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Scope IAM PassRole to one role name (aws#2963)
* Tensorflow BYO - fix passrole, SM v2 * Tensorflow BYO - fix kernel and formatting * Greengrass - fix passrole * RL markdown helper - fix passrole * Batch BYO - fix passrole, small bugs * Fairness - fix passrole * Fix formatting * Remove duplicate markdown sentence
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‎aws_sagemaker_studio/sagemaker_studio_image_build/tensorflow_bring_your_own/tensorflow_bring_your_own.ipynb

+50-49
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@@ -6,7 +6,7 @@
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"source": [
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"# Building your own TensorFlow container from Amazon SageMaker Studio\n",
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"\n",
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"**STUDIO KERNEL NOTE:** If you are prompted for Kernel, choose 'Python 3 (TensorFlow CPU Optimized) \n",
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"**STUDIO KERNEL NOTE:** If you are prompted for Kernel, choose 'Python 3 (TensorFlow 2.1 Python 3.6 CPU Optimized) \n",
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"\n",
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"With Amazon SageMaker, you can package your own algorithms that can then be trained and deployed in the SageMaker environment. This notebook guides you through an example using TensorFlow that shows you how to build a Docker container for SageMaker and use it for training and inference. \n",
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"\n",
@@ -300,9 +300,10 @@
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]
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},
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{
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"cell_type": "raw",
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```\n",
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"{\n",
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" \"Version\": \"2012-10-17\",\n",
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" \"Statement\": [\n",
@@ -323,7 +324,8 @@
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" \"Action\": \"sts:AssumeRole\"\n",
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" }\n",
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" ]\n",
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"}"
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"}\n",
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"```"
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]
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},
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{
@@ -338,17 +340,20 @@
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" \n",
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" * Open [Policies](https://console.aws.amazon.com/iam/home#/policies) in IAM\n",
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" * Click **Create policy**\n",
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" * Select the JSON tab and copy/paste the policy below"
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" * Select the JSON tab and copy/paste the printed result of the policy below"
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]
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},
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{
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"cell_type": "raw",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"{\n",
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"print(\n",
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" f\"\"\"{{\n",
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" \"Version\": \"2012-10-17\",\n",
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" \"Statement\": [\n",
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" {\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": [\n",
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" \"codebuild:DeleteProject\",\n",
@@ -357,26 +362,26 @@
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" \"codebuild:StartBuild\"\n",
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" ],\n",
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" \"Resource\": \"arn:aws:codebuild:*:*:project/sagemaker-studio*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": \"logs:CreateLogStream\",\n",
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" \"Resource\": \"arn:aws:logs:*:*:log-group:/aws/codebuild/sagemaker-studio*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": [\n",
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" \"logs:GetLogEvents\",\n",
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" \"logs:PutLogEvents\"\n",
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" ],\n",
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" \"Resource\": \"arn:aws:logs:*:*:log-group:/aws/codebuild/sagemaker-studio*:log-stream:*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": \"logs:CreateLogGroup\",\n",
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" \"Resource\": \"*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": [\n",
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" \"ecr:CreateRepository\",\n",
@@ -391,48 +396,49 @@
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" \"ecr:PutImage\"\n",
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" ],\n",
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" \"Resource\": \"arn:aws:ecr:*:*:repository/sagemaker-studio*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": \"ecr:GetAuthorizationToken\",\n",
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" \"Resource\": \"*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": [\n",
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" \"s3:GetObject\",\n",
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" \"s3:DeleteObject\",\n",
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" \"s3:PutObject\"\n",
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" ],\n",
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" \"Resource\": \"arn:aws:s3:::sagemaker-*/*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": [\n",
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" \"s3:CreateBucket\"\n",
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" ],\n",
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" \"Resource\": \"arn:aws:s3:::sagemaker*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": [\n",
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" \"iam:GetRole\",\n",
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" \"iam:ListRoles\"\n",
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" ],\n",
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" \"Resource\": \"*\"\n",
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" },\n",
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" {\n",
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" }},\n",
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" {{\n",
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" \"Effect\": \"Allow\",\n",
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" \"Action\": \"iam:PassRole\",\n",
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" \"Resource\": \"arn:aws:iam::*:role/*\",\n",
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" \"Condition\": {\n",
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" \"StringLikeIfExists\": {\n",
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" \"Resource\": \"{role}\",\n",
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" \"Condition\": {{\n",
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" \"StringLikeIfExists\": {{\n",
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" \"iam:PassedToService\": \"codebuild.amazonaws.com\"\n",
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" }\n",
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" }\n",
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" }\n",
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" }}\n",
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" }}\n",
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" }}\n",
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" ]\n",
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"}"
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"}}\"\"\"\n",
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")"
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]
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},
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{
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"We now need to attach our policy to the Execution Role attached to this notebook environment. \n",
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"\n",
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" * Go back to [Roles](https://console.aws.amazon.com/iam/home#/roles) in IAM\n",
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" * Select the SageMaker Execution Role from abovee\n",
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" * Select the SageMaker Execution Role from above\n",
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" * On the **Permissions** tab, click **Attach policies**\n",
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" * Search for the Policy we created above `Studio-Image-Build-Policy`\n",
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" * Select the policy and click **Attach policy**"
@@ -647,9 +653,10 @@
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"\n",
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"estimator = Estimator(\n",
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" role=role,\n",
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" train_instance_count=1,\n",
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" train_instance_type=instance_type,\n",
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" instance_count=1,\n",
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" instance_type=instance_type,\n",
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" image_name=ecr_image,\n",
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" image_uri=ecr_image,\n",
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" hyperparameters=hyperparameters,\n",
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")\n",
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"\n",
@@ -740,8 +747,6 @@
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"import imageio as imageio\n",
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"import numpy\n",
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"\n",
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"from sagemaker.predictor import json_serializer, json_deserializer\n",
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"\n",
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"image = imageio.imread(\"data/cat.png\")\n",
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"print(image.shape)\n",
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"\n",
@@ -758,20 +763,16 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"jupyter": {
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"source_hidden": true
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}
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# The request and response format is JSON for TensorFlow Serving.\n",
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"# For more information: https://www.tensorflow.org/serving/api_rest#predict_api\n",
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"predictor.accept = \"application/json\"\n",
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"predictor.content_type = \"application/json\"\n",
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"from sagemaker.serializers import JSONSerializer\n",
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"from sagemaker.deserializers import JSONDeserializer\n",
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"\n",
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"predictor.serializer = json_serializer\n",
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"predictor.deserializer = json_deserializer\n",
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"predictor.serializer = JSONSerializer()\n",
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"predictor.deserializer = JSONDeserializer()\n",
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"\n",
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"# For more information on the predictor class.\n",
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"# https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/predictor.py\n",
@@ -821,9 +822,9 @@
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"metadata": {
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"instance_type": "ml.t3.medium",
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"kernelspec": {
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"display_name": "Python 3 (TensorFlow CPU Optimized)",
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"display_name": "Python 3 (TensorFlow 2.1 Python 3.6 CPU Optimized)",
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"language": "python",
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"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-west-2:236514542706:image/tensorflow-1.15-cpu-py36"
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"name": "python3__SAGEMAKER_INTERNAL__arn:aws:sagemaker:us-east-2:429704687514:image/tensorflow-2.1-cpu-py36"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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"version": "3.6.13"
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}
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},
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"nbformat": 4,

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