but you have pandas 2.1.4 which is incompatible.\n",
      "hdijupyterutils 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.4 which is incompatible.\n",
      "sagemaker 2.227.0 requires boto3<2.0,>=1.34.142, but you have boto3 1.34.131 which is incompatible.\n",
      "sparkmagic 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "autogluon-multimodal 1.1.1 requires nvidia-ml-py3==7.352.0, which is not installed.\n",
      "jupyter-ai 2.20.0 requires faiss-cpu<=1.8.0, which is not installed.\n",
      "autogluon-multimodal 1.1.1 requires omegaconf<2.3.0,>=2.1.1, but you have omegaconf 2.3.0 which is incompatible.\n",
      "autogluon-multimodal 1.1.1 requires scikit-learn<1.4.1,>=1.3.0, but you have scikit-learn 1.4.2 which is incompatible.\n",
      "autogluon-timeseries 1.1.1 requires gluonts==0.15.1, but you have gluonts 0.14.3 which is incompatible.\n",
      "sagemaker 2.227.0 requires boto3<2.0,>=1.34.142, but you have boto3 1.34.131 which is incompatible.\n",
      "sparkmagic 0.21.0 requires pandas<2.0.0,>=0.17.1, but you have pandas 2.1.4 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "# cell 00\n",
    "\n",
    "!pip install --root-user-action=ignore --upgrade pip\n",
    "!pip install --root-user-action=ignore -q pandas==2.1.4\n",
    "!pip install --root-user-action=ignore -q awswrangler==3.5.1 --no-cache"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Install Docker\n",
    "\n",
    "To use Docker, you must manually install it from the terminal of your JupyterLab application. Please get familiar with the docker operations that are currently supported in Studio [see here](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-local.html).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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      "Setting up docker-compose-plugin (2.29.7-1~ubuntu.22.04~jammy) ...\n",
      "Setting up docker-ce-cli (5:20.10.24~3-0~ubuntu-jammy) ...\n",
      "Client: Docker Engine - Community\n",
      " Version:           20.10.24\n",
      " API version:       1.41\n",
      " Go version:        go1.19.7\n",
      " Git commit:        297e128\n",
      " Built:             Tue Apr  4 18:21:03 2023\n",
      " OS/Arch:           linux/amd64\n",
      " Context:           default\n",
      " Experimental:      true\n",
      "\n",
      "Server:\n",
      " Engine:\n",
      "  Version:          25.0.6\n",
      "  API version:      1.44 (minimum version 1.24)\n",
      "  Go version:       go1.22.5\n",
      "  Git commit:       b08a51f\n",
      "  Built:            Mon Jul 29 17:22:09 2024\n",
      "  OS/Arch:          linux/amd64\n",
      "  Experimental:     false\n",
      " containerd:\n",
      "  Version:          v1.7.22-24-g59cf2f9d8.m\n",
      "  GitCommit:        59cf2f9d8d78c6a749c92d36bf61471576ad45ec.m\n",
      " runc:\n",
      "  Version:          1.1.14\n",
      "  GitCommit:        2c9f5602f0ba3d9da1c2596322dfc4e156844890\n",
      " docker-init:\n",
      "  Version:          0.19.0\n",
      "  GitCommit:        de40ad0\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "# see https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository\n",
    "sudo apt-get update\n",
    "sudo apt-get install -y ca-certificates curl\n",
    "sudo install -m 0755 -d /etc/apt/keyrings\n",
    "sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc\n",
    "sudo chmod a+r /etc/apt/keyrings/docker.asc\n",
    "\n",
    "# Add the repository to Apt sources:\n",
    "echo \\\n",
    "  \"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \\\n",
    "  $(. /etc/os-release && echo \"$VERSION_CODENAME\") stable\" | \\\n",
    "  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null\n",
    "sudo apt-get update\n",
    "\n",
    "## Currently only Docker version 20.10.X is supported in Studio: see https://docs.aws.amazon.com/sagemaker/latest/dg/studio-updated-local.html\n",
    "# pick the latest patch from:\n",
    "# apt-cache madison docker-ce | awk '{ print $3 }' | grep -i 20.10\n",
    "VERSION_STRING=5:20.10.24~3-0~ubuntu-jammy\n",
    "sudo apt-get install docker-ce-cli=$VERSION_STRING docker-compose-plugin -y\n",
    "\n",
    "# validate the Docker Client is able to access Docker Server at [unix:///docker/proxy.sock]\n",
    "docker version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then unzip and copy over the files we need:\n",
    "- `scikit_bring_your_own/container` → `lab03_container`\n",
    "- `scikit_bring_your_own/data` → `lab03_data` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 01\n",
    "\n",
    "!unzip -q scikit_bring_your_own.zip\n",
    "!mv scikit_bring_your_own/data/ ./lab03_data/\n",
    "!mv scikit_bring_your_own/container/ ./lab03_container/\n",
    "!rm -rf scikit_bring_your_own"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Dockerfile\n",
    "The `Dockerfile` describes the image that we want to build. You can think of it as describing the complete operating system installation of the system that you want to run. A Docker container running is quite a bit lighter than a full operating system, however, because it takes advantage of Linux on the host machine for the basic operations.\n",
    "\n",
    "For the Python science stack, we will start from a standard Ubuntu installation and run the normal tools to install the things needed by `scikit-learn`. Finally, we add the code that implements our specific algorithm to the container and set up the right environment to run under."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look of what's inside our `Dockerfile`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[37m# Build an image that can do training and inference in SageMaker\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# This is a Python 3 image that uses the nginx, gunicorn, flask stack\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# for serving inferences in a stable way.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mFROM\u001b[39;49;00m\u001b[37m \u001b[39;49;00m\u001b[33mpublic.ecr.aws/ubuntu/ubuntu:22.04\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mMAINTAINER\u001b[39;49;00m\u001b[37m \u001b[39;49;00m\u001b[33mAmazon AI <sage-learner@amazon.com>\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mRUN\u001b[39;49;00m\u001b[37m \u001b[39;49;00mapt-get\u001b[37m \u001b[39;49;00m-y\u001b[37m \u001b[39;49;00mupdate\u001b[37m \u001b[39;49;00m&&\u001b[37m \u001b[39;49;00mapt-get\u001b[37m \u001b[39;49;00minstall\u001b[37m \u001b[39;49;00m-y\u001b[37m \u001b[39;49;00m--no-install-recommends\u001b[37m \u001b[39;49;00m\u001b[33m\\\u001b[39;49;00m\n",
      "\u001b[37m         \u001b[39;49;00mwget\u001b[37m \u001b[39;49;00m\u001b[33m\\\u001b[39;49;00m\n",
      "\u001b[37m         \u001b[39;49;00mpython3-pip\u001b[37m \u001b[39;49;00m\u001b[33m\\\u001b[39;49;00m\n",
      "\u001b[37m         \u001b[39;49;00mpython3-setuptools\u001b[37m \u001b[39;49;00m\u001b[33m\\\u001b[39;49;00m\n",
      "\u001b[37m         \u001b[39;49;00mnginx\u001b[37m \u001b[39;49;00m\u001b[33m\\\u001b[39;49;00m\n",
      "\u001b[37m         \u001b[39;49;00mca-certificates\u001b[37m \u001b[39;49;00m\u001b[33m\\\u001b[39;49;00m\n",
      "\u001b[37m    \u001b[39;49;00m&&\u001b[37m \u001b[39;49;00mrm\u001b[37m \u001b[39;49;00m-rf\u001b[37m \u001b[39;49;00m/var/lib/apt/lists/*\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mRUN\u001b[39;49;00m\u001b[37m \u001b[39;49;00mln\u001b[37m \u001b[39;49;00m-s\u001b[37m \u001b[39;49;00m/usr/bin/python3\u001b[37m \u001b[39;49;00m/usr/bin/python\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# Here we get all python packages.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# There's substantial overlap between scipy and numpy that we eliminate by\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# linking them together. Likewise, pip leaves the install caches populated which uses\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# a significant amount of space. These optimizations save a fair amount of space in the\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# image, which reduces start up time.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mRUN\u001b[39;49;00m\u001b[37m \u001b[39;49;00mpip\u001b[37m \u001b[39;49;00m--no-cache-dir\u001b[37m \u001b[39;49;00minstall\u001b[37m \u001b[39;49;00mscikit-learn==\u001b[34m1\u001b[39;49;00m.1.0\u001b[37m \u001b[39;49;00mpandas\u001b[37m \u001b[39;49;00mflask\u001b[37m \u001b[39;49;00mgunicorn\u001b[37m \u001b[39;49;00m\u001b[31mnumpy\u001b[39;49;00m==\u001b[34m1\u001b[39;49;00m.26.4\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# Set some environment variables. PYTHONUNBUFFERED keeps Python from buffering our standard\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# output stream, which means that logs can be delivered to the user quickly. PYTHONDONTWRITEBYTECODE\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# keeps Python from writing the .pyc files which are unnecessary in this case. We also update\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# PATH so that the train and serve programs are found when the container is invoked.\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mENV\u001b[39;49;00m\u001b[37m \u001b[39;49;00m\u001b[31mPYTHONUNBUFFERED\u001b[39;49;00m=TRUE\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mENV\u001b[39;49;00m\u001b[37m \u001b[39;49;00m\u001b[31mPYTHONDONTWRITEBYTECODE\u001b[39;49;00m=TRUE\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mENV\u001b[39;49;00m\u001b[37m \u001b[39;49;00m\u001b[31mPATH\u001b[39;49;00m=\u001b[33m\"\u001b[39;49;00m\u001b[33m/opt/program:\u001b[39;49;00m\u001b[33m${\u001b[39;49;00m\u001b[31mPATH\u001b[39;49;00m\u001b[33m}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# Set up the program in the image\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mCOPY\u001b[39;49;00m\u001b[37m \u001b[39;49;00mdecision_trees\u001b[37m \u001b[39;49;00m/opt/program\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mWORKDIR\u001b[39;49;00m\u001b[37m \u001b[39;49;00m\u001b[33m/opt/program\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n"
     ]
    }
   ],
   "source": [
    "!pygmentize lab03_container/Dockerfile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building and registering the container"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING! Your password will be stored unencrypted in /home/sagemaker-user/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Login Succeeded\n",
      "Sending build context to Docker daemon  58.37kB\n",
      "Step 1/11 : FROM public.ecr.aws/ubuntu/ubuntu:22.04\n",
      "22.04: Pulling from ubuntu/ubuntu\n",
      "6414378b6477: Pulling fs layer\n",
      "6414378b6477: Verifying Checksum\n",
      "6414378b6477: Download complete\n",
      "6414378b6477: Pull complete\n",
      "Digest: sha256:1582c29f34a48752e406f1a261fe9545fad895da3f6bb4be55bc82485557564e\n",
      "Status: Downloaded newer image for public.ecr.aws/ubuntu/ubuntu:22.04\n",
      " ---> 97271d29cb79\n",
      "Step 2/11 : MAINTAINER Amazon AI <sage-learner@amazon.com>\n",
      " ---> Running in a83be913f3ca\n",
      " ---> Removed intermediate container a83be913f3ca\n",
      " ---> 2ce01264fe7e\n",
      "Step 3/11 : RUN apt-get -y update && apt-get install -y --no-install-recommends          wget          python3-pip          python3-setuptools          nginx          ca-certificates     && rm -rf /var/lib/apt/lists/*\n",
      " ---> Running in ea68ab312b2a\n",
      "Get:1 http://archive.ubuntu.com/ubuntu jammy InRelease [270 kB]\n",
      "Get:2 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB]\n",
      "Get:3 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [44.7 kB]\n",
      "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB]\n",
      "Get:5 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [3205 kB]\n",
      "Get:6 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB]\n",
      "Get:7 http://archive.ubuntu.com/ubuntu jammy/restricted amd64 Packages [164 kB]\n",
      "Get:8 http://archive.ubuntu.com/ubuntu jammy/multiverse amd64 Packages [266 kB]\n",
      "Get:9 http://archive.ubuntu.com/ubuntu jammy/universe amd64 Packages [17.5 MB]\n",
      "Get:10 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [2377 kB]\n",
      "Get:11 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1162 kB]\n",
      "Get:12 http://archive.ubuntu.com/ubuntu jammy/main amd64 Packages [1792 kB]\n",
      "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [51.8 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [2654 kB]\n",
      "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [3283 kB]\n",
      "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1450 kB]\n",
      "Get:17 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [33.7 kB]\n",
      "Get:18 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [81.4 kB]\n",
      "Fetched 34.7 MB in 5s (7706 kB/s)\n",
      "Reading package lists...\n",
      "Reading package lists...\n",
      "Building dependency tree...\n",
      "Reading state information...\n",
      "The following additional packages will be installed:\n",
      "  fontconfig-config fonts-dejavu-core iproute2 libbpf0 libbrotli1 libbsd0\n",
      "  libcap2-bin libdeflate0 libelf1 libexpat1 libfontconfig1 libfreetype6 libgd3\n",
      "  libicu70 libjbig0 libjpeg-turbo8 libjpeg8 libmaxminddb0 libmd0 libmnl0\n",
      "  libmpdec3 libnginx-mod-http-geoip2 libnginx-mod-http-image-filter\n",
      "  libnginx-mod-http-xslt-filter libnginx-mod-mail libnginx-mod-stream\n",
      "  libnginx-mod-stream-geoip2 libpng16-16 libpsl5 libpython3-stdlib\n",
      "  libpython3.10-minimal libpython3.10-stdlib libreadline8 libsqlite3-0\n",
      "  libtiff5 libwebp7 libx11-6 libx11-data libxau6 libxcb1 libxdmcp6 libxml2\n",
      "  libxpm4 libxslt1.1 libxtables12 media-types nginx-common nginx-core openssl\n",
      "  python3 python3-distutils python3-lib2to3 python3-minimal\n",
      "  python3-pkg-resources python3-wheel python3.10 python3.10-minimal\n",
      "  readline-common ucf\n",
      "Suggested packages:\n",
      "  iproute2-doc libgd-tools mmdb-bin fcgiwrap nginx-doc ssl-cert python3-doc\n",
      "  python3-tk python3-venv python-setuptools-doc python3.10-venv python3.10-doc\n",
      "  binutils binfmt-support readline-doc\n",
      "Recommended packages:\n",
      "  libatm1 libpam-cap publicsuffix build-essential python3-dev\n",
      "The following NEW packages will be installed:\n",
      "  ca-certificates fontconfig-config fonts-dejavu-core iproute2 libbpf0\n",
      "  libbrotli1 libbsd0 libcap2-bin libdeflate0 libelf1 libexpat1 libfontconfig1\n",
      "  libfreetype6 libgd3 libicu70 libjbig0 libjpeg-turbo8 libjpeg8 libmaxminddb0\n",
      "  libmd0 libmnl0 libmpdec3 libnginx-mod-http-geoip2\n",
      "  libnginx-mod-http-image-filter libnginx-mod-http-xslt-filter\n",
      "  libnginx-mod-mail libnginx-mod-stream libnginx-mod-stream-geoip2 libpng16-16\n",
      "  libpsl5 libpython3-stdlib libpython3.10-minimal libpython3.10-stdlib\n",
      "  libreadline8 libsqlite3-0 libtiff5 libwebp7 libx11-6 libx11-data libxau6\n",
      "  libxcb1 libxdmcp6 libxml2 libxpm4 libxslt1.1 libxtables12 media-types nginx\n",
      "  nginx-common nginx-core openssl python3 python3-distutils python3-lib2to3\n",
      "  python3-minimal python3-pip python3-pkg-resources python3-setuptools\n",
      "  python3-wheel python3.10 python3.10-minimal readline-common ucf wget\n",
      "0 upgraded, 64 newly installed, 0 to remove and 0 not upgraded.\n",
      "Need to get 27.7 MB of archives.\n",
      "After this operation, 93.6 MB of additional disk space will be used.\n",
      "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpython3.10-minimal amd64 3.10.12-1~22.04.6 [814 kB]\n",
      "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libexpat1 amd64 2.4.7-1ubuntu0.4 [91.2 kB]\n",
      "Get:3 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3.10-minimal amd64 3.10.12-1~22.04.6 [2258 kB]\n",
      "Get:4 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-minimal amd64 3.10.6-1~22.04.1 [24.3 kB]\n",
      "Get:5 http://archive.ubuntu.com/ubuntu jammy/main amd64 media-types all 7.0.0 [25.5 kB]\n",
      "Get:6 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmpdec3 amd64 2.5.1-2build2 [86.8 kB]\n",
      "Get:7 http://archive.ubuntu.com/ubuntu jammy/main amd64 readline-common all 8.1.2-1 [53.5 kB]\n",
      "Get:8 http://archive.ubuntu.com/ubuntu jammy/main amd64 libreadline8 amd64 8.1.2-1 [153 kB]\n",
      "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libsqlite3-0 amd64 3.37.2-2ubuntu0.3 [641 kB]\n",
      "Get:10 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpython3.10-stdlib amd64 3.10.12-1~22.04.6 [1850 kB]\n",
      "Get:11 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3.10 amd64 3.10.12-1~22.04.6 [509 kB]\n",
      "Get:12 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libpython3-stdlib amd64 3.10.6-1~22.04.1 [6812 B]\n",
      "Get:13 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3 amd64 3.10.6-1~22.04.1 [22.8 kB]\n",
      "Get:14 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 openssl amd64 3.0.2-0ubuntu1.18 [1184 kB]\n",
      "Get:15 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 ca-certificates all 20240203~22.04.1 [162 kB]\n",
      "Get:16 http://archive.ubuntu.com/ubuntu jammy/main amd64 libelf1 amd64 0.186-1build1 [51.0 kB]\n",
      "Get:17 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libbpf0 amd64 1:0.5.0-1ubuntu22.04.1 [140 kB]\n",
      "Get:18 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmd0 amd64 1.0.4-1build1 [23.0 kB]\n",
      "Get:19 http://archive.ubuntu.com/ubuntu jammy/main amd64 libbsd0 amd64 0.11.5-1 [44.8 kB]\n",
      "Get:20 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmnl0 amd64 1.0.4-3build2 [13.2 kB]\n",
      "Get:21 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libxtables12 amd64 1.8.7-1ubuntu5.2 [31.3 kB]\n",
      "Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libcap2-bin amd64 1:2.44-1ubuntu0.22.04.1 [26.0 kB]\n",
      "Get:23 http://archive.ubuntu.com/ubuntu jammy/main amd64 iproute2 amd64 5.15.0-1ubuntu2 [1070 kB]\n",
      "Get:24 http://archive.ubuntu.com/ubuntu jammy/main amd64 libicu70 amd64 70.1-2 [10.6 MB]\n",
      "Get:25 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libxml2 amd64 2.9.13+dfsg-1ubuntu0.4 [763 kB]\n",
      "Get:26 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-pkg-resources all 59.6.0-1.2ubuntu0.22.04.2 [133 kB]\n",
      "Get:27 http://archive.ubuntu.com/ubuntu jammy/main amd64 ucf all 3.0043 [56.1 kB]\n",
      "Get:28 http://archive.ubuntu.com/ubuntu jammy/main amd64 libmaxminddb0 amd64 1.5.2-1build2 [24.7 kB]\n",
      "Get:29 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpng16-16 amd64 1.6.37-3build5 [191 kB]\n",
      "Get:30 http://archive.ubuntu.com/ubuntu jammy/main amd64 libpsl5 amd64 0.21.0-1.2build2 [58.4 kB]\n",
      "Get:31 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxau6 amd64 1:1.0.9-1build5 [7634 B]\n",
      "Get:32 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxdmcp6 amd64 1:1.1.3-0ubuntu5 [10.9 kB]\n",
      "Get:33 http://archive.ubuntu.com/ubuntu jammy/main amd64 libxcb1 amd64 1.14-3ubuntu3 [49.0 kB]\n",
      "Get:34 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-data all 2:1.7.5-1ubuntu0.3 [120 kB]\n",
      "Get:35 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libx11-6 amd64 2:1.7.5-1ubuntu0.3 [667 kB]\n",
      "Get:36 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 wget amd64 1.21.2-2ubuntu1.1 [339 kB]\n",
      "Get:37 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-dejavu-core all 2.37-2build1 [1041 kB]\n",
      "Get:38 http://archive.ubuntu.com/ubuntu jammy/main amd64 fontconfig-config all 2.13.1-4.2ubuntu5 [29.1 kB]\n",
      "Get:39 http://archive.ubuntu.com/ubuntu jammy/main amd64 libbrotli1 amd64 1.0.9-2build6 [315 kB]\n",
      "Get:40 http://archive.ubuntu.com/ubuntu jammy/main amd64 libdeflate0 amd64 1.10-2 [70.9 kB]\n",
      "Get:41 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libfreetype6 amd64 2.11.1+dfsg-1ubuntu0.2 [389 kB]\n",
      "Get:42 http://archive.ubuntu.com/ubuntu jammy/main amd64 libfontconfig1 amd64 2.13.1-4.2ubuntu5 [131 kB]\n",
      "Get:43 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjpeg-turbo8 amd64 2.1.2-0ubuntu1 [134 kB]\n",
      "Get:44 http://archive.ubuntu.com/ubuntu jammy/main amd64 libjpeg8 amd64 8c-2ubuntu10 [2264 B]\n",
      "Get:45 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libjbig0 amd64 2.1-3.1ubuntu0.22.04.1 [29.2 kB]\n",
      "Get:46 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libwebp7 amd64 1.2.2-2ubuntu0.22.04.2 [206 kB]\n",
      "Get:47 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libtiff5 amd64 4.3.0-6ubuntu0.10 [185 kB]\n",
      "Get:48 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libxpm4 amd64 1:3.5.12-1ubuntu0.22.04.2 [36.7 kB]\n",
      "Get:49 http://archive.ubuntu.com/ubuntu jammy/main amd64 libgd3 amd64 2.3.0-2ubuntu2 [129 kB]\n",
      "Get:50 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 nginx-common all 1.18.0-6ubuntu14.5 [40.1 kB]\n",
      "Get:51 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libnginx-mod-http-geoip2 amd64 1.18.0-6ubuntu14.5 [12.0 kB]\n",
      "Get:52 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libnginx-mod-http-image-filter amd64 1.18.0-6ubuntu14.5 [15.5 kB]\n",
      "Get:53 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.22.04.1 [164 kB]\n",
      "Get:54 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libnginx-mod-http-xslt-filter amd64 1.18.0-6ubuntu14.5 [13.8 kB]\n",
      "Get:55 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libnginx-mod-mail amd64 1.18.0-6ubuntu14.5 [45.8 kB]\n",
      "Get:56 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libnginx-mod-stream amd64 1.18.0-6ubuntu14.5 [72.8 kB]\n",
      "Get:57 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 libnginx-mod-stream-geoip2 amd64 1.18.0-6ubuntu14.5 [10.1 kB]\n",
      "Get:58 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 nginx-core amd64 1.18.0-6ubuntu14.5 [483 kB]\n",
      "Get:59 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 nginx amd64 1.18.0-6ubuntu14.5 [3882 B]\n",
      "Get:60 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-lib2to3 all 3.10.8-1~22.04 [77.6 kB]\n",
      "Get:61 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-distutils all 3.10.8-1~22.04 [139 kB]\n",
      "Get:62 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 python3-setuptools all 59.6.0-1.2ubuntu0.22.04.2 [340 kB]\n",
      "Get:63 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-wheel all 0.37.1-2ubuntu0.22.04.1 [32.0 kB]\n",
      "Get:64 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 python3-pip all 22.0.2+dfsg-1ubuntu0.4 [1305 kB]\n",
      "\u001b[91mdebconf: delaying package configuration, since apt-utils is not installed\n",
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      "(Reading database ... 4393 files and directories currently installed.)\n",
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      "Setting up libpython3.10-minimal:amd64 (3.10.12-1~22.04.6) ...\n",
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      "Setting up python3.10-minimal (3.10.12-1~22.04.6) ...\n",
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      "(Reading database ... 4697 files and directories currently installed.)\n",
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      "Setting up python3-minimal (3.10.6-1~22.04.1) ...\n",
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      "(Reading database ... 5127 files and directories currently installed.)\n",
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      "Setting up media-types (7.0.0) ...\n",
      "Setting up libxau6:amd64 (1:1.0.9-1build5) ...\n",
      "Setting up libpsl5:amd64 (0.21.0-1.2build2) ...\n",
      "Setting up wget (1.21.2-2ubuntu1.1) ...\n",
      "Setting up libmaxminddb0:amd64 (1.5.2-1build2) ...\n",
      "Setting up libbrotli1:amd64 (1.0.9-2build6) ...\n",
      "Setting up libsqlite3-0:amd64 (3.37.2-2ubuntu0.3) ...\n",
      "Setting up libdeflate0:amd64 (1.10-2) ...\n",
      "Setting up nginx-common (1.18.0-6ubuntu14.5) ...\n",
      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (TERM is not set, so the dialog frontend is not usable.)\n",
      "debconf: falling back to frontend: Readline\n",
      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (Can't locate Term/ReadLine.pm in @INC (you may need to install the Term::ReadLine module) (@INC contains: /etc/perl /usr/local/lib/x86_64-linux-gnu/perl/5.34.0 /usr/local/share/perl/5.34.0 /usr/lib/x86_64-linux-gnu/perl5/5.34 /usr/share/perl5 /usr/lib/x86_64-linux-gnu/perl-base /usr/lib/x86_64-linux-gnu/perl/5.34 /usr/share/perl/5.34 /usr/local/lib/site_perl) at /usr/share/perl5/Debconf/FrontEnd/Readline.pm line 7.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "Setting up libjbig0:amd64 (2.1-3.1ubuntu0.22.04.1) ...\n",
      "Setting up libcap2-bin (1:2.44-1ubuntu0.22.04.1) ...\n",
      "Setting up libx11-data (2:1.7.5-1ubuntu0.3) ...\n",
      "Setting up libpng16-16:amd64 (1.6.37-3build5) ...\n",
      "Setting up libmnl0:amd64 (1.0.4-3build2) ...\n",
      "Setting up fonts-dejavu-core (2.37-2build1) ...\n",
      "Setting up ucf (3.0043) ...\n",
      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (TERM is not set, so the dialog frontend is not usable.)\n",
      "debconf: falling back to frontend: Readline\n",
      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (Can't locate Term/ReadLine.pm in @INC (you may need to install the Term::ReadLine module) (@INC contains: /etc/perl /usr/local/lib/x86_64-linux-gnu/perl/5.34.0 /usr/local/share/perl/5.34.0 /usr/lib/x86_64-linux-gnu/perl5/5.34 /usr/share/perl5 /usr/lib/x86_64-linux-gnu/perl-base /usr/lib/x86_64-linux-gnu/perl/5.34 /usr/share/perl/5.34 /usr/local/lib/site_perl) at /usr/share/perl5/Debconf/FrontEnd/Readline.pm line 7.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "Setting up libjpeg-turbo8:amd64 (2.1.2-0ubuntu1) ...\n",
      "Setting up libxtables12:amd64 (1.8.7-1ubuntu5.2) ...\n",
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      "Setting up libmd0:amd64 (1.0.4-1build1) ...\n",
      "Setting up libmpdec3:amd64 (2.5.1-2build2) ...\n",
      "Setting up libnginx-mod-http-geoip2 (1.18.0-6ubuntu14.5) ...\n",
      "Setting up openssl (3.0.2-0ubuntu1.18) ...\n",
      "Setting up libbsd0:amd64 (0.11.5-1) ...\n",
      "Setting up libelf1:amd64 (0.186-1build1) ...\n",
      "Setting up readline-common (8.1.2-1) ...\n",
      "Setting up libicu70:amd64 (70.1-2) ...\n",
      "Setting up libjpeg8:amd64 (8c-2ubuntu10) ...\n",
      "Setting up libnginx-mod-mail (1.18.0-6ubuntu14.5) ...\n",
      "Setting up libxdmcp6:amd64 (1:1.1.3-0ubuntu5) ...\n",
      "Setting up libxcb1:amd64 (1.14-3ubuntu3) ...\n",
      "Setting up fontconfig-config (2.13.1-4.2ubuntu5) ...\n",
      "Setting up libreadline8:amd64 (8.1.2-1) ...\n",
      "Setting up libnginx-mod-stream (1.18.0-6ubuntu14.5) ...\n",
      "Setting up libpython3.10-stdlib:amd64 (3.10.12-1~22.04.6) ...\n",
      "Setting up ca-certificates (20240203~22.04.1) ...\n",
      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (TERM is not set, so the dialog frontend is not usable.)\n",
      "debconf: falling back to frontend: Readline\n",
      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (Can't locate Term/ReadLine.pm in @INC (you may need to install the Term::ReadLine module) (@INC contains: /etc/perl /usr/local/lib/x86_64-linux-gnu/perl/5.34.0 /usr/local/share/perl/5.34.0 /usr/lib/x86_64-linux-gnu/perl5/5.34 /usr/share/perl5 /usr/lib/x86_64-linux-gnu/perl-base /usr/lib/x86_64-linux-gnu/perl/5.34 /usr/share/perl/5.34 /usr/local/lib/site_perl) at /usr/share/perl5/Debconf/FrontEnd/Readline.pm line 7.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "Updating certificates in /etc/ssl/certs...\n",
      "146 added, 0 removed; done.\n",
      "Setting up libfreetype6:amd64 (2.11.1+dfsg-1ubuntu0.2) ...\n",
      "Setting up libx11-6:amd64 (2:1.7.5-1ubuntu0.3) ...\n",
      "Setting up libtiff5:amd64 (4.3.0-6ubuntu0.10) ...\n",
      "Setting up libfontconfig1:amd64 (2.13.1-4.2ubuntu5) ...\n",
      "Setting up libbpf0:amd64 (1:0.5.0-1ubuntu22.04.1) ...\n",
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      "debconf: unable to initialize frontend: Dialog\n",
      "debconf: (TERM is not set, so the dialog frontend is not usable.)\n",
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      "debconf: unable to initialize frontend: Readline\n",
      "debconf: (Can't locate Term/ReadLine.pm in @INC (you may need to install the Term::ReadLine module) (@INC contains: /etc/perl /usr/local/lib/x86_64-linux-gnu/perl/5.34.0 /usr/local/share/perl/5.34.0 /usr/lib/x86_64-linux-gnu/perl5/5.34 /usr/share/perl5 /usr/lib/x86_64-linux-gnu/perl-base /usr/lib/x86_64-linux-gnu/perl/5.34 /usr/share/perl/5.34 /usr/local/lib/site_perl) at /usr/share/perl5/Debconf/FrontEnd/Readline.pm line 7.)\n",
      "debconf: falling back to frontend: Teletype\n",
      "Setting up python3 (3.10.6-1~22.04.1) ...\n",
      "running python rtupdate hooks for python3.10...\n",
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      "Setting up libnginx-mod-http-image-filter (1.18.0-6ubuntu14.5) ...\n",
      "Setting up python3-wheel (0.37.1-2ubuntu0.22.04.1) ...\n",
      "Setting up libnginx-mod-http-xslt-filter (1.18.0-6ubuntu14.5) ...\n",
      "Setting up python3-pip (22.0.2+dfsg-1ubuntu0.4) ...\n",
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      "invoke-rc.d: could not determine current runlevel\n",
      "invoke-rc.d: policy-rc.d denied execution of start.\n",
      "Setting up nginx (1.18.0-6ubuntu14.5) ...\n",
      "Processing triggers for libc-bin (2.35-0ubuntu3.8) ...\n",
      "Processing triggers for ca-certificates (20240203~22.04.1) ...\n",
      "Updating certificates in /etc/ssl/certs...\n",
      "0 added, 0 removed; done.\n",
      "Running hooks in /etc/ca-certificates/update.d...\n",
      "done.\n",
      " ---> Removed intermediate container ea68ab312b2a\n",
      " ---> 6ffc6f5f6b17\n",
      "Step 4/11 : RUN ln -s /usr/bin/python3 /usr/bin/python\n",
      " ---> Running in c787891ff5ce\n",
      " ---> Removed intermediate container c787891ff5ce\n",
      " ---> ef7d38ae941c\n",
      "Step 5/11 : RUN pip --no-cache-dir install scikit-learn==1.1.0 pandas flask gunicorn numpy==1.26.4\n",
      " ---> Running in 6a8b23d9d2d7\n",
      "Collecting scikit-learn==1.1.0\n",
      "  Downloading scikit_learn-1.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30.7 MB)\n",
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      "  Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)\n",
      "Installing collected packages: pytz, tzdata, threadpoolctl, six, packaging, numpy, MarkupSafe, joblib, itsdangerous, click, blinker, Werkzeug, scipy, python-dateutil, Jinja2, gunicorn, scikit-learn, pandas, flask\n",
      "Successfully installed Jinja2-3.1.4 MarkupSafe-3.0.2 Werkzeug-3.0.4 blinker-1.8.2 click-8.1.7 flask-3.0.3 gunicorn-23.0.0 itsdangerous-2.2.0 joblib-1.4.2 numpy-1.26.4 packaging-24.1 pandas-2.2.3 python-dateutil-2.9.0.post0 pytz-2024.2 scikit-learn-1.1.0 scipy-1.14.1 six-1.16.0 threadpoolctl-3.5.0 tzdata-2024.2\n",
      "\u001b[91mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n",
      "\u001b[0m ---> Removed intermediate container 6a8b23d9d2d7\n",
      " ---> 133cb7542f73\n",
      "Step 6/11 : ENV PYTHONUNBUFFERED=TRUE\n",
      " ---> Running in e6fdf340420b\n",
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      " ---> 97f7786e9b2d\n",
      "Step 7/11 : ENV PYTHONDONTWRITEBYTECODE=TRUE\n",
      " ---> Running in c7a7e5331ff0\n",
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      " ---> f8820e451572\n",
      "Step 8/11 : ENV PATH=\"/opt/program:${PATH}\"\n",
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      " ---> 5c3ae7dddee7\n",
      "Step 9/11 : COPY decision_trees /opt/program\n",
      " ---> ed4f607b6c4d\n",
      "Step 10/11 : WORKDIR /opt/program\n",
      " ---> Running in f542e8fedd5a\n",
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      " ---> 120e9fc51291\n",
      "Successfully built 120e9fc51291\n",
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      "The push refers to repository [969223424492.dkr.ecr.us-west-2.amazonaws.com/sagemaker-decision-trees]\n",
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      "latest: digest: sha256:ab2e7800c9dec60cbb13d30165c77a0318ed2a6580cd36202270631c6fef90fc size: 1369\n"
     ]
    }
   ],
   "source": [
    "%%sh\n",
    "# Login to ECR\n",
    "aws --region ${AWS_DEFAULT_REGION} ecr get-login-password | docker login --username AWS --password-stdin ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_DEFAULT_REGION}.amazonaws.com/sagemaker-decision-trees\n",
    "\n",
    "# If the repository doesn't exist in ECR, create it.\n",
    "aws ecr describe-repositories --repository-names \"sagemaker-decision-trees\" > /dev/null 2>&1\n",
    "\n",
    "if [ $? -ne 0 ]\n",
    "then\n",
    "    aws ecr create-repository --repository-name \"sagemaker-decision-trees\" > /dev/null\n",
    "fi\n",
    "\n",
    "cd lab03_container\n",
    "\n",
    "chmod +x decision_trees/train\n",
    "chmod +x decision_trees/serve\n",
    "\n",
    "# Build the image - it might take a few minutes to complete this step\n",
    "docker build --network sagemaker . -t ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_DEFAULT_REGION}.amazonaws.com/sagemaker-decision-trees:latest\n",
    "# Push the image to ECR\n",
    "docker push ${AWS_ACCOUNT_ID}.dkr.ecr.${AWS_DEFAULT_REGION}.amazonaws.com/sagemaker-decision-trees:latest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup & Upload Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup the Environment \n",
    "Here we specify a bucket to use and the role that will be used for working with SageMaker.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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/sagemaker-user/.config/sagemaker/config.yaml\n"
     ]
    }
   ],
   "source": [
    "# cell 03\n",
    "\n",
    "S3_prefix = \"DEMO-scikit-byo-iris\"\n",
    "\n",
    "# Define IAM role\n",
    "import boto3\n",
    "import re\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The session remembers our connection parameters to SageMaker. We’ll use it to perform all of our SageMaker operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 04\n",
    "\n",
    "import sagemaker as sage\n",
    "from time import gmtime, strftime\n",
    "\n",
    "sess = sage.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload data to S3 Bucket"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When training large models with huge amounts of data, you’ll typically use big data tools, like Amazon Athena, AWS Glue, or Amazon EMR, to create your data in S3. For the purposes of this example, we’re using some the [classic Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set) in the `lab03_data` directory. \n",
    "\n",
    "We can use use the tools provided by the [SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/) to upload the data to a default bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 05\n",
    "\n",
    "WORK_DIRECTORY = \"lab03_data\"\n",
    "\n",
    "data_location = sess.upload_data(WORK_DIRECTORY, key_prefix=S3_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to use SageMaker to fit our algorithm, we create an [`estimator`](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html) that defines how to use the container to train. This includes the configuration we need to invoke SageMaker training:\n",
    "\n",
    "- `image_uri (str)` - The [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/) path where the docker image is registered. This is constructed in the shell commands in *cell 06*.\n",
    "- `role (str)` - SageMaker IAM role as obtained above in *cell 03*.\n",
    "- `instance_count (int)` - number of machines to use for training.\n",
    "- `instance_type (str)` - the type of machine to use for training.\n",
    "- `output_path (str)` - where the model artifact will be written.\n",
    "- `sagemaker_session (sagemaker.session.Session)` - the SageMaker session object that we defined in *cell 04*.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we use `estimator.fit()` method to train against the data that we uploaded.\n",
    "The API calls the Amazon SageMaker `CreateTrainingJob` API to start model training. The API uses configuration you provided to create the `estimator` and the specified input training data to send the `CreatingTrainingJob` request to Amazon SageMaker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: sagemaker-decision-trees-2024-10-23-15-59-10-299\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-10-23 15:59:11 Starting - Starting the training job...\n",
      "2024-10-23 15:59:24 Starting - Preparing the instances for training...\n",
      "2024-10-23 16:00:10 Downloading - Downloading the training image\n",
      "2024-10-23 16:00:10 Training - Training image download completed. Training in progress..\u001b[34mStarting the training.\u001b[0m\n",
      "\u001b[34mTraining complete.\u001b[0m\n",
      "\n",
      "2024-10-23 16:00:28 Uploading - Uploading generated training model\n",
      "2024-10-23 16:00:28 Completed - Training job completed\n",
      "Training seconds: 34\n",
      "Billable seconds: 34\n"
     ]
    }
   ],
   "source": [
    "# cell 06\n",
    "\n",
    "account = sess.boto_session.client(\"sts\").get_caller_identity()[\"Account\"]\n",
    "region = sess.boto_session.region_name\n",
    "image_uri = \"{}.dkr.ecr.{}.amazonaws.com/sagemaker-decision-trees:latest\".format(account, region)\n",
    "\n",
    "tree = sage.estimator.Estimator(\n",
    "    image_uri,\n",
    "    role,\n",
    "    instance_count=1,\n",
    "    instance_type=\"ml.c4.2xlarge\",\n",
    "    output_path=\"s3://{}/output\".format(sess.default_bucket()),\n",
    "    sagemaker_session=sess,\n",
    ")\n",
    "\n",
    "file_location = data_location + \"/iris.csv\"\n",
    "tree.fit(file_location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Deployment\n",
    "You can use a trained model to get real time predictions using HTTP endpoint. Follow these steps to walk you through the process.\n",
    "\n",
    "After the model training successfully completes, you can call the [`estimator.deploy()` method](https://sagemaker.readthedocs.io/en/stable/estimators.html#sagemaker.estimator.Estimator.deploy). The `deploy()` method creates a deployable model, configures the SageMaker hosting services endpoint, and launches the endpoint to host the model. \n",
    "\n",
    "The method uses the following configurations:\n",
    "- `initial_instance_count (int)` – The number of instances to deploy the model.\n",
    "- `instance_type (str)` – The type of instances that you want to operate your deployed model.\n",
    "- `serializer (int)` – Serialize input data of various formats (a NumPy array, list, file, or buffer) to a CSV-formatted string in this example. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: sagemaker-decision-trees-2024-10-23-16-02-18-294\n",
      "INFO:sagemaker:Creating endpoint-config with name sagemaker-decision-trees-2024-10-23-16-02-18-294\n",
      "INFO:sagemaker:Creating endpoint with name sagemaker-decision-trees-2024-10-23-16-02-18-294\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----!"
     ]
    }
   ],
   "source": [
    "# cell 07\n",
    "\n",
    "from sagemaker.serializers import CSVSerializer\n",
    "\n",
    "predictor = tree.deploy(\n",
    "    initial_instance_count=1, instance_type=\"ml.m4.xlarge\", serializer=CSVSerializer()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run Inferences\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing test data\n",
    "In order to do some predictions, we’ll extract some of the data we used for training and do predictions against it. This is, of course, bad statistical practice, but an easy way to see how the mechanism works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://sagemaker-us-west-2-969223424492/DEMO-scikit-byo-iris/iris.csv\n"
     ]
    }
   ],
   "source": [
    "print(file_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 08\n",
    "import awswrangler as wr\n",
    "\n",
    "shape = wr.s3.read_csv(file_location, header=None)\n",
    "\n",
    "# shape=pd.read_csv(file_location, header=None)\n",
    "shape.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 09\n",
    "\n",
    "# drop the label column in the training set\n",
    "shape.drop(shape.columns[[0]], axis=1, inplace=True)\n",
    "shape.sample(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 10\n",
    "\n",
    "import itertools\n",
    "\n",
    "a = [50 * i for i in range(3)]\n",
    "b = [40 + i for i in range(10)]\n",
    "indices = [i + j for i, j in itertools.product(a, b)]\n",
    "\n",
    "test_data = shape.iloc[indices[:-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions\n",
    "\n",
    "Prediction is as easy as calling `predict` with the `predictor` we got back from `deploy` and the data we want to do predictions with. The serializers take care of doing the data conversions for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 11\n",
    "\n",
    "print(predictor.predict(test_data.values).decode(\"utf-8\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup\n",
    "After completing the lab, use these steps to [delete the endpoint through AWS Console](https://docs.aws.amazon.com/sagemaker/latest/dg/ex1-cleanup.html) or simply run the following code\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cell 12\n",
    "sess.delete_endpoint(predictor.endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the container artifacts and data we downloaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# cell 13\n",
    "!rm -rf lab03_container lab03_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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