{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1f1bc94-666d-4184-9654-a6b2e6bdd098",
   "metadata": {},
   "source": [
    "# コード開発環境と実行環境（Studio Notebooks と Training Jobs) で同じ環境(コンテナイメージ)を使用する\n",
    "1_ready.ipynb で作成した環境を Studio Notebooks と Training Jobs で利用する\n",
    "\n",
    "## 使用するカーネルの設定\n",
    "\n",
    "右上の部分をクリックして、 `1_ready.ipynb` で作成した、カーネルを選択する\n",
    "![change_kernel](./img/change_kernel.png)\n",
    "![select_kernel](./img/select_kernel.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "489bf66d-5443-43d2-ae61-8e63aeef96da",
   "metadata": {},
   "source": [
    "選択したカーネルでコードを開発する前に、Dockerfile や requirements.txt を確認してコンテナを確認する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89e44eee-8e83-466b-afc4-2f4e4f11a1c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cat /root/Dockerfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "591fe6ab-e6f2-4972-b60f-0d1bc88a0b05",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cat /root/requirements.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ad455c-c1c7-4814-8c52-a3b262c95274",
   "metadata": {},
   "source": [
    "コード開発前にトレーニングジョブ実行に必要な定数を設定する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee3ddd2c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import yaml\n",
    "import sagemaker\n",
    "import boto3\n",
    "region = boto3.session.Session().region_name\n",
    "account = boto3.client('sts').get_caller_identity().get('Account')\n",
    "\n",
    "\n",
    "# 定数ファイル読み込み\n",
    "with open('constant_config.yml','rt') as f:\n",
    "    constant_config = yaml.safe_load(f)\n",
    "\n",
    "REPOSITORY_NAME = constant_config['REPOSITORY_NAME']\n",
    "IMAGE_NAME = constant_config['IMAGE_NAME']\n",
    "role = sagemaker.get_execution_role()\n",
    "image_uri = f'{account}.dkr.ecr.{region}.amazonaws.com/{REPOSITORY_NAME}:{IMAGE_NAME}'\n",
    "print(f'region: {region}')\n",
    "print(f'account : {account}')\n",
    "print(f'role : {role}')\n",
    "print(f'REPOSITORY_NAME : {REPOSITORY_NAME}')\n",
    "print(f'IMAGE_NAME : {IMAGE_NAME}')\n",
    "print(f'image_uri : {image_uri}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1616acb1-7df1-4198-b0fd-811088074e3a",
   "metadata": {},
   "source": [
    "コードを開発する。今回は wx+b をするコードを開発したこととする"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d9d3db3-86eb-4f6d-96b6-08143add5929",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 出来上がったコード\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(seed=1234)\n",
    "w = np.random.rand(8).reshape(4,2)\n",
    "x  = np.random.rand(4)\n",
    "b =  np.random.rand(2)\n",
    "\n",
    "print(np.dot(x,w)+b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e6cdef8-2e21-4749-9f36-778929e4dee1",
   "metadata": {},
   "source": [
    "トレーニングジョブで動かすために .py ファイルに書き出す"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6ce7b07",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%writefile train.py\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(seed=1234)\n",
    "w = np.random.rand(8).reshape(4,2)\n",
    "x  = np.random.rand(4)\n",
    "b =  np.random.rand(2)\n",
    "\n",
    "print(np.dot(x,w)+b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecf09763-5711-4096-b576-5ff8e21fcc2c",
   "metadata": {},
   "source": [
    ".py ファイルを用いてトレーニングジョブを動かす。開発したコードと同じ結果が得られることを確認する  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9190b382",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "estimator = sagemaker.estimator.Estimator(\n",
    "    image_uri = image_uri,\n",
    "    role = sagemaker.get_execution_role(),\n",
    "    entry_point='train.py',\n",
    "    instance_type = 'ml.m5.xlarge',\n",
    "    instance_count=1\n",
    ")\n",
    "estimator.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4949d05-d9e2-479c-87a4-16005e29a0dd",
   "metadata": {},
   "source": [
    "このように、開発環境と実行環境を統一することで、環境差異に悩まされずに開発をすすめることができる"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8a5dd3d-83ca-447b-8137-394d52e44f04",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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