mirror of
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408 lines
13 KiB
Plaintext
408 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copyright (c) Microsoft Corporation. All rights reserved.\n",
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"\n",
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"Licensed under the MIT License."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
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"This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
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"We then test and delete the service, image and model."
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]
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},
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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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"outputs": [],
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"source": [
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"from azureml.core import Workspace\n",
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"from azureml.core.compute import AksCompute, ComputeTarget\n",
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"from azureml.core.webservice import Webservice, AksWebservice\n",
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"from azureml.core.image import Image\n",
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"from azureml.core.model import Model"
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]
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},
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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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"outputs": [],
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"source": [
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"import azureml.core\n",
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"print(azureml.core.VERSION)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Get workspace\n",
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"Load existing workspace from the config file info."
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]
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},
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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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"outputs": [],
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"source": [
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"from azureml.core.workspace import Workspace\n",
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"\n",
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"ws = Workspace.from_config()\n",
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"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Register the model\n",
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"Register an existing trained model, add descirption and tags. Prior to registering the model, you should have a TensorFlow [Saved Model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md) in the `resnet50` directory. You can download a [pretrained resnet50](https://github.com/tensorflow/models/tree/master/official/resnet#pre-trained-model) and unpack it to that directory."
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]
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},
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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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"outputs": [],
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"source": [
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"#Register the model\n",
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"from azureml.core.model import Model\n",
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"model = Model.register(model_path = \"resnet50\", # this points to a local file\n",
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" model_name = \"resnet50\", # this is the name the model is registered as\n",
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" tags = {'area': \"Image classification\", 'type': \"classification\"},\n",
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" description = \"Image classification trained on Imagenet Dataset\",\n",
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" workspace = ws)\n",
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"\n",
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"print(model.name, model.description, model.version)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Create an image\n",
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"Create an image using the registered model the script that will load and run the model."
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]
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},
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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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"outputs": [],
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"source": [
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"%%writefile score.py\n",
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"import ujson\n",
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"from azureml.core.model import Model\n",
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"from azureml.contrib.services.aml_request import AMLRequest, rawhttp\n",
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"from azureml.contrib.services.aml_response import AMLResponse\n",
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"\n",
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"def init():\n",
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" global session\n",
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" global input_name\n",
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" global output_name\n",
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" \n",
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" session = tf.Session()\n",
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"\n",
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" model_path = Model.get_model_path('resnet50')\n",
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" model = tf.saved_model.loader.load(session, ['serve'], model_path)\n",
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" if len(model.signature_def['serving_default'].inputs) > 1:\n",
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" raise ValueError(\"This score.py only supports one input\")\n",
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" if len(model.signature_def['serving_default'].outputs) > 1:\n",
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" raise ValueError(\"This score.py only supports one input\")\n",
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" input_name = [tensor.name for tensor in model.signature_def['serving_default'].inputs.values()][0]\n",
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" output_name = [tensor.name for tensor in model.signature_def['serving_default'].outputs.values()][0]\n",
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" \n",
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"\n",
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"@rawhttp\n",
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"def run(request):\n",
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" if request.method == 'POST':\n",
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" reqBody = request.get_data(False)\n",
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" resp = score(reqBody)\n",
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" return AMLResponse(resp, 200)\n",
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" if request.method == 'GET':\n",
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" respBody = str.encode(\"GET is not supported\")\n",
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" return AMLResponse(respBody, 405)\n",
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" return AMLResponse(\"bad request\", 500)\n",
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"\n",
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"def score(data):\n",
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" result = session.run(output_name, {input_name: [data]})\n",
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" return ujson.dumps(result[0])\n",
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"\n",
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"if __name__ == \"__main__\":\n",
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" init()\n",
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" with open(\"test_image.jpg\", 'rb') as f:\n",
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" content = f.read()\n",
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" print(score(content))"
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]
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},
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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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"outputs": [],
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"source": [
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"from azureml.core.conda_dependencies import CondaDependencies \n",
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"\n",
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"myenv = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy','ujson','azureml-contrib-services'])\n",
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"\n",
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"with open(\"myenv.yml\",\"w\") as f:\n",
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" f.write(myenv.serialize_to_string())"
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]
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},
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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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"outputs": [],
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"source": [
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"from azureml.core.image import ContainerImage\n",
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"\n",
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"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
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" runtime = \"python\",\n",
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" conda_file = \"myenv.yml\",\n",
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" gpu_enabled = True\n",
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" )\n",
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"\n",
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"image = ContainerImage.create(name = \"GpuImage\",\n",
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" # this is the model object\n",
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" models = [model],\n",
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" image_config = image_config,\n",
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" workspace = ws)\n",
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"\n",
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"image.wait_for_creation(show_output = True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Provision the AKS Cluster\n",
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"This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
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]
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},
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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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"outputs": [],
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"source": [
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"# Use the default configuration (can also provide parameters to customize)\n",
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"prov_config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\")\n",
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"\n",
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"aks_name = 'my-aks-9' \n",
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"# Create the cluster\n",
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"aks_target = ComputeTarget.create(workspace = ws, \n",
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" name = aks_name, \n",
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" provisioning_configuration = prov_config)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Create AKS Cluster in an existing virtual network (optional)\n",
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"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-enable-virtual-network#use-azure-kubernetes-service) for more details."
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]
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},
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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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"outputs": [],
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"source": [
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"'''\n",
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"from azureml.core.compute import ComputeTarget, AksCompute\n",
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"\n",
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"# Create the compute configuration and set virtual network information\n",
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"config = AksCompute.provisioning_configuration(vm_size=\"Standard_NC6\", location=\"eastus2\")\n",
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"config.vnet_resourcegroup_name = \"mygroup\"\n",
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"config.vnet_name = \"mynetwork\"\n",
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"config.subnet_name = \"default\"\n",
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"config.service_cidr = \"10.0.0.0/16\"\n",
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"config.dns_service_ip = \"10.0.0.10\"\n",
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"config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
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"\n",
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"# Create the compute target\n",
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"aks_target = ComputeTarget.create(workspace = ws,\n",
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" name = \"myaks\",\n",
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" provisioning_configuration = config)\n",
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"'''"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Enable SSL on the AKS Cluster (optional)\n",
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"See code snippet below. Check the documentation [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-secure-web-service) for more details"
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]
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},
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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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"outputs": [],
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"source": [
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"# provisioning_config = AksCompute.provisioning_configuration(ssl_cert_pem_file=\"cert.pem\", ssl_key_pem_file=\"key.pem\", ssl_cname=\"www.contoso.com\")"
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]
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},
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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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"outputs": [],
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"source": [
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"%%time\n",
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"aks_target.wait_for_completion(show_output = True)\n",
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"print(aks_target.provisioning_state)\n",
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"print(aks_target.provisioning_errors)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Optional step: Attach existing AKS cluster\n",
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"\n",
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"If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
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]
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},
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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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"outputs": [],
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"source": [
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"'''\n",
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"# Use the default configuration (can also provide parameters to customize)\n",
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"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
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"\n",
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"create_name='my-existing-aks' \n",
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"# Create the cluster\n",
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"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
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"aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
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"# Wait for the operation to complete\n",
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"aks_target.wait_for_completion(True)\n",
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"'''"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Deploy web service to AKS"
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]
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},
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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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"outputs": [],
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"source": [
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"#Set the web service configuration (using default here)\n",
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"aks_config = AksWebservice.deploy_configuration()"
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]
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},
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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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"outputs": [],
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"source": [
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"%%time\n",
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"aks_service_name ='aks-service-1'\n",
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"\n",
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"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
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" name = aks_service_name,\n",
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" image = image,\n",
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" deployment_config = aks_config,\n",
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" deployment_target = aks_target)\n",
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"aks_service.wait_for_deployment(show_output = True)\n",
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"print(aks_service.state)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Test the web service\n",
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"We test the web sevice by passing the test images content."
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]
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},
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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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"outputs": [],
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"source": [
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"%%time\n",
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"import requests\n",
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"key1, key2 = aks_service.get_keys()\n",
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"\n",
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"headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
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"test_sampe = open('test_image.jpg', 'rb').read()\n",
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"resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Clean up\n",
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"Delete the service, image, model and compute target"
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]
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},
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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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"outputs": [],
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"source": [
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"%%time\n",
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"aks_service.delete()\n",
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"image.delete()\n",
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"model.delete()\n",
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"aks_target.delete()"
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]
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}
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],
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"metadata": {
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"authors": [
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{
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"name": "aashishb"
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}
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],
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.7.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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