{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9e053b-90e0-4755-b80f-e4d0d7888c63",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.\n",
    "# SPDX-License-Identifier: MIT-0\n",
    "\n",
    "# REQUIRED: Set to the model pathname\n",
    "MODEL_PATH = \"/root/model/\"\n",
    "\n",
    "# SageMaker endpoint requires a bundled model with a numerical version number as the directory name\n",
    "MODEL_VERSION = \"0001\"\n",
    "\n",
    "# Select which type of SageMaker EC2 instance to deploy the model on  \n",
    "INSTANCE_TYPE = 'ml.c5.xlarge' \n",
    "\n",
    "# Specify the number of instances\n",
    "INSTANCE_NUM = 1\n",
    "\n",
    "# Specify the name for the SageMaker endpoint\n",
    "ENDPOINT_NAME = \"cell-segmentation-model\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e082d2-aea5-4bf2-bcfd-7caaacd4ed02",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker import get_execution_role\n",
    "from sagemaker import Session\n",
    "from sagemaker.tensorflow.serving import Model\n",
    "from datetime import datetime\n",
    "role = get_execution_role()\n",
    "sess = Session()\n",
    "bucket = sess.default_bucket()\n",
    "\n",
    "import tensorflow as tf\n",
    "import shutil\n",
    "import os\n",
    "import tarfile\n",
    "\n",
    "print(tf.__version__)  # This notebook runs on TensorFlow 2.11.0 as tested for the workshop\n",
    "tf_framework_version = tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f080f4fd-9097-49bc-b57f-b7fe308fa58a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Display the default bucket associated to the SageMaker instance\n",
    "print(bucket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "345c1288-d4a1-4eb2-9ab0-bd259bdf88f5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_bundle = MODEL_PATH + MODEL_VERSION\n",
    "\n",
    "if os.path.exists(model_bundle) == False:\n",
    "    shutil.copytree(MODEL_PATH + \"unet-cell-segmentation-v2-512x512.model\", model_bundle)\n",
    "\n",
    "!saved_model_cli show --all --dir {model_bundle}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d06e2d79-fafc-4785-ad51-ccf439edb95a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_archive = MODEL_PATH + 'cell-segmentation-model.tar.gz'\n",
    "\n",
    "with tarfile.open(model_archive, mode='w:gz') as archive:\n",
    "    archive.add(model_bundle, recursive=True) \n",
    "\n",
    "# upload model artifacts to S3\n",
    "model_s3 = sess.upload_data(path=model_archive, key_prefix='data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c16994cc-69a3-4932-b039-a283a743bcfa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(model_s3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e1350d2-d569-48c5-aac3-ecaf1a21d87f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Instantiate the SageMaker TensorFlow serving model  \n",
    "sm_model = Model(model_data=model_s3, \n",
    "                 framework_version=tf_framework_version,\n",
    "                 role=role)\n",
    "\n",
    "# v2.0 and later\n",
    "#Model(\"unet-cell-segmentation:latest\", role=role, framework_version=tf_framework_version)\n",
    "#, model_data=\"s3://sagemaker-ap-southeast-2-072766311530/model/model.tar.gz\")\n",
    "\n",
    "uncompiled_predictor = sm_model.deploy(endpoint_name=ENDPOINT_NAME, initial_instance_count=INSTANCE_NUM, instance_type=INSTANCE_TYPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "506cd9cf-821f-47df-b8ee-80d06278b4a7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\"Successfully created SageMaker Endpoint => \", ENDPOINT_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41f7ce21-d28b-478a-8091-3b52c84d0af4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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}