1_model_training/1_train_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e1cd147",
   "metadata": {},
   "source": [
    "# Tensorflow Object Detection API and AWS Sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85592c17",
   "metadata": {},
   "source": [
    "In this notebook, you will train and evaluate different models using the [Tensorflow Object Detection API](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/) and [AWS Sagemaker](https://aws.amazon.com/sagemaker/). \n",
    "\n",
    "If you ever feel stuck, you can refer to this [tutorial](https://aws.amazon.com/blogs/machine-learning/training-and-deploying-models-using-tensorflow-2-with-the-object-detection-api-on-amazon-sagemaker/).\n",
    "\n",
    "## Dataset\n",
    "\n",
    "We are using the [Waymo Open Dataset](https://waymo.com/open/) for this project. The dataset has already been exported using the tfrecords format. The files have been created following the format described [here](https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html#create-tensorflow-records). You can find data stored on [AWS S3](https://aws.amazon.com/s3/), AWS Object Storage. The images are saved with a resolution of 640x640."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcc1d114",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%pip install tensorflow_io sagemaker -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96f55350",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sagemaker\n",
    "from sagemaker.estimator import Estimator\n",
    "from framework import CustomFramework"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccde6fd1",
   "metadata": {},
   "source": [
    "Save the IAM role in a variable called `role`. This would be useful when training the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ab6b13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arn:aws:iam::476375380884:role/service-role/AmazonSageMaker-ExecutionRole-20230227T161354\n"
     ]
    }
   ],
   "source": [
    "role = sagemaker.get_execution_role()\n",
    "print(role)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae64e29f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The train and val paths below are public S3 buckets created by Udacity for this project\n",
    "inputs = {'train': 's3://cd2688-object-detection-tf2/train/', \n",
    "        'val': 's3://cd2688-object-detection-tf2/val/'} \n",
    "\n",
    "# Insert path of a folder in your personal S3 bucket to store tensorboard logs.\n",
    "tensorboard_s3_prefix = 's3://object-detection-project-jckuri/logs/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc16a825",
   "metadata": {},
   "source": [
    "## Container\n",
    "\n",
    "To train the model, you will first need to build a [docker](https://www.docker.com/) container with all the dependencies required by the TF Object Detection API. The code below does the following:\n",
    "* clone the Tensorflow models repository\n",
    "* get the exporter and training scripts from the the repository\n",
    "* build the docker image and push it \n",
    "* print the container name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ad5ac8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fatal: destination path 'docker/models' already exists and is not an empty directory.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "# clone the repo and get the scripts\n",
    "git clone https://github.com/tensorflow/models.git docker/models\n",
    "\n",
    "# get model_main and exporter_main files from TF2 Object Detection GitHub repository\n",
    "cp docker/models/research/object_detection/exporter_main_v2.py source_dir \n",
    "cp docker/models/research/object_detection/model_main_tf2.py source_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2dab3f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING! Your password will be stored unencrypted in /home/ec2-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",
      "Login Succeeded\n",
      "Building image with name tf2-object-detection\n",
      "Sending build context to Docker daemon  722.9MB\n",
      "Step 1/17 : FROM tensorflow/tensorflow:2.9.0-gpu\n",
      " ---> c8d9ee2a0ff4\n",
      "Step 2/17 : ARG DEBIAN_FRONTEND=noninteractive\n",
      " ---> Running in 2d451099c387\n",
      "Removing intermediate container 2d451099c387\n",
      " ---> 53733a625ce4\n",
      "Step 3/17 : RUN rm /etc/apt/sources.list.d/cuda.list\n",
      " ---> Running in d92622d896f8\n",
      "Removing intermediate container d92622d896f8\n",
      " ---> 1ce76e865cc5\n",
      "Step 4/17 : RUN apt-key del 7fa2af80\n",
      " ---> Running in 6065b7cdf337\n",
      "OK\n",
      "Removing intermediate container 6065b7cdf337\n",
      " ---> 0740919dd8ae\n",
      "Step 5/17 : RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub\n",
      " ---> Running in 52d41e1bfae2\n",
      "\u001b[91mWarning: apt-key output should not be parsed (stdout is not a terminal)\n",
      "\u001b[0mExecuting: /tmp/apt-key-gpghome.KvH31gNyHJ/gpg.1.sh --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub\n",
      "\u001b[91mgpg: requesting key from 'https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub'\n",
      "\u001b[0m\u001b[91mgpg: key A4B469963BF863CC: \"cudatools <cudatools@nvidia.com>\" not changed\n",
      "gpg: Total number processed: 1\n",
      "gpg:              unchanged: 1\n",
      "\u001b[0mRemoving intermediate container 52d41e1bfae2\n",
      " ---> c9f25c2bf288\n",
      "Step 6/17 : RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub\n",
      " ---> Running in 501d6bef9a4f\n",
      "\u001b[91mWarning: apt-key output should not be parsed (stdout is not a terminal)\n",
      "\u001b[0mExecuting: /tmp/apt-key-gpghome.eqj2SRXKxx/gpg.1.sh --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub\n",
      "\u001b[91mgpg: requesting key from 'https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub'\n",
      "\u001b[0m\u001b[91mgpg: key F60F4B3D7FA2AF80: public key \"cudatools <cudatools@nvidia.com>\" imported\n",
      "\u001b[0m\u001b[91mgpg: Total number processed: 1\n",
      "gpg:               imported: 1\n",
      "\u001b[0mRemoving intermediate container 501d6bef9a4f\n",
      " ---> b7bf7b57746f\n",
      "Step 7/17 : RUN apt-get update && apt-get install -y     git     gpg-agent     python3-cairocffi     protobuf-compiler     python3-pil     python3-lxml     python3-tk     libgl1-mesa-dev     wget\n",
      " ---> Running in d4f61dd5b722\n",
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      "  libegl-mesa0 libegl1 liberror-perl libfido2-1 libfontconfig1 libgbm1\n",
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      "  libgl1 libgl1-mesa-dri libglapi-mesa libgles-dev libgles1 libgles2\n",
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      "  libpixman-1-0 libprotobuf-dev libprotobuf-lite17 libprotobuf17 libprotoc17\n",
      "  libpthread-stubs0-dev libsensors-config libsensors5 libtcl8.6 libtiff5\n",
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      "Suggested packages:\n",
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      "  | git-daemon-sysvinit git-doc git-el git-email git-gui gitk gitweb git-cvs\n",
      "  git-mediawiki git-svn parcimonie xloadimage scdaemon liblcms2-utils pciutils\n",
      "  lm-sensors tcl8.6 tk8.6 libx11-doc libxcb-doc keychain libpam-ssh\n",
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      "  libjbig0 liblcms2-2 libllvm12 libopengl-dev libopengl0 libpciaccess0\n",
      "  libpixman-1-0 libprotobuf-dev libprotobuf-lite17 libprotobuf17 libprotoc17\n",
      "  libpthread-stubs0-dev libsensors-config libsensors5 libtcl8.6 libtiff5\n",
      "  libtk8.6 libvulkan1 libwayland-client0 libwayland-server0 libwebp6\n",
      "  libwebpdemux2 libwebpmux3 libx11-6 libx11-data libx11-dev libx11-xcb1\n",
      "  libxau-dev libxau6 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0\n",
      "  libxcb-randr0 libxcb-render0 libxcb-shm0 libxcb-sync1 libxcb-xfixes0 libxcb1\n",
      "  libxcb1-dev libxdmcp-dev libxdmcp6 libxext6 libxfixes3 libxft2 libxmuu1\n",
      "  libxrender1 libxshmfence1 libxslt1.1 libxss1 libxxf86vm1 mesa-vulkan-drivers\n",
      "  openssh-client protobuf-compiler python3-bs4 python3-cairocffi python3-cffi\n",
      "  python3-cffi-backend python3-html5lib python3-lxml python3-olefile\n",
      "  python3-pil python3-ply python3-pycparser python3-soupsieve python3-tk\n",
      "  python3-webencodings python3-xcffib shared-mime-info tk8.6-blt2.5 ucf wget\n",
      "  x11-common x11proto-core-dev x11proto-dev xauth xorg-sgml-doctools\n",
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      "Get:53 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-intel1 amd64 2.4.107-8ubuntu1~20.04.2 [60.3 kB]\n",
      "Get:54 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-nouveau2 amd64 2.4.107-8ubuntu1~20.04.2 [16.6 kB]\n",
      "Get:55 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libdrm-radeon1 amd64 2.4.107-8ubuntu1~20.04.2 [19.7 kB]\n",
      "Get:56 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd0 amd64 1.3.2-1~ubuntu0.20.04.2 [48.1 kB]\n",
      "Get:57 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-server0 amd64 1.18.0-1ubuntu0.1 [31.3 kB]\n",
      "Get:58 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgbm1 amd64 21.2.6-0ubuntu0.1~20.04.2 [29.2 kB]\n",
      "Get:59 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglapi-mesa amd64 21.2.6-0ubuntu0.1~20.04.2 [27.4 kB]\n",
      "Get:60 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwayland-client0 amd64 1.18.0-1ubuntu0.1 [23.9 kB]\n",
      "Get:61 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-xcb1 amd64 2:1.6.9-2ubuntu1.2 [9372 B]\n",
      "Get:62 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri2-0 amd64 1.14-2 [6920 B]\n",
      "Get:63 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-dri3-0 amd64 1.14-2 [6552 B]\n",
      "Get:64 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-present0 amd64 1.14-2 [5560 B]\n",
      "Get:65 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-sync1 amd64 1.14-2 [8884 B]\n",
      "Get:66 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-xfixes0 amd64 1.14-2 [9296 B]\n",
      "Get:67 http://archive.ubuntu.com/ubuntu focal/main amd64 libxshmfence1 amd64 1.3-1 [5028 B]\n",
      "Get:68 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libegl-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [96.3 kB]\n",
      "Get:69 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libegl1 amd64 1.3.2-1~ubuntu0.20.04.2 [31.9 kB]\n",
      "Get:70 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-glx0 amd64 1.14-2 [22.1 kB]\n",
      "Get:71 http://archive.ubuntu.com/ubuntu focal/main amd64 libxfixes3 amd64 1:5.0.3-2 [10.9 kB]\n",
      "Get:72 http://archive.ubuntu.com/ubuntu focal/main amd64 libxxf86vm1 amd64 1:1.1.4-1build1 [10.2 kB]\n",
      "Get:73 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libllvm12 amd64 1:12.0.0-3ubuntu1~20.04.5 [18.8 MB]\n",
      "Get:74 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors-config all 1:3.6.0-2ubuntu1.1 [6052 B]\n",
      "Get:75 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libsensors5 amd64 1:3.6.0-2ubuntu1.1 [27.2 kB]\n",
      "Get:76 http://archive.ubuntu.com/ubuntu focal/main amd64 libvulkan1 amd64 1.2.131.2-1 [93.3 kB]\n",
      "Get:77 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dri amd64 21.2.6-0ubuntu0.1~20.04.2 [11.0 MB]\n",
      "Get:78 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-mesa0 amd64 21.2.6-0ubuntu0.1~20.04.2 [137 kB]\n",
      "Get:79 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx0 amd64 1.3.2-1~ubuntu0.20.04.2 [32.5 kB]\n",
      "Get:80 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1 amd64 1.3.2-1~ubuntu0.20.04.2 [85.8 kB]\n",
      "Get:81 http://archive.ubuntu.com/ubuntu focal/main amd64 xorg-sgml-doctools all 1:1.11-1 [12.9 kB]\n",
      "Get:82 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-dev all 2019.2-1ubuntu1 [594 kB]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Get:83 http://archive.ubuntu.com/ubuntu focal/main amd64 x11proto-core-dev all 2019.2-1ubuntu1 [2620 B]\n",
      "Get:84 http://archive.ubuntu.com/ubuntu focal/main amd64 libxau-dev amd64 1:1.0.9-0ubuntu1 [9552 B]\n",
      "Get:85 http://archive.ubuntu.com/ubuntu focal/main amd64 libxdmcp-dev amd64 1:1.1.3-0ubuntu1 [25.3 kB]\n",
      "Get:86 http://archive.ubuntu.com/ubuntu focal/main amd64 xtrans-dev all 1.4.0-1 [68.9 kB]\n",
      "Get:87 http://archive.ubuntu.com/ubuntu focal/main amd64 libpthread-stubs0-dev amd64 0.4-1 [5384 B]\n",
      "Get:88 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb1-dev amd64 1.14-2 [80.5 kB]\n",
      "Get:89 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libx11-dev amd64 2:1.6.9-2ubuntu1.2 [647 kB]\n",
      "Get:90 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglx-dev amd64 1.3.2-1~ubuntu0.20.04.2 [14.0 kB]\n",
      "Get:91 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl-dev amd64 1.3.2-1~ubuntu0.20.04.2 [97.8 kB]\n",
      "Get:92 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libegl-dev amd64 1.3.2-1~ubuntu0.20.04.2 [17.2 kB]\n",
      "Get:93 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libjbig0 amd64 2.1-3.1ubuntu0.20.04.1 [27.3 kB]\n",
      "Get:94 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebp6 amd64 0.6.1-2ubuntu0.20.04.1 [185 kB]\n",
      "Get:95 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libtiff5 amd64 4.1.0+git191117-2ubuntu0.20.04.8 [163 kB]\n",
      "Get:96 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgdk-pixbuf2.0-common all 2.40.0+dfsg-3ubuntu0.4 [4592 B]\n",
      "Get:97 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgdk-pixbuf2.0-0 amd64 2.40.0+dfsg-3ubuntu0.4 [168 kB]\n",
      "Get:98 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgdk-pixbuf2.0-bin amd64 2.40.0+dfsg-3ubuntu0.4 [14.1 kB]\n",
      "Get:99 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgles1 amd64 1.3.2-1~ubuntu0.20.04.2 [10.3 kB]\n",
      "Get:100 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgles2 amd64 1.3.2-1~ubuntu0.20.04.2 [15.6 kB]\n",
      "Get:101 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgles-dev amd64 1.3.2-1~ubuntu0.20.04.2 [47.9 kB]\n",
      "Get:102 http://archive.ubuntu.com/ubuntu focal/main amd64 libimagequant0 amd64 2.12.2-1.1 [31.4 kB]\n",
      "Get:103 http://archive.ubuntu.com/ubuntu focal/main amd64 liblcms2-2 amd64 2.9-4 [140 kB]\n",
      "Get:104 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libopengl0 amd64 1.3.2-1~ubuntu0.20.04.2 [29.2 kB]\n",
      "Get:105 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libopengl-dev amd64 1.3.2-1~ubuntu0.20.04.2 [3584 B]\n",
      "Get:106 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotobuf-lite17 amd64 3.6.1.3-2ubuntu5.2 [132 kB]\n",
      "Get:107 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotobuf17 amd64 3.6.1.3-2ubuntu5.2 [798 kB]\n",
      "Get:108 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotoc17 amd64 3.6.1.3-2ubuntu5.2 [646 kB]\n",
      "Get:109 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebpdemux2 amd64 0.6.1-2ubuntu0.20.04.1 [9576 B]\n",
      "Get:110 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libwebpmux3 amd64 0.6.1-2ubuntu0.20.04.1 [19.5 kB]\n",
      "Get:111 http://archive.ubuntu.com/ubuntu focal/main amd64 libxcb-randr0 amd64 1.14-2 [16.3 kB]\n",
      "Get:112 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libxslt1.1 amd64 1.1.34-4ubuntu0.20.04.1 [151 kB]\n",
      "Get:113 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 mesa-vulkan-drivers amd64 21.2.6-0ubuntu0.1~20.04.2 [5788 kB]\n",
      "Get:114 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-soupsieve all 1.9.5+dfsg-1 [29.1 kB]\n",
      "Get:115 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-bs4 all 4.8.2-1 [83.0 kB]\n",
      "Get:116 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-ply all 3.11-3ubuntu0.1 [46.3 kB]\n",
      "Get:117 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-pycparser all 2.19-1ubuntu1 [71.0 kB]\n",
      "Get:118 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-cffi all 1.14.0-1build1 [70.3 kB]\n",
      "Get:119 http://archive.ubuntu.com/ubuntu focal/universe amd64 python3-xcffib amd64 0.8.1-0.8 [61.9 kB]\n",
      "Get:120 http://archive.ubuntu.com/ubuntu focal/universe amd64 python3-cairocffi all 0.9.0-4 [67.1 kB]\n",
      "Get:121 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-webencodings all 0.5.1-1ubuntu1 [11.0 kB]\n",
      "Get:122 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-html5lib all 1.0.1-2 [84.3 kB]\n",
      "Get:123 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-lxml amd64 4.5.0-1ubuntu0.5 [1384 kB]\n",
      "Get:124 http://archive.ubuntu.com/ubuntu focal/main amd64 python3-olefile all 0.46-2 [33.7 kB]\n",
      "Get:125 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-pil amd64 7.0.0-4ubuntu0.7 [366 kB]\n",
      "Get:126 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 python3-tk amd64 3.8.10-0ubuntu1~20.04 [104 kB]\n",
      "Get:127 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libglvnd-dev amd64 1.3.2-1~ubuntu0.20.04.2 [11.6 kB]\n",
      "Get:128 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libgl1-mesa-dev amd64 21.2.6-0ubuntu0.1~20.04.2 [6420 B]\n",
      "Get:129 http://archive.ubuntu.com/ubuntu focal-updates/main amd64 libprotobuf-dev amd64 3.6.1.3-2ubuntu5.2 [1156 kB]\n",
      "Get:130 http://archive.ubuntu.com/ubuntu focal-updates/universe amd64 protobuf-compiler amd64 3.6.1.3-2ubuntu5.2 [27.6 kB]\n",
      "\u001b[91mdebconf: delaying package configuration, since apt-utils is not installed\n",
      "\u001b[0mFetched 59.5 MB in 10s (5836 kB/s)\n",
      "(Reading database ... 19687 files and directories currently installed.)\n",
      "Preparing to unpack .../00-gpg-wks-client_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg-wks-client (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../01-dirmngr_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking dirmngr (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../02-gpg-wks-server_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg-wks-server (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../03-gnupg-utils_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gnupg-utils (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../04-gpg-agent_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg-agent (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../05-gpg_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpg (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../06-gpgconf_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpgconf (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../07-gnupg-l10n_2.2.19-3ubuntu2.2_all.deb ...\n",
      "Unpacking gnupg-l10n (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../08-gnupg_2.2.19-3ubuntu2.2_all.deb ...\n",
      "Unpacking gnupg (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../09-gpgsm_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpgsm (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Preparing to unpack .../10-gpgv_2.2.19-3ubuntu2.2_amd64.deb ...\n",
      "Unpacking gpgv (2.2.19-3ubuntu2.2) over (2.2.19-3ubuntu2.1) ...\n",
      "Setting up gpgv (2.2.19-3ubuntu2.2) ...\n",
      "Selecting previously unselected package less.\n",
      "(Reading database ... 19687 files and directories currently installed.)\n",
      "Preparing to unpack .../000-less_551-1ubuntu0.1_amd64.deb ...\n",
      "Unpacking less (551-1ubuntu0.1) ...\n",
      "Selecting previously unselected package libbsd0:amd64.\n",
      "Preparing to unpack .../001-libbsd0_0.10.0-1_amd64.deb ...\n",
      "Unpacking libbsd0:amd64 (0.10.0-1) ...\n",
      "Selecting previously unselected package python3-cffi-backend.\n",
      "Preparing to unpack .../002-python3-cffi-backend_1.14.0-1build1_amd64.deb ...\n",
      "Unpacking python3-cffi-backend (1.14.0-1build1) ...\n",
      "Selecting previously unselected package shared-mime-info.\n",
      "Preparing to unpack .../003-shared-mime-info_1.15-1_amd64.deb ...\n",
      "Unpacking shared-mime-info (1.15-1) ...\n",
      "Selecting previously unselected package ucf.\n",
      "Preparing to unpack .../004-ucf_3.0038+nmu1_all.deb ...\n",
      "Moving old data out of the way\n",
      "Unpacking ucf (3.0038+nmu1) ...\n",
      "Selecting previously unselected package libcbor0.6:amd64.\n",
      "Preparing to unpack .../005-libcbor0.6_0.6.0-0ubuntu1_amd64.deb ...\n",
      "Unpacking libcbor0.6:amd64 (0.6.0-0ubuntu1) ...\n",
      "Selecting previously unselected package libdrm-common.\n",
      "Preparing to unpack .../006-libdrm-common_2.4.107-8ubuntu1~20.04.2_all.deb ...\n",
      "Unpacking libdrm-common (2.4.107-8ubuntu1~20.04.2) ...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selecting previously unselected package libdrm2:amd64.\n",
      "Preparing to unpack .../007-libdrm2_2.4.107-8ubuntu1~20.04.2_amd64.deb ...\n",
      "Unpacking libdrm2:amd64 (2.4.107-8ubuntu1~20.04.2) ...\n",
      "Selecting previously unselected package libedit2:amd64.\n",
      "Preparing to unpack .../008-libedit2_3.1-20191231-1_amd64.deb ...\n",
      "Unpacking libedit2:amd64 (3.1-20191231-1) ...\n",
      "Selecting previously unselected package libfido2-1:amd64.\n",
      "Preparing to unpack .../009-libfido2-1_1.3.1-1ubuntu2_amd64.deb ...\n",
      "Unpacking libfido2-1:amd64 (1.3.1-1ubuntu2) ...\n",
      "Selecting previously unselected package libxau6:amd64.\n",
      "Preparing to unpack .../010-libxau6_1%3a1.0.9-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxau6:amd64 (1:1.0.9-0ubuntu1) ...\n",
      "Selecting previously unselected package libxdmcp6:amd64.\n",
      "Preparing to unpack .../011-libxdmcp6_1%3a1.1.3-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxdmcp6:amd64 (1:1.1.3-0ubuntu1) ...\n",
      "Selecting previously unselected package libxcb1:amd64.\n",
      "Preparing to unpack .../012-libxcb1_1.14-2_amd64.deb ...\n",
      "Unpacking libxcb1:amd64 (1.14-2) ...\n",
      "Selecting previously unselected package libx11-data.\n",
      "Preparing to unpack .../013-libx11-data_2%3a1.6.9-2ubuntu1.2_all.deb ...\n",
      "Unpacking libx11-data (2:1.6.9-2ubuntu1.2) ...\n",
      "Selecting previously unselected package libx11-6:amd64.\n",
      "Preparing to unpack .../014-libx11-6_2%3a1.6.9-2ubuntu1.2_amd64.deb ...\n",
      "Unpacking libx11-6:amd64 (2:1.6.9-2ubuntu1.2) ...\n",
      "Selecting previously unselected package libxext6:amd64.\n",
      "Preparing to unpack .../015-libxext6_2%3a1.3.4-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxext6:amd64 (2:1.3.4-0ubuntu1) ...\n",
      "Selecting previously unselected package libxmuu1:amd64.\n",
      "Preparing to unpack .../016-libxmuu1_2%3a1.1.3-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxmuu1:amd64 (2:1.1.3-0ubuntu1) ...\n",
      "Selecting previously unselected package openssh-client.\n",
      "Preparing to unpack .../017-openssh-client_1%3a8.2p1-4ubuntu0.5_amd64.deb ...\n",
      "Unpacking openssh-client (1:8.2p1-4ubuntu0.5) ...\n",
      "Selecting previously unselected package wget.\n",
      "Preparing to unpack .../018-wget_1.20.3-1ubuntu2_amd64.deb ...\n",
      "Unpacking wget (1.20.3-1ubuntu2) ...\n",
      "Selecting previously unselected package xauth.\n",
      "Preparing to unpack .../019-xauth_1%3a1.1-0ubuntu1_amd64.deb ...\n",
      "Unpacking xauth (1:1.1-0ubuntu1) ...\n",
      "Selecting previously unselected package libtcl8.6:amd64.\n",
      "Preparing to unpack .../020-libtcl8.6_8.6.10+dfsg-1_amd64.deb ...\n",
      "Unpacking libtcl8.6:amd64 (8.6.10+dfsg-1) ...\n",
      "Selecting previously unselected package fonts-dejavu-core.\n",
      "Preparing to unpack .../021-fonts-dejavu-core_2.37-1_all.deb ...\n",
      "Unpacking fonts-dejavu-core (2.37-1) ...\n",
      "Selecting previously unselected package fontconfig-config.\n",
      "Preparing to unpack .../022-fontconfig-config_2.13.1-2ubuntu3_all.deb ...\n",
      "Unpacking fontconfig-config (2.13.1-2ubuntu3) ...\n",
      "Selecting previously unselected package libfontconfig1:amd64.\n",
      "Preparing to unpack .../023-libfontconfig1_2.13.1-2ubuntu3_amd64.deb ...\n",
      "Unpacking libfontconfig1:amd64 (2.13.1-2ubuntu3) ...\n",
      "Selecting previously unselected package libxrender1:amd64.\n",
      "Preparing to unpack .../024-libxrender1_1%3a0.9.10-1_amd64.deb ...\n",
      "Unpacking libxrender1:amd64 (1:0.9.10-1) ...\n",
      "Selecting previously unselected package libxft2:amd64.\n",
      "Preparing to unpack .../025-libxft2_2.3.3-0ubuntu1_amd64.deb ...\n",
      "Unpacking libxft2:amd64 (2.3.3-0ubuntu1) ...\n",
      "Selecting previously unselected package x11-common.\n",
      "Preparing to unpack .../026-x11-common_1%3a7.7+19ubuntu14_all.deb ...\n",
      "dpkg-query: no packages found matching nux-tools\n",
      "Unpacking x11-common (1:7.7+19ubuntu14) ...\n",
      "Selecting previously unselected package libxss1:amd64.\n",
      "Preparing to unpack .../027-libxss1_1%3a1.2.3-1_amd64.deb ...\n",
      "Unpacking libxss1:amd64 (1:1.2.3-1) ...\n",
      "Selecting previously unselected package libtk8.6:amd64.\n",
      "Preparing to unpack .../028-libtk8.6_8.6.10-1_amd64.deb ...\n",
      "Unpacking libtk8.6:amd64 (8.6.10-1) ...\n",
      "Selecting previously unselected package tk8.6-blt2.5.\n",
      "Preparing to unpack .../029-tk8.6-blt2.5_2.5.3+dfsg-4_amd64.deb ...\n",
      "Unpacking tk8.6-blt2.5 (2.5.3+dfsg-4) ...\n",
      "Selecting previously unselected package blt.\n",
      "Preparing to unpack .../030-blt_2.5.3+dfsg-4_amd64.deb ...\n",
      "Unpacking blt (2.5.3+dfsg-4) ...\n",
      "Selecting previously unselected package libcurl3-gnutls:amd64.\n",
      "Preparing to unpack .../031-libcurl3-gnutls_7.68.0-1ubuntu2.18_amd64.deb ...\n",
      "Unpacking libcurl3-gnutls:amd64 (7.68.0-1ubuntu2.18) ...\n",
      "Selecting previously unselected package liberror-perl.\n",
      "Preparing to unpack .../032-liberror-perl_0.17029-1_all.deb ...\n",
      "Unpacking liberror-perl (0.17029-1) ...\n",
      "Selecting previously unselected package git-man.\n",
      "Preparing to unpack .../033-git-man_1%3a2.25.1-1ubuntu3.10_all.deb ...\n",
      "Unpacking git-man (1:2.25.1-1ubuntu3.10) ...\n",
      "Selecting previously unselected package git.\n",
      "Preparing to unpack .../034-git_1%3a2.25.1-1ubuntu3.10_amd64.deb ...\n",
      "Unpacking git (1:2.25.1-1ubuntu3.10) ...\n",
      "Selecting previously unselected package libpixman-1-0:amd64.\n",
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     ]
    },
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Setting up libwayland-server0:amd64 (1.18.0-1ubuntu0.1) ...\n",
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      "Setting up libegl-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
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      "Setting up libgles-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
      "Setting up libglvnd-dev:amd64 (1.3.2-1~ubuntu0.20.04.2) ...\n",
      "Setting up libgl1-mesa-dev:amd64 (21.2.6-0ubuntu0.1~20.04.2) ...\n",
      "Processing triggers for libc-bin (2.31-0ubuntu9.7) ...\n",
      "Processing triggers for systemd (245.4-4ubuntu3.17) ...\n",
      "Processing triggers for mime-support (3.64ubuntu1) ...\n",
      "Removing intermediate container d4f61dd5b722\n",
      " ---> 92ab9528d324\n",
      "Step 8/17 : COPY models/research/object_detection /home/tensorflow/models/research/object_detection\n",
      " ---> f39ff6ddb69d\n",
      "Step 9/17 : RUN (cd /home/tensorflow/models/research/ && protoc object_detection/protos/*.proto --python_out=.)\n",
      " ---> Running in e5319a863b7c\n",
      "Removing intermediate container e5319a863b7c\n",
      " ---> 6186d300a034\n",
      "Step 10/17 : WORKDIR /home/tensorflow/models/research/\n",
      " ---> Running in d7d082e7e862\n",
      "Removing intermediate container d7d082e7e862\n",
      " ---> b79b59529482\n",
      "Step 11/17 : RUN cp object_detection/packages/tf2/setup.py ./\n",
      " ---> Running in 64db74757e9f\n",
      "Removing intermediate container 64db74757e9f\n",
      " ---> 9204b5a47867\n",
      "Step 12/17 : ENV PATH=\"/home/tensorflow/.local/bin:${PATH}\"\n",
      " ---> Running in 3891fdeb3577\n",
      "Removing intermediate container 3891fdeb3577\n",
      " ---> 01efd1243abf\n",
      "Step 13/17 : RUN python -m pip install -U pip\n",
      " ---> Running in 391a2bfe70c1\n",
      "Collecting pip\n",
      "  Downloading pip-23.0.1-py3-none-any.whl (2.1 MB)\n",
      "Installing collected packages: pip\n",
      "  Attempting uninstall: pip\n",
      "    Found existing installation: pip 20.2.4\n",
      "    Uninstalling pip-20.2.4:\n",
      "      Successfully uninstalled pip-20.2.4\n",
      "Successfully installed pip-23.0.1\n",
      "Removing intermediate container 391a2bfe70c1\n",
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      "Step 14/17 : RUN python -m pip install .\n",
      " ---> Running in 9cdd237c2617\n",
      "Processing /home/tensorflow/models/research\n",
      "  Preparing metadata (setup.py): started\n",
      "  Preparing metadata (setup.py): finished with status 'done'\n",
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      "  Installing build dependencies: started\n",
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      "  Preparing metadata (pyproject.toml): started\n",
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    },
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     "name": "stdout",
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     "text": [
      "  Preparing metadata (setup.py): finished with status 'done'\n",
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      "Building wheels for collected packages: object-detection, avro-python3, pycocotools, crcmod, dill, kaggle, seqeval, docopt, promise\n",
      "  Building wheel for object-detection (setup.py): started\n",
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      "  Building wheel for avro-python3 (setup.py): finished with status 'done'\n",
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      "  Building wheel for promise (setup.py): started\n",
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      "  Created wheel for promise: filename=promise-2.3-py3-none-any.whl size=21485 sha256=14892ed60025bd3b739f39022d218bace8d28289c4eb02d862dfaa6c6d12912d\n",
      "  Stored in directory: /root/.cache/pip/wheels/54/aa/01/724885182f93150035a2a91bce34a12877e8067a97baaf5dc8\n",
      "Successfully built object-detection avro-python3 pycocotools crcmod dill kaggle seqeval docopt promise\n",
      "Installing collected packages: text-unidecode, sentencepiece, pytz, py-cpuinfo, gin-config, flatbuffers, docopt, dm-tree, crcmod, zstandard, uritemplate, typing-extensions, tqdm, toml, threadpoolctl, tensorflow-model-optimization, tensorflow-io-gcs-filesystem, tensorflow-estimator, tabulate, scipy, regex, pyyaml, python-slugify, python-dateutil, pyparsing, pymongo, pyarrow, psutil, protobuf, promise, portalocker, orjson, opencv-python-headless, opencv-python, objsize, kiwisolver, keras, joblib, importlib-resources, immutabledict, fonttools, fasteners, fastavro, etils, dill, Cython, cycler, contourpy, contextlib2, colorama, cloudpickle, click, charset-normalizer, avro-python3, typeguard, tf-slim, tensorflow_io, tensorflow-hub, scikit-learn, sacrebleu, requests, pydot, proto-plus, pandas, httplib2, googleapis-common-protos, google-auth, tensorflow-metadata, tensorflow-addons, seqeval, oauth2client, matplotlib, kaggle, hdfs, google-auth-httplib2, google-api-core, tensorflow-datasets, pycocotools, lvis, google-api-python-client, apache-beam, tensorboard, tensorflow, tensorflow-text, tf-models-official, object-detection\n",
      "  Attempting uninstall: flatbuffers\n",
      "    Found existing installation: flatbuffers 1.12\n",
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      "  Attempting uninstall: typing-extensions\n",
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      "    Found existing installation: pyparsing 3.0.9\n",
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      "    Found existing installation: protobuf 3.20.1\n",
      "    Uninstalling protobuf-3.20.1:\n",
      "      Successfully uninstalled protobuf-3.20.1\n",
      "  Attempting uninstall: keras\n",
      "    Found existing installation: keras 2.9.0\n",
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      "      Successfully uninstalled keras-2.9.0\n",
      "  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.22.0\n",
      "    Uninstalling requests-2.22.0:\n",
      "      Successfully uninstalled requests-2.22.0\n",
      "  Attempting uninstall: google-auth\n",
      "    Found existing installation: google-auth 2.6.6\n",
      "    Uninstalling google-auth-2.6.6:\n",
      "      Successfully uninstalled google-auth-2.6.6\n",
      "  Attempting uninstall: tensorboard\n",
      "    Found existing installation: tensorboard 2.9.0\n",
      "    Uninstalling tensorboard-2.9.0:\n",
      "      Successfully uninstalled tensorboard-2.9.0\n",
      "  Attempting uninstall: tensorflow\n",
      "    Found existing installation: tensorflow 2.9.0\n",
      "    Uninstalling tensorflow-2.9.0:\n",
      "      Successfully uninstalled tensorflow-2.9.0\n",
      "Successfully installed Cython-0.29.33 apache-beam-2.46.0 avro-python3-1.10.2 charset-normalizer-3.1.0 click-8.1.3 cloudpickle-2.2.1 colorama-0.4.6 contextlib2-21.6.0 contourpy-1.0.7 crcmod-1.7 cycler-0.11.0 dill-0.3.1.1 dm-tree-0.1.8 docopt-0.6.2 etils-1.1.1 fastavro-1.7.3 fasteners-0.18 flatbuffers-23.3.3 fonttools-4.39.2 gin-config-0.5.0 google-api-core-2.11.0 google-api-python-client-2.82.0 google-auth-2.16.3 google-auth-httplib2-0.1.0 googleapis-common-protos-1.59.0 hdfs-2.7.0 httplib2-0.21.0 immutabledict-2.2.3 importlib-resources-5.12.0 joblib-1.2.0 kaggle-1.5.13 keras-2.11.0 kiwisolver-1.4.4 lvis-0.5.3 matplotlib-3.7.1 oauth2client-4.1.3 object-detection-0.1 objsize-0.6.1 opencv-python-4.7.0.72 opencv-python-headless-4.7.0.72 orjson-3.8.8 pandas-1.5.3 portalocker-2.7.0 promise-2.3 proto-plus-1.22.2 protobuf-3.19.6 psutil-5.9.4 py-cpuinfo-9.0.0 pyarrow-9.0.0 pycocotools-2.0.6 pydot-1.4.2 pymongo-3.13.0 pyparsing-2.4.7 python-dateutil-2.8.2 python-slugify-8.0.1 pytz-2023.2 pyyaml-5.4.1 regex-2023.3.23 requests-2.28.2 sacrebleu-2.2.0 scikit-learn-1.2.2 scipy-1.10.1 sentencepiece-0.1.97 seqeval-1.2.2 tabulate-0.9.0 tensorboard-2.11.2 tensorflow-2.11.1 tensorflow-addons-0.19.0 tensorflow-datasets-4.8.3 tensorflow-estimator-2.11.0 tensorflow-hub-0.13.0 tensorflow-io-gcs-filesystem-0.31.0 tensorflow-metadata-1.12.0 tensorflow-model-optimization-0.7.3 tensorflow-text-2.11.0 tensorflow_io-0.31.0 text-unidecode-1.3 tf-models-official-2.11.5 tf-slim-1.1.0 threadpoolctl-3.1.0 toml-0.10.2 tqdm-4.65.0 typeguard-3.0.2 typing-extensions-4.5.0 uritemplate-4.1.1 zstandard-0.20.0\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[0mRemoving intermediate container 9cdd237c2617\n",
      " ---> 070a9b2d1fff\n",
      "Step 15/17 : ENV TF_CPP_MIN_LOG_LEVEL 3\n",
      " ---> Running in b1a8d851b03e\n",
      "Removing intermediate container b1a8d851b03e\n",
      " ---> d4edd09ba197\n",
      "Step 16/17 : RUN pip3 install sagemaker-training\n",
      " ---> Running in a40a2a77da5c\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sagemaker-training\n",
      "  Downloading sagemaker_training-4.4.8.tar.gz (57 kB)\n",
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      "Building wheels for collected packages: sagemaker-training, inotify_simple\n",
      "  Building wheel for sagemaker-training (setup.py): started\n",
      "  Building wheel for sagemaker-training (setup.py): finished with status 'done'\n",
      "  Created wheel for sagemaker-training: filename=sagemaker_training-4.4.8-cp38-cp38-linux_x86_64.whl size=92188 sha256=a2b590ca3bf57abfbcced61d6c6f952b40095d661bd6ddc6cfe3e62cd20fa007\n",
      "  Stored in directory: /root/.cache/pip/wheels/2d/c8/75/3a93f047986d48b64d2b799442ddad0eaa9e7565a77e7fe823\n",
      "  Building wheel for inotify_simple (setup.py): started\n",
      "  Building wheel for inotify_simple (setup.py): finished with status 'done'\n",
      "  Created wheel for inotify_simple: filename=inotify_simple-1.2.1-py3-none-any.whl size=8201 sha256=6f0dcc17781715b1dd8fab3ee7cc5061650720d5446de1b975492a0770cc759d\n",
      "  Stored in directory: /root/.cache/pip/wheels/8b/2d/c2/46bac8503a2469925f6f463615984b3d1fe472e729363a28d3\n",
      "Successfully built sagemaker-training inotify_simple\n",
      "Installing collected packages: inotify_simple, zope.interface, zope.event, retrying, pynacl, jmespath, greenlet, cryptography, bcrypt, paramiko, gevent, botocore, s3transfer, boto3, sagemaker-training\n",
      "Successfully installed bcrypt-4.0.1 boto3-1.26.100 botocore-1.29.100 cryptography-40.0.1 gevent-22.10.2 greenlet-2.0.2 inotify_simple-1.2.1 jmespath-1.0.1 paramiko-3.1.0 pynacl-1.5.0 retrying-1.3.4 s3transfer-0.6.0 sagemaker-training-4.4.8 zope.event-4.6 zope.interface-6.0\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[0mRemoving intermediate container a40a2a77da5c\n",
      " ---> 5039dc21006a\n",
      "Step 17/17 : RUN pip install \"opencv-python-headless<4.3\"\n",
      " ---> Running in e5efa2416537\n",
      "Collecting opencv-python-headless<4.3\n",
      "  Downloading opencv_python_headless-4.2.0.34-cp38-cp38-manylinux1_x86_64.whl (21.6 MB)\n",
      "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.6/21.6 MB 64.6 MB/s eta 0:00:00\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from opencv-python-headless<4.3) (1.22.3)\n",
      "Installing collected packages: opencv-python-headless\n",
      "  Attempting uninstall: opencv-python-headless\n",
      "    Found existing installation: opencv-python-headless 4.7.0.72\n",
      "    Uninstalling opencv-python-headless-4.7.0.72:\n",
      "      Successfully uninstalled opencv-python-headless-4.7.0.72\n",
      "Successfully installed opencv-python-headless-4.2.0.34\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[0mRemoving intermediate container e5efa2416537\n",
      " ---> 038a1bf21c2a\n",
      "Successfully built 038a1bf21c2a\n",
      "Successfully tagged tf2-object-detection:latest\n",
      "Pushing image to ECR 476375380884.dkr.ecr.us-east-1.amazonaws.com/tf2-object-detection:20230328003317\n",
      "The push refers to repository [476375380884.dkr.ecr.us-east-1.amazonaws.com/tf2-object-detection]\n",
      "\n",
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digest: sha256:b1157b2f786011d24f23cffa2d14c1e5b3c453f3422a422a0da4bfce35e806dd size: 5782\n",
      "Saving ECR image URI into ecr_image_fullname.txt\n"
     ]
    }
   ],
   "source": [
    "# build and push the docker image. This code can be commented after being ran once.\n",
    "# This will take around 10 mins.\n",
    "image_name = 'tf2-object-detection'\n",
    "!sh ./docker/build_and_push.sh $image_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e62b3562",
   "metadata": {},
   "source": [
    "To verify that the image was correctly pushed to the [Elastic Container Registry](https://aws.amazon.com/ecr/), you can look at it in the AWS webapp. For example, below you can see that three different images have been pushed to ECR. You should only see one, called `tf2-object-detection`.\n",
    "![ECR Example](../data/example_ecr.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0310b6a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "476375380884.dkr.ecr.us-east-1.amazonaws.com/tf2-object-detection:20230328003317\n"
     ]
    }
   ],
   "source": [
    "# display the container name\n",
    "with open (os.path.join('docker', 'ecr_image_fullname.txt'), 'r') as f:\n",
    "    container = f.readlines()[0][:-1]\n",
    "\n",
    "print(container)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13b2a754",
   "metadata": {},
   "source": [
    "## Pre-trained model from model zoo\n",
    "\n",
    "As often, we are not training from scratch and we will be using a pretrained model from the TF Object Detection model zoo. You can find pretrained checkpoints [here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md). Because your time is limited for this project, we recommend to only experiment with the following models:\n",
    "* SSD MobileNet V2 FPNLite 640x640\t\n",
    "* SSD ResNet50 V1 FPN 640x640 (RetinaNet50)\t\n",
    "* Faster R-CNN ResNet50 V1 640x640\t\n",
    "* EfficientDet D1 640x640\t\n",
    "* Faster R-CNN ResNet152 V1 640x640\t\n",
    "\n",
    "In the code below, the EfficientDet D1 model is downloaded and extracted. This code should be ajusted if you were to experiment with other architectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c4b1d46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "efficientdet_d1_coco17_tpu-32/checkpoint/ckpt-0.data-00000-of-00001\n",
      "efficientdet_d1_coco17_tpu-32/checkpoint/checkpoint\n",
      "efficientdet_d1_coco17_tpu-32/checkpoint/ckpt-0.index\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘/tmp/checkpoint’: File exists\n",
      "mkdir: cannot create directory ‘source_dir/checkpoint’: File exists\n",
      "--2023-03-28 00:40:56--  http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d1_coco17_tpu-32.tar.gz\n",
      "Resolving download.tensorflow.org (download.tensorflow.org)... 172.253.62.128, 2607:f8b0:4004:c07::80\n",
      "Connecting to download.tensorflow.org (download.tensorflow.org)|172.253.62.128|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 51839363 (49M) [application/x-tar]\n",
      "Saving to: ‘/tmp/efficientdet.tar.gz’\n",
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      " 11750K .......... .......... .......... .......... .......... 23%  299M 1s\n",
      " 11800K .......... .......... .......... .......... .......... 23%  305M 1s\n",
      " 11850K .......... .......... .......... .......... .......... 23%  280M 1s\n",
      " 11900K .......... .......... .......... .......... .......... 23%  254M 1s\n",
      " 11950K .......... .......... .......... .......... .......... 23%  289M 1s\n",
      " 12000K .......... .......... .......... .......... .......... 23%  250M 1s\n",
      " 12050K .......... .......... .......... .......... .......... 23%  162M 1s\n",
      " 12100K .......... .......... .......... .......... .......... 24%  218M 1s\n",
      " 12150K .......... .......... .......... .......... .......... 24%  192M 1s\n",
      " 12200K .......... .......... .......... .......... .......... 24% 7.40M 1s\n",
      " 12250K .......... .......... .......... .......... .......... 24%  275M 1s\n",
      " 12300K .......... .......... .......... .......... .......... 24%  256M 1s\n",
      " 12350K .......... .......... .......... .......... .......... 24% 22.8M 1s\n",
      " 12400K .......... .......... .......... .......... .......... 24%  272M 1s\n",
      " 12450K .......... .......... .......... .......... .......... 24% 24.9M 1s\n",
      " 12500K .......... .......... .......... .......... .......... 24%  249M 1s\n",
      " 12550K .......... .......... .......... .......... .......... 24%  308M 1s\n",
      " 12600K .......... .......... .......... .......... .......... 24%  242M 1s\n",
      " 12650K .......... .......... .......... .......... .......... 25%  183M 1s\n",
      " 12700K .......... .......... .......... .......... .......... 25%  256M 1s\n",
      " 12750K .......... .......... .......... .......... .......... 25%  304M 1s\n",
      " 12800K .......... .......... .......... .......... .......... 25%  297M 1s\n",
      " 12850K .......... .......... .......... .......... .......... 25%  283M 1s\n",
      " 12900K .......... .......... .......... .......... .......... 25% 18.6M 1s\n",
      " 12950K .......... .......... .......... .......... .......... 25%  301M 1s\n",
      " 13000K .......... .......... .......... .......... .......... 25%  296M 1s\n",
      " 13050K .......... .......... .......... .......... .......... 25%  313M 1s\n",
      " 13100K .......... .......... .......... .......... .......... 25%  242M 1s\n",
      " 13150K .......... .......... .......... .......... .......... 26% 26.6M 1s\n",
      " 13200K .......... .......... .......... .......... .......... 26% 70.5M 1s\n",
      " 13250K .......... .......... .......... .......... .......... 26%  275M 1s\n",
      " 13300K .......... .......... .......... .......... .......... 26%  156M 1s\n",
      " 13350K .......... .......... .......... .......... .......... 26%  298M 1s\n",
      " 13400K .......... .......... .......... .......... .......... 26%  282M 1s\n",
      " 13450K .......... .......... .......... .......... .......... 26%  271M 1s\n",
      " 13500K .......... .......... .......... .......... .......... 26%  156M 1s\n",
      " 13550K .......... .......... .......... .......... .......... 26% 5.65M 1s\n",
      " 13600K .......... .......... .......... .......... .......... 26%  223M 1s\n",
      " 13650K .......... .......... .......... .......... .......... 27%  288M 1s\n",
      " 13700K .......... .......... .......... .......... .......... 27%  256M 1s\n",
      " 13750K .......... .......... .......... .......... .......... 27%  301M 1s\n",
      " 13800K .......... .......... .......... .......... .......... 27%  306M 1s\n",
      " 13850K .......... .......... .......... .......... .......... 27%  309M 1s\n",
      " 13900K .......... .......... .......... .......... .......... 27%  257M 1s\n",
      " 13950K .......... .......... .......... .......... .......... 27%  274M 1s\n",
      " 14000K .......... .......... .......... .......... .......... 27% 10.7M 1s\n",
      " 14050K .......... .......... .......... .......... .......... 27%  278M 1s\n",
      " 14100K .......... .......... .......... .......... .......... 27%  265M 1s\n",
      " 14150K .......... .......... .......... .......... .......... 28% 15.7M 1s\n",
      " 14200K .......... .......... .......... .......... .......... 28% 16.7M 1s\n",
      " 14250K .......... .......... .......... .......... .......... 28% 41.7M 1s\n",
      " 14300K .......... .......... .......... .......... .......... 28% 48.4M 1s\n",
      " 14350K .......... .......... .......... .......... .......... 28%  192M 1s\n",
      " 14400K .......... .......... .......... .......... .......... 28% 96.9M 1s\n",
      " 14450K .......... .......... .......... .......... .......... 28%  103M 1s\n",
      " 14500K .......... .......... .......... .......... .......... 28% 80.0M 1s\n",
      " 14550K .......... .......... .......... .......... .......... 28% 9.16M 1s\n",
      " 14600K .......... .......... .......... .......... .......... 28%  272M 1s\n",
      " 14650K .......... .......... .......... .......... .......... 29% 51.0M 1s\n",
      " 14700K .......... .......... .......... .......... .......... 29%  256M 1s\n",
      " 14750K .......... .......... .......... .......... .......... 29%  298M 1s\n",
      " 14800K .......... .......... .......... .......... .......... 29% 45.9M 1s\n",
      " 14850K .......... .......... .......... .......... .......... 29%  275M 1s\n",
      " 14900K .......... .......... .......... .......... .......... 29% 38.0M 1s\n",
      " 14950K .......... .......... .......... .......... .......... 29%  406M 1s\n",
      " 15000K .......... .......... .......... .......... .......... 29%  409M 1s\n",
      " 15050K .......... .......... .......... .......... .......... 29%  451M 1s\n",
      " 15100K .......... .......... .......... .......... .......... 29% 68.4M 1s\n",
      " 15150K .......... .......... .......... .......... .......... 30%  256M 1s\n",
      " 15200K .......... .......... .......... .......... .......... 30% 21.6M 1s\n",
      " 15250K .......... .......... .......... .......... .......... 30%  199M 1s\n",
      " 15300K .......... .......... .......... .......... .......... 30% 30.3M 1s\n",
      " 15350K .......... .......... .......... .......... .......... 30%  297M 1s\n",
      " 15400K .......... .......... .......... .......... .......... 30% 42.3M 1s\n",
      " 15450K .......... .......... .......... .......... .......... 30% 50.4M 1s\n",
      " 15500K .......... .......... .......... .......... .......... 30% 20.2M 1s\n",
      " 15550K .......... .......... .......... .......... .......... 30%  132M 1s\n",
      " 15600K .......... .......... .......... .......... .......... 30% 36.9M 1s\n",
      " 15650K .......... .......... .......... .......... .......... 31%  236M 1s\n",
      " 15700K .......... .......... .......... .......... .......... 31%  264M 1s\n",
      " 15750K .......... .......... .......... .......... .......... 31%  255M 1s\n",
      " 15800K .......... .......... .......... .......... .......... 31% 94.2M 1s\n",
      " 15850K .......... .......... .......... .......... .......... 31% 59.9M 1s\n",
      " 15900K .......... .......... .......... .......... .......... 31% 22.6M 1s\n",
      " 15950K .......... .......... .......... .......... .......... 31%  280M 1s\n",
      " 16000K .......... .......... .......... .......... .......... 31% 73.7M 1s\n",
      " 16050K .......... .......... .......... .......... .......... 31%  157M 1s\n",
      " 16100K .......... .......... .......... .......... .......... 31%  197M 1s\n",
      " 16150K .......... .......... .......... .......... .......... 32%  260M 1s\n",
      " 16200K .......... .......... .......... .......... .......... 32%  222M 1s\n",
      " 16250K .......... .......... .......... .......... .......... 32%  215M 1s\n",
      " 16300K .......... .......... .......... .......... .......... 32%  189M 1s\n",
      " 16350K .......... .......... .......... .......... .......... 32%  301M 1s\n",
      " 16400K .......... .......... .......... .......... .......... 32%  134M 1s\n",
      " 16450K .......... .......... .......... .......... .......... 32%  278M 1s\n",
      " 16500K .......... .......... .......... .......... .......... 32%  262M 1s\n",
      " 16550K .......... .......... .......... .......... .......... 32%  208M 1s\n",
      " 16600K .......... .......... .......... .......... .......... 32%  158M 1s\n",
      " 16650K .......... .......... .......... .......... .......... 32%  167M 1s\n",
      " 16700K .......... .......... .......... .......... .......... 33%  220M 1s\n",
      " 16750K .......... .......... .......... .......... .......... 33%  191M 1s\n",
      " 16800K .......... .......... .......... .......... .......... 33%  227M 1s\n",
      " 16850K .......... .......... .......... .......... .......... 33%  303M 1s\n",
      " 16900K .......... .......... .......... .......... .......... 33% 12.3M 1s\n",
      " 16950K .......... .......... .......... .......... .......... 33%  264M 1s\n",
      " 17000K .......... .......... .......... .......... .......... 33%  239M 1s\n",
      " 17050K .......... .......... .......... .......... .......... 33% 52.0M 1s\n",
      " 17100K .......... .......... .......... .......... .......... 33%  118M 1s\n",
      " 17150K .......... .......... .......... .......... .......... 33% 52.0M 1s\n",
      " 17200K .......... .......... .......... .......... .......... 34%  138M 1s\n",
      " 17250K .......... .......... .......... .......... .......... 34%  251M 1s\n",
      " 17300K .......... .......... .......... .......... .......... 34%  127M 1s\n",
      " 17350K .......... .......... .......... .......... .......... 34%  278M 1s\n",
      " 17400K .......... .......... .......... .......... .......... 34%  298M 1s\n",
      " 17450K .......... .......... .......... .......... .......... 34%  271M 1s\n",
      " 17500K .......... .......... .......... .......... .......... 34%  199M 1s\n",
      " 17550K .......... .......... .......... .......... .......... 34%  288M 1s\n",
      " 17600K .......... .......... .......... .......... .......... 34%  194M 1s\n",
      " 17650K .......... .......... .......... .......... .......... 34%  262M 1s\n",
      " 17700K .......... .......... .......... .......... .......... 35% 43.4M 1s\n",
      " 17750K .......... .......... .......... .......... .......... 35%  122M 1s\n",
      " 17800K .......... .......... .......... .......... .......... 35%  139M 1s\n",
      " 17850K .......... .......... .......... .......... .......... 35% 7.45M 1s\n",
      " 17900K .......... .......... .......... .......... .......... 35%  242M 1s\n",
      " 17950K .......... .......... .......... .......... .......... 35% 34.9M 1s\n",
      " 18000K .......... .......... .......... .......... .......... 35%  215M 1s\n",
      " 18050K .......... .......... .......... .......... .......... 35%  110M 1s\n",
      " 18100K .......... .......... .......... .......... .......... 35%  115M 1s\n",
      " 18150K .......... .......... .......... .......... .......... 35% 41.3M 1s\n",
      " 18200K .......... .......... .......... .......... .......... 36%  187M 1s\n",
      " 18250K .......... .......... .......... .......... .......... 36%  206M 1s\n",
      " 18300K .......... .......... .......... .......... .......... 36%  137M 1s\n",
      " 18350K .......... .......... .......... .......... .......... 36% 69.9M 1s\n",
      " 18400K .......... .......... .......... .......... .......... 36% 70.4M 1s\n",
      " 18450K .......... .......... .......... .......... .......... 36% 87.3M 1s\n",
      " 18500K .......... .......... .......... .......... .......... 36% 55.7M 1s\n",
      " 18550K .......... .......... .......... .......... .......... 36%  222M 1s\n",
      " 18600K .......... .......... .......... .......... .......... 36% 66.0M 1s\n",
      " 18650K .......... .......... .......... .......... .......... 36% 82.6M 1s\n",
      " 18700K .......... .......... .......... .......... .......... 37% 93.8M 1s\n",
      " 18750K .......... .......... .......... .......... .......... 37% 86.3M 1s\n",
      " 18800K .......... .......... .......... .......... .......... 37%  317M 1s\n",
      " 18850K .......... .......... .......... .......... .......... 37%  279M 1s\n",
      " 18900K .......... .......... .......... .......... .......... 37%  247M 1s\n",
      " 18950K .......... .......... .......... .......... .......... 37% 38.9M 1s\n",
      " 19000K .......... .......... .......... .......... .......... 37% 45.9M 1s\n",
      " 19050K .......... .......... .......... .......... .......... 37% 45.6M 1s\n",
      " 19100K .......... .......... .......... .......... .......... 37% 49.5M 1s\n",
      " 19150K .......... .......... .......... .......... .......... 37% 57.2M 1s\n",
      " 19200K .......... .......... .......... .......... .......... 38%  319M 1s\n",
      " 19250K .......... .......... .......... .......... .......... 38%  340M 1s\n",
      " 19300K .......... .......... .......... .......... .......... 38% 73.2M 1s\n",
      " 19350K .......... .......... .......... .......... .......... 38%  234M 1s\n",
      " 19400K .......... .......... .......... .......... .......... 38%  260M 1s\n",
      " 19450K .......... .......... .......... .......... .......... 38%  129M 1s\n",
      " 19500K .......... .......... .......... .......... .......... 38%  197M 1s\n",
      " 19550K .......... .......... .......... .......... .......... 38%  180M 1s\n",
      " 19600K .......... .......... .......... .......... .......... 38% 39.6M 1s\n",
      " 19650K .......... .......... .......... .......... .......... 38%  152M 1s\n",
      " 19700K .......... .......... .......... .......... .......... 39%  202M 1s\n",
      " 19750K .......... .......... .......... .......... .......... 39%  304M 1s\n",
      " 19800K .......... .......... .......... .......... .......... 39%  203M 1s\n",
      " 19850K .......... .......... .......... .......... .......... 39%  138M 1s\n",
      " 19900K .......... .......... .......... .......... .......... 39%  260M 1s\n",
      " 19950K .......... .......... .......... .......... .......... 39%  305M 1s\n",
      " 20000K .......... .......... .......... .......... .......... 39%  111M 1s\n",
      " 20050K .......... .......... .......... .......... .......... 39%  131M 1s\n",
      " 20100K .......... .......... .......... .......... .......... 39%  189M 1s\n",
      " 20150K .......... .......... .......... .......... .......... 39% 83.0M 1s\n",
      " 20200K .......... .......... .......... .......... .......... 40%  172M 1s\n",
      " 20250K .......... .......... .......... .......... .......... 40%  299M 1s\n",
      " 20300K .......... .......... .......... .......... .......... 40%  225M 0s\n",
      " 20350K .......... .......... .......... .......... .......... 40%  110M 0s\n",
      " 20400K .......... .......... .......... .......... .......... 40%  136M 0s\n",
      " 20450K .......... .......... .......... .......... .......... 40%  156M 0s\n",
      " 20500K .......... .......... .......... .......... .......... 40%  185M 0s\n",
      " 20550K .......... .......... .......... .......... .......... 40%  173M 0s\n",
      " 20600K .......... .......... .......... .......... .......... 40% 79.5M 0s\n",
      " 20650K .......... .......... .......... .......... .......... 40%  135M 0s\n",
      " 20700K .......... .......... .......... .......... .......... 40%  154M 0s\n",
      " 20750K .......... .......... .......... .......... .......... 41%  309M 0s\n",
      " 20800K .......... .......... .......... .......... .......... 41%  139M 0s\n",
      " 20850K .......... .......... .......... .......... .......... 41%  221M 0s\n",
      " 20900K .......... .......... .......... .......... .......... 41%  191M 0s\n",
      " 20950K .......... .......... .......... .......... .......... 41%  145M 0s\n",
      " 21000K .......... .......... .......... .......... .......... 41%  306M 0s\n",
      " 21050K .......... .......... .......... .......... .......... 41%  144M 0s\n",
      " 21100K .......... .......... .......... .......... .......... 41%  154M 0s\n",
      " 21150K .......... .......... .......... .......... .......... 41%  143M 0s\n",
      " 21200K .......... .......... .......... .......... .......... 41% 8.93M 0s\n",
      " 21250K .......... .......... .......... .......... .......... 42%  240M 0s\n",
      " 21300K .......... .......... .......... .......... .......... 42%  254M 0s\n",
      " 21350K .......... .......... .......... .......... .......... 42%  193M 0s\n",
      " 21400K .......... .......... .......... .......... .......... 42%  302M 0s\n",
      " 21450K .......... .......... .......... .......... .......... 42%  318M 0s\n",
      " 21500K .......... .......... .......... .......... .......... 42% 28.6M 0s\n",
      " 21550K .......... .......... .......... .......... .......... 42%  228M 0s\n",
      " 21600K .......... .......... .......... .......... .......... 42%  311M 0s\n",
      " 21650K .......... .......... .......... .......... .......... 42%  297M 0s\n",
      " 21700K .......... .......... .......... .......... .......... 42%  276M 0s\n",
      " 21750K .......... .......... .......... .......... .......... 43%  316M 0s\n",
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      " 21950K .......... .......... .......... .......... .......... 43%  139M 0s\n",
      " 22000K .......... .......... .......... .......... .......... 43% 25.7M 0s\n",
      " 22050K .......... .......... .......... .......... .......... 43%  174M 0s\n",
      " 22100K .......... .......... .......... .......... .......... 43%  266M 0s\n",
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      " 22750K .......... .......... .......... .......... .......... 45%  164M 0s\n",
      " 22800K .......... .......... .......... .......... .......... 45% 99.1M 0s\n",
      " 22850K .......... .......... .......... .......... .......... 45%  138M 0s\n",
      " 22900K .......... .......... .......... .......... .......... 45%  154M 0s\n",
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      " 23050K .......... .......... .......... .......... .......... 45%  192M 0s\n",
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      " 23200K .......... .......... .......... .......... .......... 45% 11.9M 0s\n",
      " 23250K .......... .......... .......... .......... .......... 46%  275M 0s\n",
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      " 23800K .......... .......... .......... .......... .......... 47% 27.1M 0s\n",
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      " 23900K .......... .......... .......... .......... .......... 47% 7.07M 0s\n",
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      " 24250K .......... .......... .......... .......... .......... 48%  308M 0s\n",
      " 24300K .......... .......... .......... .......... .......... 48%  254M 0s\n",
      " 24350K .......... .......... .......... .......... .......... 48%  302M 0s\n",
      " 24400K .......... .......... .......... .......... .......... 48% 13.3M 0s\n",
      " 24450K .......... .......... .......... .......... .......... 48%  253M 0s\n",
      " 24500K .......... .......... .......... .......... .......... 48%  250M 0s\n",
      " 24550K .......... .......... .......... .......... .......... 48%  263M 0s\n",
      " 24600K .......... .......... .......... .......... .......... 48%  291M 0s\n",
      " 24650K .......... .......... .......... .......... .......... 48%  212M 0s\n",
      " 24700K .......... .......... .......... .......... .......... 48% 10.7M 0s\n",
      " 24750K .......... .......... .......... .......... .......... 48%  331M 0s\n",
      " 24800K .......... .......... .......... .......... .......... 49%  339M 0s\n",
      " 24850K .......... .......... .......... .......... .......... 49% 29.9M 0s\n",
      " 24900K .......... .......... .......... .......... .......... 49%  158M 0s\n",
      " 24950K .......... .......... .......... .......... .......... 49%  220M 0s\n",
      " 25000K .......... .......... .......... .......... .......... 49% 21.5M 0s\n",
      " 25050K .......... .......... .......... .......... .......... 49%  340M 0s\n",
      " 25100K .......... .......... .......... .......... .......... 49%  288M 0s\n",
      " 25150K .......... .......... .......... .......... .......... 49%  354M 0s\n",
      " 25200K .......... .......... .......... .......... .......... 49%  352M 0s\n",
      " 25250K .......... .......... .......... .......... .......... 49%  364M 0s\n",
      " 25300K .......... .......... .......... .......... .......... 50%  294M 0s\n",
      " 25350K .......... .......... .......... .......... .......... 50%  338M 0s\n",
      " 25400K .......... .......... .......... .......... .......... 50%  349M 0s\n",
      " 25450K .......... .......... .......... .......... .......... 50%  335M 0s\n",
      " 25500K .......... .......... .......... .......... .......... 50% 9.01M 0s\n",
      " 25550K .......... .......... .......... .......... .......... 50%  338M 0s\n",
      " 25600K .......... .......... .......... .......... .......... 50% 63.8M 0s\n",
      " 25650K .......... .......... .......... .......... .......... 50%  129M 0s\n",
      " 25700K .......... .......... .......... .......... .......... 50% 71.9M 0s\n",
      " 25750K .......... .......... .......... .......... .......... 50%  195M 0s\n",
      " 25800K .......... .......... .......... .......... .......... 51%  322M 0s\n",
      " 25850K .......... .......... .......... .......... .......... 51%  205M 0s\n",
      " 25900K .......... .......... .......... .......... .......... 51% 17.9M 0s\n",
      " 25950K .......... .......... .......... .......... .......... 51% 54.8M 0s\n",
      " 26000K .......... .......... .......... .......... .......... 51%  261M 0s\n",
      " 26050K .......... .......... .......... .......... .......... 51%  306M 0s\n",
      " 26100K .......... .......... .......... .......... .......... 51%  262M 0s\n",
      " 26150K .......... .......... .......... .......... .......... 51%  311M 0s\n",
      " 26200K .......... .......... .......... .......... .......... 51%  316M 0s\n",
      " 26250K .......... .......... .......... .......... .......... 51% 15.4M 0s\n",
      " 26300K .......... .......... .......... .......... .......... 52% 31.8M 0s\n",
      " 26350K .......... .......... .......... .......... .......... 52%  296M 0s\n",
      " 26400K .......... .......... .......... .......... .......... 52%  149M 0s\n",
      " 26450K .......... .......... .......... .......... .......... 52% 53.8M 0s\n",
      " 26500K .......... .......... .......... .......... .......... 52% 8.21M 0s\n",
      " 26550K .......... .......... .......... .......... .......... 52%  288M 0s\n",
      " 26600K .......... .......... .......... .......... .......... 52%  274M 0s\n",
      " 26650K .......... .......... .......... .......... .......... 52% 30.9M 0s\n",
      " 26700K .......... .......... .......... .......... .......... 52%  252M 0s\n",
      " 26750K .......... .......... .......... .......... .......... 52%  299M 0s\n",
      " 26800K .......... .......... .......... .......... .......... 53%  300M 0s\n",
      " 26850K .......... .......... .......... .......... .......... 53% 21.6M 0s\n",
      " 26900K .......... .......... .......... .......... .......... 53% 10.4M 0s\n",
      " 26950K .......... .......... .......... .......... .......... 53%  302M 0s\n",
      " 27000K .......... .......... .......... .......... .......... 53%  307M 0s\n",
      " 27050K .......... .......... .......... .......... .......... 53% 21.3M 0s\n",
      " 27100K .......... .......... .......... .......... .......... 53%  148M 0s\n",
      " 27150K .......... .......... .......... .......... .......... 53% 11.0M 0s\n",
      " 27200K .......... .......... .......... .......... .......... 53%  301M 0s\n",
      " 27250K .......... .......... .......... .......... .......... 53%  206M 0s\n",
      " 27300K .......... .......... .......... .......... .......... 54% 23.0M 0s\n",
      " 27350K .......... .......... .......... .......... .......... 54%  293M 0s\n",
      " 27400K .......... .......... .......... .......... .......... 54%  282M 0s\n",
      " 27450K .......... .......... .......... .......... .......... 54%  335M 0s\n",
      " 27500K .......... .......... .......... .......... .......... 54%  273M 0s\n",
      " 27550K .......... .......... .......... .......... .......... 54%  327M 0s\n",
      " 27600K .......... .......... .......... .......... .......... 54% 21.7M 0s\n",
      " 27650K .......... .......... .......... .......... .......... 54%  267M 0s\n",
      " 27700K .......... .......... .......... .......... .......... 54%  251M 0s\n",
      " 27750K .......... .......... .......... .......... .......... 54%  313M 0s\n",
      " 27800K .......... .......... .......... .......... .......... 55%  344M 0s\n",
      " 27850K .......... .......... .......... .......... .......... 55%  345M 0s\n",
      " 27900K .......... .......... .......... .......... .......... 55%  305M 0s\n",
      " 27950K .......... .......... .......... .......... .......... 55%  311M 0s\n",
      " 28000K .......... .......... .......... .......... .......... 55%  294M 0s\n",
      " 28050K .......... .......... .......... .......... .......... 55%  327M 0s\n",
      " 28100K .......... .......... .......... .......... .......... 55%  282M 0s\n",
      " 28150K .......... .......... .......... .......... .......... 55%  293M 0s\n",
      " 28200K .......... .......... .......... .......... .......... 55%  252M 0s\n",
      " 28250K .......... .......... .......... .......... .......... 55%  303M 0s\n",
      " 28300K .......... .......... .......... .......... .......... 56%  272M 0s\n",
      " 28350K .......... .......... .......... .......... .......... 56%  250M 0s\n",
      " 28400K .......... .......... .......... .......... .......... 56%  171M 0s\n",
      " 28450K .......... .......... .......... .......... .......... 56% 58.2M 0s\n",
      " 28500K .......... .......... .......... .......... .......... 56% 53.8M 0s\n",
      " 28550K .......... .......... .......... .......... .......... 56% 59.3M 0s\n",
      " 28600K .......... .......... .......... .......... .......... 56% 62.9M 0s\n",
      " 28650K .......... .......... .......... .......... .......... 56% 59.6M 0s\n",
      " 28700K .......... .......... .......... .......... .......... 56% 56.7M 0s\n",
      " 28750K .......... .......... .......... .......... .......... 56% 64.9M 0s\n",
      " 28800K .......... .......... .......... .......... .......... 56% 60.6M 0s\n",
      " 28850K .......... .......... .......... .......... .......... 57% 64.5M 0s\n",
      " 28900K .......... .......... .......... .......... .......... 57% 53.1M 0s\n",
      " 28950K .......... .......... .......... .......... .......... 57% 64.7M 0s\n",
      " 29000K .......... .......... .......... .......... .......... 57% 63.9M 0s\n",
      " 29050K .......... .......... .......... .......... .......... 57% 65.1M 0s\n",
      " 29100K .......... .......... .......... .......... .......... 57%  218M 0s\n",
      " 29150K .......... .......... .......... .......... .......... 57%  270M 0s\n",
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      " 30000K .......... .......... .......... .......... .......... 59%  252M 0s\n",
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      " 30700K .......... .......... .......... .......... .......... 60%  216M 0s\n",
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      " 36850K .......... .......... .......... .......... .......... 72%  273M 0s\n",
      " 36900K .......... .......... .......... .......... .......... 72%  231M 0s\n",
      " 36950K .......... .......... .......... .......... .......... 73%  264M 0s\n",
      " 37000K .......... .......... .......... .......... .......... 73%  283M 0s\n",
      " 37050K .......... .......... .......... .......... .......... 73%  223M 0s\n",
      " 37100K .......... .......... .......... .......... .......... 73%  238M 0s\n",
      " 37150K .......... .......... .......... .......... .......... 73%  276M 0s\n",
      " 37200K .......... .......... .......... .......... .......... 73%  254M 0s\n",
      " 37250K .......... .......... .......... .......... .......... 73%  296M 0s\n",
      " 37300K .......... .......... .......... .......... .......... 73%  231M 0s\n",
      " 37350K .......... .......... .......... .......... .......... 73%  269M 0s\n",
      " 37400K .......... .......... .......... .......... .......... 73%  249M 0s\n",
      " 37450K .......... .......... .......... .......... .......... 74%  264M 0s\n",
      " 37500K .......... .......... .......... .......... .......... 74%  220M 0s\n",
      " 37550K .......... .......... .......... .......... .......... 74%  253M 0s\n",
      " 37600K .......... .......... .......... .......... .......... 74%  272M 0s\n",
      " 37650K .......... .......... .......... .......... .......... 74%  268M 0s\n",
      " 37700K .......... .......... .......... .......... .......... 74%  223M 0s\n",
      " 37750K .......... .......... .......... .......... .......... 74%  250M 0s\n",
      " 37800K .......... .......... .......... .......... .......... 74%  268M 0s\n",
      " 37850K .......... .......... .......... .......... .......... 74%  265M 0s\n",
      " 37900K .......... .......... .......... .......... .......... 74%  216M 0s\n",
      " 37950K .......... .......... .......... .......... .......... 75%  275M 0s\n",
      " 38000K .......... .......... .......... .......... .......... 75%  238M 0s\n",
      " 38050K .......... .......... .......... .......... .......... 75%  265M 0s\n",
      " 38100K .......... .......... .......... .......... .......... 75%  246M 0s\n",
      " 38150K .......... .......... .......... .......... .......... 75%  277M 0s\n",
      " 38200K .......... .......... .......... .......... .......... 75%  233M 0s\n",
      " 38250K .......... .......... .......... .......... .......... 75%  289M 0s\n",
      " 38300K .......... .......... .......... .......... .......... 75%  237M 0s\n",
      " 38350K .......... .......... .......... .......... .......... 75%  269M 0s\n",
      " 38400K .......... .......... .......... .......... .......... 75%  276M 0s\n",
      " 38450K .......... .......... .......... .......... .......... 76%  255M 0s\n",
      " 38500K .......... .......... .......... .......... .......... 76%  227M 0s\n",
      " 38550K .......... .......... .......... .......... .......... 76%  298M 0s\n",
      " 38600K .......... .......... .......... .......... .......... 76%  249M 0s\n",
      " 38650K .......... .......... .......... .......... .......... 76%  229M 0s\n",
      " 38700K .......... .......... .......... .......... .......... 76%  210M 0s\n",
      " 38750K .......... .......... .......... .......... .......... 76%  249M 0s\n",
      " 38800K .......... .......... .......... .......... .......... 76%  267M 0s\n",
      " 38850K .......... .......... .......... .......... .......... 76%  263M 0s\n",
      " 38900K .......... .......... .......... .......... .......... 76%  208M 0s\n",
      " 38950K .......... .......... .......... .......... .......... 77%  234M 0s\n",
      " 39000K .......... .......... .......... .......... .......... 77%  259M 0s\n",
      " 39050K .......... .......... .......... .......... .......... 77%  250M 0s\n",
      " 39100K .......... .......... .......... .......... .......... 77%  223M 0s\n",
      " 39150K .......... .......... .......... .......... .......... 77%  234M 0s\n",
      " 39200K .......... .......... .......... .......... .......... 77%  236M 0s\n",
      " 39250K .......... .......... .......... .......... .......... 77%  253M 0s\n",
      " 39300K .......... .......... .......... .......... .......... 77%  251M 0s\n",
      " 39350K .......... .......... .......... .......... .......... 77%  243M 0s\n",
      " 39400K .......... .......... .......... .......... .......... 77%  310M 0s\n",
      " 39450K .......... .......... .......... .......... .......... 78%  296M 0s\n",
      " 39500K .......... .......... .......... .......... .......... 78%  228M 0s\n",
      " 39550K .......... .......... .......... .......... .......... 78%  279M 0s\n",
      " 39600K .......... .......... .......... .......... .......... 78%  291M 0s\n",
      " 39650K .......... .......... .......... .......... .......... 78%  270M 0s\n",
      " 39700K .......... .......... .......... .......... .......... 78%  224M 0s\n",
      " 39750K .......... .......... .......... .......... .......... 78%  285M 0s\n",
      " 39800K .......... .......... .......... .......... .......... 78%  283M 0s\n",
      " 39850K .......... .......... .......... .......... .......... 78%  276M 0s\n",
      " 39900K .......... .......... .......... .......... .......... 78%  218M 0s\n",
      " 39950K .......... .......... .......... .......... .......... 79%  278M 0s\n",
      " 40000K .......... .......... .......... .......... .......... 79%  260M 0s\n",
      " 40050K .......... .......... .......... .......... .......... 79%  241M 0s\n",
      " 40100K .......... .......... .......... .......... .......... 79%  242M 0s\n",
      " 40150K .......... .......... .......... .......... .......... 79%  252M 0s\n",
      " 40200K .......... .......... .......... .......... .......... 79%  245M 0s\n",
      " 40250K .......... .......... .......... .......... .......... 79%  280M 0s\n",
      " 40300K .......... .......... .......... .......... .......... 79%  236M 0s\n",
      " 40350K .......... .......... .......... .......... .......... 79%  256M 0s\n",
      " 40400K .......... .......... .......... .......... .......... 79%  289M 0s\n",
      " 40450K .......... .......... .......... .......... .......... 80%  276M 0s\n",
      " 40500K .......... .......... .......... .......... .......... 80%  251M 0s\n",
      " 40550K .......... .......... .......... .......... .......... 80%  274M 0s\n",
      " 40600K .......... .......... .......... .......... .......... 80%  293M 0s\n",
      " 40650K .......... .......... .......... .......... .......... 80%  303M 0s\n",
      " 40700K .......... .......... .......... .......... .......... 80%  263M 0s\n",
      " 40750K .......... .......... .......... .......... .......... 80%  295M 0s\n",
      " 40800K .......... .......... .......... .......... .......... 80%  317M 0s\n",
      " 40850K .......... .......... .......... .......... .......... 80%  314M 0s\n",
      " 40900K .......... .......... .......... .......... .......... 80%  264M 0s\n",
      " 40950K .......... .......... .......... .......... .......... 80% 7.14M 0s\n",
      " 41000K .......... .......... .......... .......... .......... 81%  238M 0s\n",
      " 41050K .......... .......... .......... .......... .......... 81%  272M 0s\n",
      " 41100K .......... .......... .......... .......... .......... 81%  165M 0s\n",
      " 41150K .......... .......... .......... .......... .......... 81%  223M 0s\n",
      " 41200K .......... .......... .......... .......... .......... 81%  202M 0s\n",
      " 41250K .......... .......... .......... .......... .......... 81%  247M 0s\n",
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      " 41500K .......... .......... .......... .......... .......... 82%  256M 0s\n",
      " 41550K .......... .......... .......... .......... .......... 82%  228M 0s\n",
      " 41600K .......... .......... .......... .......... .......... 82%  271M 0s\n",
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      " 42000K .......... .......... .......... .......... .......... 83%  300M 0s\n",
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      " 49500K .......... .......... .......... .......... .......... 97%  258M 0s\n",
      " 49550K .......... .......... .......... .......... .......... 97%  300M 0s\n",
      " 49600K .......... .......... .......... .......... .......... 98%  310M 0s\n",
      " 49650K .......... .......... .......... .......... .......... 98%  273M 0s\n",
      " 49700K .......... .......... .......... .......... .......... 98%  290M 0s\n",
      " 49750K .......... .......... .......... .......... .......... 98%  308M 0s\n",
      " 49800K .......... .......... .......... .......... .......... 98%  320M 0s\n",
      " 49850K .......... .......... .......... .......... .......... 98%  269M 0s\n",
      " 49900K .......... .......... .......... .......... .......... 98%  308M 0s\n",
      " 49950K .......... .......... .......... .......... .......... 98%  302M 0s\n",
      " 50000K .......... .......... .......... .......... .......... 98%  302M 0s\n",
      " 50050K .......... .......... .......... .......... .......... 98%  275M 0s\n",
      " 50100K .......... .......... .......... .......... .......... 99%  318M 0s\n",
      " 50150K .......... .......... .......... .......... .......... 99%  304M 0s\n",
      " 50200K .......... .......... .......... .......... .......... 99%  315M 0s\n",
      " 50250K .......... .......... .......... .......... .......... 99%  277M 0s\n",
      " 50300K .......... .......... .......... .......... .......... 99%  303M 0s\n",
      " 50350K .......... .......... .......... .......... .......... 99%  319M 0s\n",
      " 50400K .......... .......... .......... .......... .......... 99%  320M 0s\n",
      " 50450K .......... .......... .......... .......... .......... 99%  271M 0s\n",
      " 50500K .......... .......... .......... .......... .......... 99%  301M 0s\n",
      " 50550K .......... .......... .......... .......... .......... 99%  305M 0s\n",
      " 50600K .......... .......... ....                            100%  295M=0.5s\n",
      "\n",
      "2023-03-28 00:40:57 (90.9 MB/s) - ‘/tmp/efficientdet.tar.gz’ saved [51839363/51839363]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir /tmp/checkpoint\n",
    "mkdir source_dir/checkpoint\n",
    "wget -O /tmp/efficientdet.tar.gz http://download.tensorflow.org/models/object_detection/tf2/20200711/efficientdet_d1_coco17_tpu-32.tar.gz\n",
    "tar -zxvf /tmp/efficientdet.tar.gz --strip-components 2 --directory source_dir/checkpoint efficientdet_d1_coco17_tpu-32/checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e04a98",
   "metadata": {},
   "source": [
    "## Edit pipeline.config file\n",
    "\n",
    "The [`pipeline.config`](source_dir/pipeline.config) in the `source_dir` folder should be updated when you experiment with different models. The different config files are available [here](https://github.com/tensorflow/models/tree/master/research/object_detection/configs/tf2).\n",
    "\n",
    ">Note: The provided `pipeline.config` file works well with the `EfficientDet` model. You would need to modify it when working with other models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47483545",
   "metadata": {},
   "source": [
    "## Launch Training Job\n",
    "\n",
    "Now that we have a dataset, a docker image and some pretrained model weights, we can launch the training job. To do so, we create a [Sagemaker Framework](https://sagemaker.readthedocs.io/en/stable/frameworks/index.html), where we indicate the container name, name of the config file, number of training steps etc.\n",
    "\n",
    "The `run_training.sh` script does the following:\n",
    "* train the model for `num_train_steps` \n",
    "* evaluate over the val dataset\n",
    "* export the model\n",
    "\n",
    "Different metrics will be displayed during the evaluation phase, including the mean average precision. These metrics can be used to quantify your model performances and compare over the different iterations.\n",
    "\n",
    "You can also monitor the training progress by navigating to **Training -> Training Jobs** from the Amazon Sagemaker dashboard in the Web UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0c7175cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: tf2-object-detection-2023-03-28-00-40-58-060\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-03-28 00:41:00 Starting - Starting the training job...\n",
      "2023-03-28 00:41:25 Starting - Preparing the instances for training......\n",
      "2023-03-28 00:42:34 Downloading - Downloading input data...\n",
      "2023-03-28 00:42:54 Training - Downloading the training image...............\n",
      "2023-03-28 00:45:30 Training - Training image download completed. Training in progress...\u001b[34m2023-03-28 00:45:54,953 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2023-03-28 00:45:54,984 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2023-03-28 00:45:55,016 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2023-03-28 00:45:55,029 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "\u001b[34mTraining Env:\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"train\": \"/opt/ml/input/data/train\",\n",
      "        \"val\": \"/opt/ml/input/data/val\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"current_instance_group\": \"homogeneousCluster\",\n",
      "    \"current_instance_group_hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"current_instance_type\": \"ml.p3.2xlarge\",\n",
      "    \"distribution_hosts\": [],\n",
      "    \"distribution_instance_groups\": [],\n",
      "    \"framework_module\": null,\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"model_dir\": \"/opt/training\",\n",
      "        \"num_train_steps\": \"2000\",\n",
      "        \"pipeline_config_path\": \"pipeline.config\",\n",
      "        \"sample_1_of_n_eval_examples\": \"1\"\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"train\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"val\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"instance_groups\": [\n",
      "        \"homogeneousCluster\"\n",
      "    ],\n",
      "    \"instance_groups_dict\": {\n",
      "        \"homogeneousCluster\": {\n",
      "            \"instance_group_name\": \"homogeneousCluster\",\n",
      "            \"instance_type\": \"ml.p3.2xlarge\",\n",
      "            \"hosts\": [\n",
      "                \"algo-1\"\n",
      "            ]\n",
      "        }\n",
      "    },\n",
      "    \"is_hetero\": false,\n",
      "    \"is_master\": true,\n",
      "    \"is_modelparallel_enabled\": null,\n",
      "    \"is_smddpmprun_installed\": false,\n",
      "    \"job_name\": \"tf2-object-detection-2023-03-28-00-40-58-060\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-00-40-58-060/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"run_training.sh\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 8,\n",
      "    \"num_gpus\": 1,\n",
      "    \"num_neurons\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"current_instance_type\": \"ml.p3.2xlarge\",\n",
      "        \"current_group_name\": \"homogeneousCluster\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"instance_groups\": [\n",
      "            {\n",
      "                \"instance_group_name\": \"homogeneousCluster\",\n",
      "                \"instance_type\": \"ml.p3.2xlarge\",\n",
      "                \"hosts\": [\n",
      "                    \"algo-1\"\n",
      "                ]\n",
      "            }\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"run_training.sh\"\u001b[0m\n",
      "\u001b[34m}\u001b[0m\n",
      "\u001b[34mEnvironment variables:\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"model_dir\":\"/opt/training\",\"num_train_steps\":\"2000\",\"pipeline_config_path\":\"pipeline.config\",\"sample_1_of_n_eval_examples\":\"1\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=run_training.sh\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.p3.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"val\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"train\",\"val\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.p3.2xlarge\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}}\u001b[0m\n",
      "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n",
      "\u001b[34mSM_IS_HETERO=false\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=run_training.sh\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=8\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=1\u001b[0m\n",
      "\u001b[34mSM_NUM_NEURONS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-00-40-58-060/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\",\"val\":\"/opt/ml/input/data/val\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.p3.2xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":null,\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"model_dir\":\"/opt/training\",\"num_train_steps\":\"2000\",\"pipeline_config_path\":\"pipeline.config\",\"sample_1_of_n_eval_examples\":\"1\"},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"val\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":false,\"job_name\":\"tf2-object-detection-2023-03-28-00-40-58-060\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-00-40-58-060/source/sourcedir.tar.gz\",\"module_name\":\"run_training.sh\",\"network_interface_name\":\"eth0\",\"num_cpus\":8,\"num_gpus\":1,\"num_neurons\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.p3.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.p3.2xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"run_training.sh\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--model_dir\",\"/opt/training\",\"--num_train_steps\",\"2000\",\"--pipeline_config_path\",\"pipeline.config\",\"--sample_1_of_n_eval_examples\",\"1\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAIN=/opt/ml/input/data/train\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_VAL=/opt/ml/input/data/val\u001b[0m\n",
      "\u001b[34mSM_HP_MODEL_DIR=/opt/training\u001b[0m\n",
      "\u001b[34mSM_HP_NUM_TRAIN_STEPS=2000\u001b[0m\n",
      "\u001b[34mSM_HP_PIPELINE_CONFIG_PATH=pipeline.config\u001b[0m\n",
      "\u001b[34mSM_HP_SAMPLE_1_OF_N_EVAL_EXAMPLES=1\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/usr/local/bin:/usr/lib/python38.zip:/usr/lib/python3.8:/usr/lib/python3.8/lib-dynload:/usr/local/lib/python3.8/dist-packages:/usr/lib/python3/dist-packages\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\u001b[0m\n",
      "\u001b[34m/bin/sh -c \"./run_training.sh --model_dir /opt/training --num_train_steps 2000 --pipeline_config_path pipeline.config --sample_1_of_n_eval_examples 1\"\u001b[0m\n",
      "\u001b[34m2023-03-28 00:45:55,029 sagemaker-training-toolkit INFO     Exceptions not imported for SageMaker Debugger as it is not installed.\u001b[0m\n",
      "\u001b[34m===TRAINING THE MODEL==\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.230138 140441181665088 mirrored_strategy.py:374] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting train_steps: 2000\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.234192 140441181665088 config_util.py:552] Maybe overwriting train_steps: 2000\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.234334 140441181665088 config_util.py:552] Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.246515 140441181665088 ssd_efficientnet_bifpn_feature_extractor.py:150] EfficientDet EfficientNet backbone version: efficientnet-b1\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.246622 140441181665088 ssd_efficientnet_bifpn_feature_extractor.py:152] EfficientDet BiFPN num filters: 88\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.246712 140441181665088 ssd_efficientnet_bifpn_feature_extractor.py:153] EfficientDet BiFPN num iterations: 4\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.251503 140441181665088 efficientnet_model.py:143] round_filter input=32 output=32\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.290355 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.294197 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.296424 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.297474 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.305820 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.310025 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.317189 140441181665088 efficientnet_model.py:143] round_filter input=32 output=32\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.317292 140441181665088 efficientnet_model.py:143] round_filter input=16 output=16\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.338415 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.339526 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.341682 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.342752 140441181665088 cross_device_ops.py:616] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.594987 140441181665088 efficientnet_model.py:143] round_filter input=16 output=16\u001b[0m\n",
      "\u001b[34mI0328 00:46:03.595109 140441181665088 efficientnet_model.py:143] round_filter input=24 output=24\u001b[0m\n",
      "\u001b[34mI0328 00:46:04.119454 140441181665088 efficientnet_model.py:143] round_filter input=24 output=24\u001b[0m\n",
      "\u001b[34mI0328 00:46:04.119611 140441181665088 efficientnet_model.py:143] round_filter input=40 output=40\u001b[0m\n",
      "\u001b[34mI0328 00:46:04.619745 140441181665088 efficientnet_model.py:143] round_filter input=40 output=40\u001b[0m\n",
      "\u001b[34mI0328 00:46:04.619873 140441181665088 efficientnet_model.py:143] round_filter input=80 output=80\u001b[0m\n",
      "\u001b[34mI0328 00:46:05.270238 140441181665088 efficientnet_model.py:143] round_filter input=80 output=80\u001b[0m\n",
      "\u001b[34mI0328 00:46:05.270368 140441181665088 efficientnet_model.py:143] round_filter input=112 output=112\u001b[0m\n",
      "\u001b[34mI0328 00:46:05.919008 140441181665088 efficientnet_model.py:143] round_filter input=112 output=112\u001b[0m\n",
      "\u001b[34mI0328 00:46:05.919172 140441181665088 efficientnet_model.py:143] round_filter input=192 output=192\u001b[0m\n",
      "\u001b[34mI0328 00:46:06.728332 140441181665088 efficientnet_model.py:143] round_filter input=192 output=192\u001b[0m\n",
      "\u001b[34mI0328 00:46:06.728485 140441181665088 efficientnet_model.py:143] round_filter input=320 output=320\u001b[0m\n",
      "\u001b[34mI0328 00:46:07.064761 140441181665088 efficientnet_model.py:143] round_filter input=1280 output=1280\u001b[0m\n",
      "\u001b[34mI0328 00:46:07.140546 140441181665088 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mrename to distribute_datasets_from_function\u001b[0m\n",
      "\u001b[34mW0328 00:46:07.193219 140441181665088 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/model_lib_v2.py:563: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mrename to distribute_datasets_from_function\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading unweighted datasets: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 00:46:07.201601 140441181665088 dataset_builder.py:162] Reading unweighted datasets: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading record datasets for input file: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 00:46:07.203152 140441181665088 dataset_builder.py:79] Reading record datasets for input file: ['/opt/ml/input/data/train/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Number of filenames to read: 84\u001b[0m\n",
      "\u001b[34mI0328 00:46:07.203276 140441181665088 dataset_builder.py:80] Number of filenames to read: 84\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mW0328 00:46:07.210763 140441181665088 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mW0328 00:46:07.229038 140441181665088 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mW0328 00:46:07.873434 140441181665088 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mW0328 00:46:14.305331 140441181665088 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mW0328 00:46:18.233657 140441181665088 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34m/usr/local/lib/python3.8/dist-packages/keras/backend.py:451: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn(\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse fn_output_signature instead\u001b[0m\n",
      "\u001b[34mW0328 00:47:01.490363 140418085799680 deprecation.py:554] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/deprecation.py:629: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse fn_output_signature instead\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mW0328 00:47:12.857374 140418085799680 utils.py:82] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mW0328 00:47:28.866588 140418085799680 utils.py:82] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mW0328 00:47:43.760036 140418085799680 utils.py:82] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mW0328 00:47:59.360594 140418085799680 utils.py:82] Gradients do not exist for variables ['stack_6/block_1/expand_bn/gamma:0', 'stack_6/block_1/expand_bn/beta:0', 'stack_6/block_1/depthwise_conv2d/depthwise_kernel:0', 'stack_6/block_1/depthwise_bn/gamma:0', 'stack_6/block_1/depthwise_bn/beta:0', 'stack_6/block_1/project_bn/gamma:0', 'stack_6/block_1/project_bn/beta:0', 'top_bn/gamma:0', 'top_bn/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 100 per-step time 1.497s\u001b[0m\n",
      "\u001b[34mI0328 00:49:30.842540 140441181665088 model_lib_v2.py:705] Step 100 per-step time 1.497s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.41115823,\n",
      " 'Loss/localization_loss': 0.036584705,\n",
      " 'Loss/regularization_loss': 0.029542705,\n",
      " 'Loss/total_loss': 0.47728565,\n",
      " 'learning_rate': 0.00416}\u001b[0m\n",
      "\u001b[34mI0328 00:49:30.842988 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.41115823,\n",
      " 'Loss/localization_loss': 0.036584705,\n",
      " 'Loss/regularization_loss': 0.029542705,\n",
      " 'Loss/total_loss': 0.47728565,\n",
      " 'learning_rate': 0.00416}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 200 per-step time 0.566s\u001b[0m\n",
      "\u001b[34mI0328 00:50:27.385935 140441181665088 model_lib_v2.py:705] Step 200 per-step time 0.566s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.41589385,\n",
      " 'Loss/localization_loss': 0.0335033,\n",
      " 'Loss/regularization_loss': 0.029546417,\n",
      " 'Loss/total_loss': 0.47894356,\n",
      " 'learning_rate': 0.0073200003}\u001b[0m\n",
      "\u001b[34mI0328 00:50:27.386314 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.41589385,\n",
      " 'Loss/localization_loss': 0.0335033,\n",
      " 'Loss/regularization_loss': 0.029546417,\n",
      " 'Loss/total_loss': 0.47894356,\n",
      " 'learning_rate': 0.0073200003}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 300 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mI0328 00:51:24.148418 140441181665088 model_lib_v2.py:705] Step 300 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.290769,\n",
      " 'Loss/localization_loss': 0.01693606,\n",
      " 'Loss/regularization_loss': 0.02954808,\n",
      " 'Loss/total_loss': 0.33725315,\n",
      " 'learning_rate': 0.010480001}\u001b[0m\n",
      "\u001b[34mI0328 00:51:24.148705 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.290769,\n",
      " 'Loss/localization_loss': 0.01693606,\n",
      " 'Loss/regularization_loss': 0.02954808,\n",
      " 'Loss/total_loss': 0.33725315,\n",
      " 'learning_rate': 0.010480001}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 400 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mI0328 00:52:20.969173 140441181665088 model_lib_v2.py:705] Step 400 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.34660465,\n",
      " 'Loss/localization_loss': 0.027877854,\n",
      " 'Loss/regularization_loss': 0.0295505,\n",
      " 'Loss/total_loss': 0.404033,\n",
      " 'learning_rate': 0.0136400005}\u001b[0m\n",
      "\u001b[34mI0328 00:52:20.969492 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.34660465,\n",
      " 'Loss/localization_loss': 0.027877854,\n",
      " 'Loss/regularization_loss': 0.0295505,\n",
      " 'Loss/total_loss': 0.404033,\n",
      " 'learning_rate': 0.0136400005}\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 500 per-step time 0.566s\u001b[0m\n",
      "\u001b[34mI0328 00:53:17.561467 140441181665088 model_lib_v2.py:705] Step 500 per-step time 0.566s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.32134366,\n",
      " 'Loss/localization_loss': 0.019176926,\n",
      " 'Loss/regularization_loss': 0.029560426,\n",
      " 'Loss/total_loss': 0.370081,\n",
      " 'learning_rate': 0.016800001}\u001b[0m\n",
      "\u001b[34mI0328 00:53:17.561806 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.32134366,\n",
      " 'Loss/localization_loss': 0.019176926,\n",
      " 'Loss/regularization_loss': 0.029560426,\n",
      " 'Loss/total_loss': 0.370081,\n",
      " 'learning_rate': 0.016800001}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 600 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 00:54:14.268592 140441181665088 model_lib_v2.py:705] Step 600 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.29795703,\n",
      " 'Loss/localization_loss': 0.013520041,\n",
      " 'Loss/regularization_loss': 0.029574914,\n",
      " 'Loss/total_loss': 0.34105197,\n",
      " 'learning_rate': 0.019960001}\u001b[0m\n",
      "\u001b[34mI0328 00:54:14.268904 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.29795703,\n",
      " 'Loss/localization_loss': 0.013520041,\n",
      " 'Loss/regularization_loss': 0.029574914,\n",
      " 'Loss/total_loss': 0.34105197,\n",
      " 'learning_rate': 0.019960001}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 700 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mI0328 00:55:11.089897 140441181665088 model_lib_v2.py:705] Step 700 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.32186875,\n",
      " 'Loss/localization_loss': 0.016542327,\n",
      " 'Loss/regularization_loss': 0.029595915,\n",
      " 'Loss/total_loss': 0.36800697,\n",
      " 'learning_rate': 0.023120001}\u001b[0m\n",
      "\u001b[34mI0328 00:55:11.090223 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.32186875,\n",
      " 'Loss/localization_loss': 0.016542327,\n",
      " 'Loss/regularization_loss': 0.029595915,\n",
      " 'Loss/total_loss': 0.36800697,\n",
      " 'learning_rate': 0.023120001}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 800 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 00:56:07.749899 140441181665088 model_lib_v2.py:705] Step 800 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.28884575,\n",
      " 'Loss/localization_loss': 0.014378266,\n",
      " 'Loss/regularization_loss': 0.029630333,\n",
      " 'Loss/total_loss': 0.33285436,\n",
      " 'learning_rate': 0.02628}\u001b[0m\n",
      "\u001b[34mI0328 00:56:07.750256 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.28884575,\n",
      " 'Loss/localization_loss': 0.014378266,\n",
      " 'Loss/regularization_loss': 0.029630333,\n",
      " 'Loss/total_loss': 0.33285436,\n",
      " 'learning_rate': 0.02628}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 900 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 00:57:04.473166 140441181665088 model_lib_v2.py:705] Step 900 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.25892597,\n",
      " 'Loss/localization_loss': 0.021475324,\n",
      " 'Loss/regularization_loss': 0.029663911,\n",
      " 'Loss/total_loss': 0.3100652,\n",
      " 'learning_rate': 0.02944}\u001b[0m\n",
      "\u001b[34mI0328 00:57:04.473471 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.25892597,\n",
      " 'Loss/localization_loss': 0.021475324,\n",
      " 'Loss/regularization_loss': 0.029663911,\n",
      " 'Loss/total_loss': 0.3100652,\n",
      " 'learning_rate': 0.02944}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1000 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 00:58:01.157377 140441181665088 model_lib_v2.py:705] Step 1000 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.22086726,\n",
      " 'Loss/localization_loss': 0.011658314,\n",
      " 'Loss/regularization_loss': 0.029700784,\n",
      " 'Loss/total_loss': 0.26222634,\n",
      " 'learning_rate': 0.0326}\u001b[0m\n",
      "\u001b[34mI0328 00:58:01.157665 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.22086726,\n",
      " 'Loss/localization_loss': 0.011658314,\n",
      " 'Loss/regularization_loss': 0.029700784,\n",
      " 'Loss/total_loss': 0.26222634,\n",
      " 'learning_rate': 0.0326}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1100 per-step time 0.596s\u001b[0m\n",
      "\u001b[34mI0328 00:59:00.727945 140441181665088 model_lib_v2.py:705] Step 1100 per-step time 0.596s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.28631297,\n",
      " 'Loss/localization_loss': 0.012804863,\n",
      " 'Loss/regularization_loss': 0.029747168,\n",
      " 'Loss/total_loss': 0.328865,\n",
      " 'learning_rate': 0.03576}\u001b[0m\n",
      "\u001b[34mI0328 00:59:00.728290 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.28631297,\n",
      " 'Loss/localization_loss': 0.012804863,\n",
      " 'Loss/regularization_loss': 0.029747168,\n",
      " 'Loss/total_loss': 0.328865,\n",
      " 'learning_rate': 0.03576}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1200 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 00:59:57.392279 140441181665088 model_lib_v2.py:705] Step 1200 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.29968554,\n",
      " 'Loss/localization_loss': 0.019994535,\n",
      " 'Loss/regularization_loss': 0.029796802,\n",
      " 'Loss/total_loss': 0.34947687,\n",
      " 'learning_rate': 0.03892}\u001b[0m\n",
      "\u001b[34mI0328 00:59:57.392568 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.29968554,\n",
      " 'Loss/localization_loss': 0.019994535,\n",
      " 'Loss/regularization_loss': 0.029796802,\n",
      " 'Loss/total_loss': 0.34947687,\n",
      " 'learning_rate': 0.03892}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1300 per-step time 0.565s\u001b[0m\n",
      "\u001b[34mI0328 01:00:53.925427 140441181665088 model_lib_v2.py:705] Step 1300 per-step time 0.565s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.26589113,\n",
      " 'Loss/localization_loss': 0.013890101,\n",
      " 'Loss/regularization_loss': 0.029922888,\n",
      " 'Loss/total_loss': 0.30970412,\n",
      " 'learning_rate': 0.04208}\u001b[0m\n",
      "\u001b[34mI0328 01:00:53.925768 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.26589113,\n",
      " 'Loss/localization_loss': 0.013890101,\n",
      " 'Loss/regularization_loss': 0.029922888,\n",
      " 'Loss/total_loss': 0.30970412,\n",
      " 'learning_rate': 0.04208}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1400 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 01:01:50.619614 140441181665088 model_lib_v2.py:705] Step 1400 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.3846599,\n",
      " 'Loss/localization_loss': 0.03151042,\n",
      " 'Loss/regularization_loss': 0.02998787,\n",
      " 'Loss/total_loss': 0.44615817,\n",
      " 'learning_rate': 0.04524}\u001b[0m\n",
      "\u001b[34mI0328 01:01:50.619970 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.3846599,\n",
      " 'Loss/localization_loss': 0.03151042,\n",
      " 'Loss/regularization_loss': 0.02998787,\n",
      " 'Loss/total_loss': 0.44615817,\n",
      " 'learning_rate': 0.04524}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1500 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 01:02:47.310118 140441181665088 model_lib_v2.py:705] Step 1500 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.24283071,\n",
      " 'Loss/localization_loss': 0.013128477,\n",
      " 'Loss/regularization_loss': 0.030100165,\n",
      " 'Loss/total_loss': 0.28605935,\n",
      " 'learning_rate': 0.0484}\u001b[0m\n",
      "\u001b[34mI0328 01:02:47.310453 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.24283071,\n",
      " 'Loss/localization_loss': 0.013128477,\n",
      " 'Loss/regularization_loss': 0.030100165,\n",
      " 'Loss/total_loss': 0.28605935,\n",
      " 'learning_rate': 0.0484}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1600 per-step time 0.566s\u001b[0m\n",
      "\u001b[34mI0328 01:03:43.955686 140441181665088 model_lib_v2.py:705] Step 1600 per-step time 0.566s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.2790616,\n",
      " 'Loss/localization_loss': 0.01383378,\n",
      " 'Loss/regularization_loss': 0.030194202,\n",
      " 'Loss/total_loss': 0.32308957,\n",
      " 'learning_rate': 0.05156}\u001b[0m\n",
      "\u001b[34mI0328 01:03:43.956010 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.2790616,\n",
      " 'Loss/localization_loss': 0.01383378,\n",
      " 'Loss/regularization_loss': 0.030194202,\n",
      " 'Loss/total_loss': 0.32308957,\n",
      " 'learning_rate': 0.05156}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1700 per-step time 0.571s\u001b[0m\n",
      "\u001b[34mI0328 01:04:41.039130 140441181665088 model_lib_v2.py:705] Step 1700 per-step time 0.571s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.27401993,\n",
      " 'Loss/localization_loss': 0.016041948,\n",
      " 'Loss/regularization_loss': 0.030286275,\n",
      " 'Loss/total_loss': 0.32034814,\n",
      " 'learning_rate': 0.05472}\u001b[0m\n",
      "\u001b[34mI0328 01:04:41.039424 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.27401993,\n",
      " 'Loss/localization_loss': 0.016041948,\n",
      " 'Loss/regularization_loss': 0.030286275,\n",
      " 'Loss/total_loss': 0.32034814,\n",
      " 'learning_rate': 0.05472}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 1800 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mI0328 01:05:37.793729 140441181665088 model_lib_v2.py:705] Step 1800 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.28801465,\n",
      " 'Loss/localization_loss': 0.014114184,\n",
      " 'Loss/regularization_loss': 0.03037327,\n",
      " 'Loss/total_loss': 0.3325021,\n",
      " 'learning_rate': 0.05788}\u001b[0m\n",
      "\u001b[34mI0328 01:05:37.794088 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.28801465,\n",
      " 'Loss/localization_loss': 0.014114184,\n",
      " 'Loss/regularization_loss': 0.03037327,\n",
      " 'Loss/total_loss': 0.3325021,\n",
      " 'learning_rate': 0.05788}\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Step 1900 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mI0328 01:06:34.626529 140441181665088 model_lib_v2.py:705] Step 1900 per-step time 0.568s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.23173094,\n",
      " 'Loss/localization_loss': 0.01425875,\n",
      " 'Loss/regularization_loss': 0.030507268,\n",
      " 'Loss/total_loss': 0.27649695,\n",
      " 'learning_rate': 0.06104}\u001b[0m\n",
      "\u001b[34mI0328 01:06:34.626842 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.23173094,\n",
      " 'Loss/localization_loss': 0.01425875,\n",
      " 'Loss/regularization_loss': 0.030507268,\n",
      " 'Loss/total_loss': 0.27649695,\n",
      " 'learning_rate': 0.06104}\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Step 2000 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mI0328 01:07:31.324479 140441181665088 model_lib_v2.py:705] Step 2000 per-step time 0.567s\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:{'Loss/classification_loss': 0.2540252,\n",
      " 'Loss/localization_loss': 0.015094813,\n",
      " 'Loss/regularization_loss': 0.030619182,\n",
      " 'Loss/total_loss': 0.29973918,\n",
      " 'learning_rate': 0.06420001}\u001b[0m\n",
      "\u001b[34mI0328 01:07:31.324790 140441181665088 model_lib_v2.py:708] {'Loss/classification_loss': 0.2540252,\n",
      " 'Loss/localization_loss': 0.015094813,\n",
      " 'Loss/regularization_loss': 0.030619182,\n",
      " 'Loss/total_loss': 0.29973918,\n",
      " 'learning_rate': 0.06420001}\u001b[0m\n",
      "\u001b[34m==EVALUATING THE MODEL==\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Forced number of epochs for all eval validations to be 1.\u001b[0m\n",
      "\u001b[34mW0328 01:07:42.112486 140502212597568 model_lib_v2.py:1089] Forced number of epochs for all eval validations to be 1.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting sample_1_of_n_eval_examples: None\u001b[0m\n",
      "\u001b[34mI0328 01:07:42.112874 140502212597568 config_util.py:552] Maybe overwriting sample_1_of_n_eval_examples: None\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mI0328 01:07:42.112997 140502212597568 config_util.py:552] Maybe overwriting use_bfloat16: False\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Maybe overwriting eval_num_epochs: 1\u001b[0m\n",
      "\u001b[34mI0328 01:07:42.113124 140502212597568 config_util.py:552] Maybe overwriting eval_num_epochs: 1\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\u001b[0m\n",
      "\u001b[34mW0328 01:07:42.113281 140502212597568 model_lib_v2.py:1106] Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.214852 140502212597568 ssd_efficientnet_bifpn_feature_extractor.py:150] EfficientDet EfficientNet backbone version: efficientnet-b1\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.215022 140502212597568 ssd_efficientnet_bifpn_feature_extractor.py:152] EfficientDet BiFPN num filters: 88\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.215124 140502212597568 ssd_efficientnet_bifpn_feature_extractor.py:153] EfficientDet BiFPN num iterations: 4\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.220855 140502212597568 efficientnet_model.py:143] round_filter input=32 output=32\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.262649 140502212597568 efficientnet_model.py:143] round_filter input=32 output=32\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.262776 140502212597568 efficientnet_model.py:143] round_filter input=16 output=16\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.445292 140502212597568 efficientnet_model.py:143] round_filter input=16 output=16\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.445405 140502212597568 efficientnet_model.py:143] round_filter input=24 output=24\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.770825 140502212597568 efficientnet_model.py:143] round_filter input=24 output=24\u001b[0m\n",
      "\u001b[34mI0328 01:07:43.770960 140502212597568 efficientnet_model.py:143] round_filter input=40 output=40\u001b[0m\n",
      "\u001b[34mI0328 01:07:44.091315 140502212597568 efficientnet_model.py:143] round_filter input=40 output=40\u001b[0m\n",
      "\u001b[34mI0328 01:07:44.091444 140502212597568 efficientnet_model.py:143] round_filter input=80 output=80\u001b[0m\n",
      "\u001b[34mI0328 01:07:44.520766 140502212597568 efficientnet_model.py:143] round_filter input=80 output=80\u001b[0m\n",
      "\u001b[34mI0328 01:07:44.520905 140502212597568 efficientnet_model.py:143] round_filter input=112 output=112\u001b[0m\n",
      "\u001b[34mI0328 01:07:44.951530 140502212597568 efficientnet_model.py:143] round_filter input=112 output=112\u001b[0m\n",
      "\u001b[34mI0328 01:07:44.951653 140502212597568 efficientnet_model.py:143] round_filter input=192 output=192\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.476803 140502212597568 efficientnet_model.py:143] round_filter input=192 output=192\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.476948 140502212597568 efficientnet_model.py:143] round_filter input=320 output=320\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.688951 140502212597568 efficientnet_model.py:143] round_filter input=1280 output=1280\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.739643 140502212597568 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading unweighted datasets: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.802077 140502212597568 dataset_builder.py:162] Reading unweighted datasets: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Reading record datasets for input file: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.803447 140502212597568 dataset_builder.py:79] Reading record datasets for input file: ['/opt/ml/input/data/val/*.tfrecord']\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Number of filenames to read: 13\u001b[0m\n",
      "\u001b[34mI0328 01:07:45.803586 140502212597568 dataset_builder.py:80] Number of filenames to read: 13\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:num_readers has been reduced to 13 to match input file shards.\u001b[0m\n",
      "\u001b[34mW0328 01:07:45.803693 140502212597568 dataset_builder.py:86] num_readers has been reduced to 13 to match input file shards.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:`shuffle` is false, but the input data stream is still slightly shuffled since `num_readers` > 1.\u001b[0m\n",
      "\u001b[34mW0328 01:07:45.805427 140502212597568 dataset_builder.py:93] `shuffle` is false, but the input data stream is still slightly shuffled since `num_readers` > 1.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mW0328 01:07:45.807420 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:100: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.deterministic`.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mW0328 01:07:45.825652 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/object_detection/builders/dataset_builder.py:235: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.data.Dataset.map()\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mW0328 01:07:46.473110 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mW0328 01:07:49.831758 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mCreate a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mW0328 01:07:51.259612 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Waiting for new checkpoint at /opt/training\u001b[0m\n",
      "\u001b[34mI0328 01:07:53.868934 140502212597568 checkpoint_utils.py:140] Waiting for new checkpoint at /opt/training\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Found new checkpoint at /opt/training/ckpt-3\u001b[0m\n",
      "\u001b[34mI0328 01:07:53.869555 140502212597568 checkpoint_utils.py:149] Found new checkpoint at /opt/training/ckpt-3\u001b[0m\n",
      "\u001b[34m/usr/local/lib/python3.8/dist-packages/keras/backend.py:451: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn(\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mW0328 01:08:29.001181 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/util/dispatch.py:1176: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mUse `tf.cast` instead.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Finished eval step 0\u001b[0m\n",
      "\u001b[34mI0328 01:08:29.033480 140502212597568 model_lib_v2.py:966] Finished eval step 0\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:459: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mtf.py_func is deprecated in TF V2. Instead, there are two\n",
      "    options available in V2.\n",
      "    - tf.py_function takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
      "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
      "    stateful argument making all functions stateful.\n",
      "    \u001b[0m\n",
      "\u001b[34mW0328 01:08:29.184613 140502212597568 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:459: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mtf.py_func is deprecated in TF V2. Instead, there are two\n",
      "    options available in V2.\n",
      "    - tf.py_function takes a python function which manipulates tf eager\n",
      "    tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n",
      "    an ndarray (just call tensor.numpy()) but having access to eager tensors\n",
      "    means `tf.py_function`s can use accelerators such as GPUs as well as\n",
      "    being differentiable using a gradient tape.\n",
      "    - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n",
      "    (it is not differentiable, and manipulates numpy arrays). It drops the\n",
      "    stateful argument making all functions stateful.\n",
      "    \u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Finished eval step 100\u001b[0m\n",
      "\u001b[34mI0328 01:08:39.975919 140502212597568 model_lib_v2.py:966] Finished eval step 100\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Finished eval step 200\u001b[0m\n",
      "\u001b[34mI0328 01:08:47.286592 140502212597568 model_lib_v2.py:966] Finished eval step 200\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Performing evaluation on 258 images.\u001b[0m\n",
      "\u001b[34mI0328 01:08:51.369329 140502212597568 coco_evaluation.py:293] Performing evaluation on 258 images.\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Loading and preparing annotation results...\u001b[0m\n",
      "\u001b[34mI0328 01:08:51.374888 140502212597568 coco_tools.py:116] Loading and preparing annotation results...\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:DONE (t=0.01s)\u001b[0m\n",
      "\u001b[34mI0328 01:08:51.389627 140502212597568 coco_tools.py:138] DONE (t=0.01s)\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Eval metrics at step 2000\u001b[0m\n",
      "\u001b[34mI0328 01:09:05.979646 140502212597568 model_lib_v2.py:1015] Eval metrics at step 2000\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP: 0.076311\u001b[0m\n",
      "\u001b[34mI0328 01:09:05.993146 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP: 0.076311\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP@.50IOU: 0.184656\u001b[0m\n",
      "\u001b[34mI0328 01:09:05.994580 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP@.50IOU: 0.184656\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP@.75IOU: 0.052877\u001b[0m\n",
      "\u001b[34mI0328 01:09:05.995963 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP@.75IOU: 0.052877\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP (small): 0.024017\u001b[0m\n",
      "\u001b[34mI0328 01:09:05.997376 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP (small): 0.024017\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP (medium): 0.326107\u001b[0m\n",
      "\u001b[34mI0328 01:09:05.998735 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP (medium): 0.326107\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Precision/mAP (large): 0.388688\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.000088 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Precision/mAP (large): 0.388688\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@1: 0.020425\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.001492 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@1: 0.020425\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@10: 0.086712\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.002859 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@10: 0.086712\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100: 0.115894\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.004273 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100: 0.115894\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100 (small): 0.055665\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.005633 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100 (small): 0.055665\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100 (medium): 0.414811\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.006985 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100 (medium): 0.414811\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ DetectionBoxes_Recall/AR@100 (large): 0.444962\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.008388 140502212597568 model_lib_v2.py:1018] #011+ DetectionBoxes_Recall/AR@100 (large): 0.444962\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/localization_loss: 0.026075\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.009453 140502212597568 model_lib_v2.py:1018] #011+ Loss/localization_loss: 0.026075\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/classification_loss: 0.370959\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.010545 140502212597568 model_lib_v2.py:1018] #011+ Loss/classification_loss: 0.370959\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/regularization_loss: 0.030621\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.011614 140502212597568 model_lib_v2.py:1018] #011+ Loss/regularization_loss: 0.030621\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:#011+ Loss/total_loss: 0.427654\u001b[0m\n",
      "\u001b[34mI0328 01:09:06.012726 140502212597568 model_lib_v2.py:1018] #011+ Loss/total_loss: 0.427654\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Waiting for new checkpoint at /opt/training\u001b[0m\n",
      "\u001b[34mI0328 01:12:53.958076 140502212597568 checkpoint_utils.py:140] Waiting for new checkpoint at /opt/training\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Timed-out waiting for a checkpoint.\u001b[0m\n",
      "\u001b[34mI0328 01:13:02.973814 140502212597568 checkpoint_utils.py:203] Timed-out waiting for a checkpoint.\u001b[0m\n",
      "\u001b[34mcreating index...\u001b[0m\n",
      "\u001b[34mindex created!\u001b[0m\n",
      "\u001b[34mcreating index...\u001b[0m\n",
      "\u001b[34mindex created!\u001b[0m\n",
      "\u001b[34mRunning per image evaluation...\u001b[0m\n",
      "\u001b[34mEvaluate annotation type *bbox*\u001b[0m\n",
      "\u001b[34mDONE (t=14.29s).\u001b[0m\n",
      "\u001b[34mAccumulating evaluation results...\u001b[0m\n",
      "\u001b[34mDONE (t=0.26s).\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.076\n",
      " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.185\n",
      " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.053\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.024\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.326\n",
      " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.389\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.020\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.087\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.116\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.056\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.415\n",
      " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.445\u001b[0m\n",
      "\u001b[34m==EXPORTING THE MODEL==\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.203425 140550616389440 ssd_efficientnet_bifpn_feature_extractor.py:150] EfficientDet EfficientNet backbone version: efficientnet-b1\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.203611 140550616389440 ssd_efficientnet_bifpn_feature_extractor.py:152] EfficientDet BiFPN num filters: 88\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.203691 140550616389440 ssd_efficientnet_bifpn_feature_extractor.py:153] EfficientDet BiFPN num iterations: 4\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.208562 140550616389440 efficientnet_model.py:143] round_filter input=32 output=32\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.249630 140550616389440 efficientnet_model.py:143] round_filter input=32 output=32\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.249772 140550616389440 efficientnet_model.py:143] round_filter input=16 output=16\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.431888 140550616389440 efficientnet_model.py:143] round_filter input=16 output=16\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.432029 140550616389440 efficientnet_model.py:143] round_filter input=24 output=24\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.769793 140550616389440 efficientnet_model.py:143] round_filter input=24 output=24\u001b[0m\n",
      "\u001b[34mI0328 01:13:09.769945 140550616389440 efficientnet_model.py:143] round_filter input=40 output=40\u001b[0m\n",
      "\u001b[34mI0328 01:13:10.099148 140550616389440 efficientnet_model.py:143] round_filter input=40 output=40\u001b[0m\n",
      "\u001b[34mI0328 01:13:10.099298 140550616389440 efficientnet_model.py:143] round_filter input=80 output=80\u001b[0m\n",
      "\u001b[34mI0328 01:13:10.530757 140550616389440 efficientnet_model.py:143] round_filter input=80 output=80\u001b[0m\n",
      "\u001b[34mI0328 01:13:10.530912 140550616389440 efficientnet_model.py:143] round_filter input=112 output=112\u001b[0m\n",
      "\u001b[34mI0328 01:13:10.963073 140550616389440 efficientnet_model.py:143] round_filter input=112 output=112\u001b[0m\n",
      "\u001b[34mI0328 01:13:10.963238 140550616389440 efficientnet_model.py:143] round_filter input=192 output=192\u001b[0m\n",
      "\u001b[34mI0328 01:13:11.495482 140550616389440 efficientnet_model.py:143] round_filter input=192 output=192\u001b[0m\n",
      "\u001b[34mI0328 01:13:11.495653 140550616389440 efficientnet_model.py:143] round_filter input=320 output=320\u001b[0m\n",
      "\u001b[34mI0328 01:13:11.715111 140550616389440 efficientnet_model.py:143] round_filter input=1280 output=1280\u001b[0m\n",
      "\u001b[34mI0328 01:13:11.767736 140550616389440 efficientnet_model.py:453] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mW0328 01:13:16.280551 140550616389440 deprecation.py:350] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mLambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mback_prop=False is deprecated. Consider using tf.stop_gradient instead.\u001b[0m\n",
      "\u001b[34mInstead of:\u001b[0m\n",
      "\u001b[34mresults = tf.map_fn(fn, elems, back_prop=False)\u001b[0m\n",
      "\u001b[34mUse:\u001b[0m\n",
      "\u001b[34mresults = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\u001b[0m\n",
      "\u001b[34mW0328 01:13:16.496555 140550616389440 deprecation.py:623] From /usr/local/lib/python3.8/dist-packages/tensorflow/python/autograph/impl/api.py:458: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\u001b[0m\n",
      "\u001b[34mInstructions for updating:\u001b[0m\n",
      "\u001b[34mback_prop=False is deprecated. Consider using tf.stop_gradient instead.\u001b[0m\n",
      "\u001b[34mInstead of:\u001b[0m\n",
      "\u001b[34mresults = tf.map_fn(fn, elems, back_prop=False)\u001b[0m\n",
      "\u001b[34mUse:\u001b[0m\n",
      "\u001b[34mresults = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\u001b[0m\n",
      "\u001b[34mWARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fd3c00bad60>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 01:13:44.163936 140550616389440 save_impl.py:66] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7fd3c00bad60>, because it is not built.\u001b[0m\n",
      "\u001b[34mW0328 01:14:51.170687 140550616389440 save.py:271] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalClassHead_layer_call_fn while saving (showing 5 of 535). These functions will not be directly callable after loading.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mINFO:tensorflow:Assets written to: /tmp/exported/saved_model/assets\u001b[0m\n",
      "\u001b[34mI0328 01:15:26.552504 140550616389440 builder_impl.py:797] Assets written to: /tmp/exported/saved_model/assets\u001b[0m\n",
      "\u001b[34mINFO:tensorflow:Writing pipeline config file to /tmp/exported/pipeline.config\u001b[0m\n",
      "\u001b[34mI0328 01:15:31.065593 140550616389440 config_util.py:253] Writing pipeline config file to /tmp/exported/pipeline.config\u001b[0m\n",
      "\u001b[34m2023-03-28 01:15:34,405 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2023-03-28 01:15:53 Uploading - Uploading generated training model\n",
      "2023-03-28 01:15:53 Completed - Training job completed\n",
      "Training seconds: 2000\n",
      "Billable seconds: 2000\n"
     ]
    }
   ],
   "source": [
    "tensorboard_output_config = sagemaker.debugger.TensorBoardOutputConfig(\n",
    "    s3_output_path=tensorboard_s3_prefix,\n",
    "    container_local_output_path='/opt/training/'\n",
    ")\n",
    "\n",
    "estimator = CustomFramework(\n",
    "    role=role,\n",
    "    image_uri=container,\n",
    "    entry_point='run_training.sh',\n",
    "    source_dir='source_dir/',\n",
    "    hyperparameters={\n",
    "        \"model_dir\":\"/opt/training\",        \n",
    "        \"pipeline_config_path\": \"pipeline.config\",\n",
    "        #\"num_train_steps\": \"200\",    \n",
    "        \"num_train_steps\": \"2000\",    \n",
    "        \"sample_1_of_n_eval_examples\": \"1\"\n",
    "    },\n",
    "    instance_count=1,\n",
    "    instance_type='ml.p3.2xlarge',\n",
    "    tensorboard_output_config=tensorboard_output_config,\n",
    "    disable_profiler=True,\n",
    "    base_job_name='tf2-object-detection'\n",
    ")\n",
    "\n",
    "estimator.fit(inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84545881",
   "metadata": {},
   "source": [
    "You should be able to see your model training in the AWS webapp as shown below:\n",
    "![ECR Example](../data/example_trainings.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c4e38719",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:botocore.credentials:Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Output path:\n",
      "s3://sagemaker-us-east-1-476375380884/\n",
      "\n",
      "Last model URI:\n",
      "s3://sagemaker-us-east-1-476375380884/tf2-object-detection-2023-03-28-00-40-58-060/output/model.tar.gz\n"
     ]
    }
   ],
   "source": [
    "# Inspired by: https://stackoverflow.com/questions/36209068/boto3-grabbing-only-selected-objects-from-the-s3-resource\n",
    "\n",
    "import boto3\n",
    "\n",
    "output_path = estimator.output_path\n",
    "print('Output path:\\n{}\\n'.format(output_path))\n",
    "\n",
    "bucket_name = output_path[5:-1]\n",
    "s3 = boto3.resource('s3')\n",
    "my_bucket = s3.Bucket(bucket_name)\n",
    "\n",
    "prefix = 'tf2-object-detection-'\n",
    "model_objects = my_bucket.objects.filter(Prefix=prefix)\n",
    "model_objects = [object_summary for object_summary in model_objects if object_summary.key.endswith('model.tar.gz')]\n",
    "model_objects = sorted(model_objects, key = lambda e: e.key)\n",
    "#for model_object in model_objects: print(model_object)\n",
    "last_model_object = model_objects[-1]\n",
    "last_model_uri = 's3://' + last_model_object.bucket_name + '/' + last_model_object.key\n",
    "print('Last model URI:\\n{}'.format(last_model_uri))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "500e3fab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aws_region=us-east-1\n"
     ]
    }
   ],
   "source": [
    "import sagemaker\n",
    "sagemaker_session = sagemaker.Session()\n",
    "aws_region = sagemaker_session.boto_region_name\n",
    "print('aws_region={}'.format(aws_region))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e722407",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pip install 'tensorflow<2.4'\n",
      "pip install 'tensorflow-io<2.4'\n",
      "pip install 'tensorboard<2.4'\n",
      "AWS_REGION=us-east-1\n",
      "tensorboard --logdir=s3://object-detection-project-jckuri/logs/tf2-object-detection-2023-03-28-00-40-58-060/tensorboard-output\n"
     ]
    }
   ],
   "source": [
    "job_artifacts_path = estimator.latest_job_tensorboard_artifacts_path()\n",
    "\n",
    "!echo \"pip install 'tensorflow<2.4'\"\n",
    "!echo \"pip install 'tensorflow-io<2.4'\"\n",
    "!echo \"pip install 'tensorboard<2.4'\"\n",
    "!echo \"AWS_REGION={aws_region}\"\n",
    "!echo \"tensorboard --logdir={job_artifacts_path}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a8c6b6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class URL:\n",
    "    \n",
    "    def __init__(self, url):\n",
    "        self.url = url\n",
    "    \n",
    "    def _repr_html_(self):\n",
    "        return '<a href=\"{}\">{}</a>'.format(self.url, self.url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "daed91bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"https://object-detection-project-a5lc.notebook.us-east-1.sagemaker.aws/proxy/6006/\">https://object-detection-project-a5lc.notebook.us-east-1.sagemaker.aws/proxy/6006/</a>"
      ],
      "text/plain": [
       "<__main__.URL at 0x7f05becc50f0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://<notebook instance hostname>/proxy/6006/\n",
    "jupyter_notebook_url = 'https://object-detection-project-a5lc.notebook.us-east-1.sagemaker.aws'\n",
    "url = '{}/proxy/6006/'.format(jupyter_notebook_url)\n",
    "\n",
    "o = URL(url)\n",
    "o"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9844f25",
   "metadata": {},
   "source": [
    "## Improve on the initial model\n",
    "\n",
    "Most likely, this initial experiment did not yield optimal results. However, you can make multiple changes to the `pipeline.config` file to improve this model. One obvious change consists in improving the data augmentation strategy. The [`preprocessor.proto`](https://github.com/tensorflow/models/blob/master/research/object_detection/protos/preprocessor.proto) file contains the different data augmentation method available in the Tf Object Detection API. Justify your choices of augmentations in the writeup.\n",
    "\n",
    "Keep in mind that the following are also available:\n",
    "* experiment with the optimizer: type of optimizer, learning rate, scheduler etc\n",
    "* experiment with the architecture. The Tf Object Detection API model zoo offers many architectures. Keep in mind that the pipeline.config file is unique for each architecture and you will have to edit it.\n",
    "* visualize results on the test frames using the `2_deploy_model` notebook available in this repository.\n",
    "\n",
    "In the cell below, write down all the different approaches you have experimented with, why you have chosen them and what you would have done if you had more time and resources. Justify your choices using the tensorboard visualizations (take screenshots and insert them in your writeup), the metrics on the evaluation set and the generated animation you have created with [this tool](../2_run_inference/2_deploy_model.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "17284a60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# your writeup goes here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a3411c",
   "metadata": {},
   "source": [
    "**I preferred to write all my documentation in the file <big><big>[README.md](../README.md)</big></big>.<br/>\n",
    "Please, click on this file and read it because it explains all the items in the rubric.**"
   ]
  }
 ],
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