Update notebooks
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Roope Astala
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{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group, \n location = workspace_region,\n exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n 'Azure region: ' + ws.location, \n 'Subscription id: ' + ws.subscription_id, \n 'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}
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project-brainwave/databricks/02.Ingest_data.ipynb
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project-brainwave/databricks/02.Ingest_data.ipynb
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{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this."],"metadata":{}},{"cell_type":"markdown","source":["#Data Ingestion"],"metadata":{}},{"cell_type":"code","source":["import os\nimport urllib"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\nbasedataurl = \"https://amldockerdatasets.azureedge.net\"\ndatafile = \"AdultCensusIncome.csv\"\ndatafile_dbfs = os.path.join(\"/dbfs\", datafile)\n\nif os.path.isfile(datafile_dbfs):\n print(\"found {} at {}\".format(datafile, datafile_dbfs))\nelse:\n print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# Create a Spark dataframe out of the csv file.\ndata_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\nprint(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["#renaming columns\ncolumns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\ndata_all = data_all.toDF(*columns_new)\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["display(data_all.limit(5))"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":["#Data Preparation"],"metadata":{}},{"cell_type":"code","source":["# Choose feature columns and the label column.\nlabel = \"income\"\nxvals_all = set(data_all.columns) - {label}\n\n#dbutils.widgets.remove(\"xvars_multiselect\")\ndbutils.widgets.removeAll()\n\ndbutils.widgets.multiselect('xvars_multiselect', 'hours_per_week', xvals_all)\nxvars_multiselect = dbutils.widgets.get(\"xvars_multiselect\")\nxvars = xvars_multiselect.split(\",\")\n\nprint(\"label = {}\".format(label))\nprint(\"features = {}\".format(xvars))\n\ndata = data_all.select([*xvars, label])\n\n# Split data into train and test.\ntrain, test = data.randomSplit([0.75, 0.25], seed=123)\n\nprint(\"train ({}, {})\".format(train.count(), len(train.columns)))\nprint(\"test ({}, {})\".format(test.count(), len(test.columns)))"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":["#Data Persistence"],"metadata":{}},{"cell_type":"code","source":["# Write the train and test data sets to intermediate storage\ntrain_data_path = \"AdultCensusIncomeTrain\"\ntest_data_path = \"AdultCensusIncomeTest\"\n\ntrain_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\ntest_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n\ntrain.write.mode('overwrite').parquet(train_data_path)\ntest.write.mode('overwrite').parquet(test_data_path)\nprint(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":13},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":14}],"metadata":{"name":"02.Ingest_data","notebookId":3874566296719393},"nbformat":4,"nbformat_minor":0}
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project-brainwave/databricks/03a.Build_model.ipynb
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project-brainwave/databricks/03a.Build_model.ipynb
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project-brainwave/databricks/04.Deploy_to_ACI.ipynb
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project-brainwave/databricks/04.Deploy_to_ACI.ipynb
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{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this. This notebook uses image from ACI notebook for deploying to AKS."],"metadata":{}},{"cell_type":"code","source":["from azureml.core import Workspace\nimport azureml.core\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\n#'''\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n 'Azure region: ' + ws.location, \n 'Subscription id: ' + ws.subscription_id, \n 'Resource group: ' + ws.resource_group, sep = '\\n')\n#'''"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"code","source":["# List images by ws\n\nfrom azureml.core.image import ContainerImage\nfor i in ContainerImage.list(workspace = ws):\n print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["from azureml.core.image import Image\nmyimage = Image(workspace=ws, id=\"aciws:25\")"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["#create AKS compute\n#it may take 20-25 minutes to create a new cluster\n\nfrom azureml.core.compute import AksCompute, ComputeTarget\n\n# Use the default configuration (can also provide parameters to customize)\nprov_config = AksCompute.provisioning_configuration()\n\naks_name = 'ps-aks-clus2' \n\n# Create the cluster\naks_target = ComputeTarget.create(workspace = ws, \n name = aks_name, \n provisioning_configuration = prov_config)\n\naks_target.wait_for_completion(show_output = True)\n\nprint(aks_target.provisioning_state)\nprint(aks_target.provisioning_errors)"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["from azureml.core.webservice import Webservice\nhelp( Webservice.deploy_from_image)"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["from azureml.core.webservice import Webservice, AksWebservice\nfrom azureml.core.image import ContainerImage\n\n#Set the web service configuration (using default here)\naks_config = AksWebservice.deploy_configuration()\n\n#unique service name\nservice_name ='ps-aks-service'\n\n# Webservice creation using single command, there is a variant to use image directly as well.\naks_service = Webservice.deploy_from_image(\n workspace=ws, \n name=service_name,\n deployment_config = aks_config,\n image = myimage,\n deployment_target = aks_target\n )\n\naks_service.wait_for_deployment(show_output=True)"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["#for using the Web HTTP API \nprint(aks_service.scoring_uri)\nprint(aks_service.get_keys())"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["import json\n\n#get the some sample data\ntest_data_path = \"AdultCensusIncomeTest\"\ntest = spark.read.parquet(test_data_path).limit(5)\n\ntest_json = json.dumps(test.toJSON().collect())\n\nprint(test_json)"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":["#using data defined above predict if income is >50K (1) or <=50K (0)\naks_service.run(input_data=test_json)"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["#comment to not delete the web service\naks_service.delete()\n#image.delete()\n#model.delete()\n#aks_target.delete()"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":13}],"metadata":{"name":"04.DeploytoACI","notebookId":3874566296719318},"nbformat":4,"nbformat_minor":0}
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project-brainwave/databricks/Databricks_AMLSDK_github.dbc
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project-brainwave/databricks/Databricks_AMLSDK_github.dbc
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project-brainwave/databricks/readme.md
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project-brainwave/databricks/readme.md
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# Azure Databricks - Azure ML SDK Sample Notebooks
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**NOTE**: With the latest version of our AML SDK, there are some API changes due to which previous version of notebooks will not work.
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Kindly use this v4 notebooks (updated Sep 18)– if you had installed the AML SDK in your Databricks cluster please update to latest SDK version by installing azureml-sdk[databricks] as a library from GUI.
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**NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown). We are extending it to more runtimes asap.
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**NOTE**: Some packages like psutil upgrade libs that can cause a conflict, please install such packages by freezing lib version. Eg. "pstuil **cryptography==1.5 pyopenssl==16.0.0 ipython=2.2.0**" to avoid install error. This issue is related to Databricks and not related to AML SDK.
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**NOTE**: You should at least have contributor access to your Azure subcription to run some of the notebooks.
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The iPython Notebooks have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing .dbc instead of downloading individually.
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This set of notebooks are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. For details on SDK concepts, please refer to [Private preview notebooks](https://github.com/Azure/ViennaDocs/tree/master/PrivatePreview/notebooks)
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(Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your Databricks workspace.
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01. [Installation and Configuration](01.Installation_and_Configuration.ipynb): Install the Azure ML Python SDK and Initialize an Azure ML Workspace and save the Workspace configuration file.
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02. [Ingest data](02.Ingest_data.ipynb): Download the Adult Census Income dataset and split it into train and test sets.
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03. [Build model](03a.Build_model.ipynb): Train a binary classification model in Azure Databricks with a Spark ML Pipeline.
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04. [Build model with Run History](03b.Build_model_runHistory.ipynb): Train model and also capture run history (tracking) with Azure ML Python SDK.
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05. [Deploy to ACI](04.Deploy_to_ACI.ipynb): Deploy model to Azure Container Instance (ACI) with Azure ML Python SDK.
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06. [Deploy to AKS](04.Deploy_to_AKS_existingImage.ipynb): Deploy model to Azure Kubernetis Service (AKS) with Azure ML Python SDK from an existing Image with model, conda and score file.
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Copyright (c) Microsoft Corporation. All rights reserved.
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All notebooks in this folder are licensed under the MIT License.
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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": "coverste"
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}
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],
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"kernelspec": {
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"display_name": "Python 3.6",
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"language": "python",
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{
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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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"# Azure ML Hardware Accelerated Models Quickstart"
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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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"This tutorial will show you how to deploy an image recognition service based on the ResNet 50 classifier in just a few minutes using the Azure Machine Learning Accelerated AI service. Get more help from our [documentation](https://aka.ms/aml-real-time-ai) or [forum](https://aka.ms/aml-forum).\n",
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"\n",
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"We will use an accelerated ResNet50 featurizer running on an FPGA. This functionality is powered by Project Brainwave, which handles translating deep neural networks (DNN) into an FPGA program.\n",
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"\n",
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"## Request Quota\n",
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"**IMPORTANT:** You must [request quota](https://aka.ms/aml-real-time-ai-request) and be approved before you can successfully run this notebook. Notebook 00 will show you how to create a workspace which you can use to request quota."
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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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"## Imports"
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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 os\n",
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"import tensorflow as tf"
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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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"## Image preprocessing\n",
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"We'd like our service to accept JPEG images as input. However the input to ResNet50 is a tensor. So we need code that decodes JPEG images and does the preprocessing required by ResNet50. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as strings) and produces a tensor that is ready to be featurized by ResNet50."
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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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"# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
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"import azureml.contrib.brainwave.models.utils as utils\n",
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"in_images = tf.placeholder(tf.string)\n",
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"image_tensors = utils.preprocess_array(in_images)\n",
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"print(image_tensors.shape)"
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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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"## Featurizer\n",
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"We use ResNet50 as a featurizer. In this step we initialize the model. This downloads a TensorFlow checkpoint of the quantized ResNet50."
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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.contrib.brainwave.models import QuantizedResnet50, Resnet50\n",
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"model_path = os.path.expanduser('~/models')\n",
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"model = QuantizedResnet50(model_path, is_frozen = True)\n",
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"feature_tensor = model.import_graph_def(image_tensors)\n",
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"print(model.version)\n",
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"print(feature_tensor.name)\n",
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"print(feature_tensor.shape)"
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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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"## Classifier\n",
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"The model we downloaded includes a classifier which takes the output of the ResNet50 and identifies an image. This classifier is trained on the ImageNet dataset. We are going to use this classifier for our service. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to train a classifier for a different data set. The input to the classifier is a tensor matching the output of our ResNet50 featurizer."
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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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"classifier_output = model.get_default_classifier(feature_tensor)"
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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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"## Service Definition\n",
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"Now that we've definied the image preprocessing, featurizer, and classifier that we will execute on our service we can create a service definition. The service definition is a set of files generated from the model that allow us to deploy to the FPGA service. The service definition consists of a pipeline. The pipeline is a series of stages that are executed in order. We support TensorFlow stages, Keras stages, and BrainWave stages. The stages will be executed in order on the service, with the output of each stage input into the subsequent stage.\n",
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"\n",
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"To create a TensorFlow stage we specify a session containing the graph (in this case we are using the default graph) and the input and output tensors to this stage. We use this information to save the graph so that we can execute it on the service."
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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.contrib.brainwave.pipeline import ModelDefinition, TensorflowStage, BrainWaveStage\n",
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"\n",
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"save_path = os.path.expanduser('~/models/save')\n",
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"model_def_path = os.path.join(save_path, 'model_def.zip')\n",
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"\n",
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"model_def = ModelDefinition()\n",
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"with tf.Session() as sess:\n",
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" model_def.pipeline.append(TensorflowStage(sess, in_images, image_tensors))\n",
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" model_def.pipeline.append(BrainWaveStage(sess, model))\n",
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" model_def.pipeline.append(TensorflowStage(sess, feature_tensor, classifier_output))\n",
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" model_def.save(model_def_path)\n",
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" print(model_def_path)"
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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\n",
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"Time to create a service from the service definition. You need a Workspace in the **East US 2** location. In the previous notebooks, you've created this Workspace. The code below will load that Workspace from a configuration file."
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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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"\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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"Upload the model 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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"from azureml.core.model import Model\n",
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"model_name = \"resnet-50-rtai\"\n",
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"registered_model = Model.register(ws, model_def_path, model_name)"
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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 a service from the model that we registered. If this is a new service then we create it. If you already have a service with this name then the existing service will be updated to use this model."
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"from azureml.exceptions import WebserviceException\n",
|
||||
"from azureml.contrib.brainwave import BrainwaveWebservice, BrainwaveImage\n",
|
||||
"service_name = \"imagenet-infer\"\n",
|
||||
"service = None\n",
|
||||
"try:\n",
|
||||
" service = Webservice(ws, service_name)\n",
|
||||
"except WebserviceException:\n",
|
||||
" image_config = BrainwaveImage.image_configuration()\n",
|
||||
" deployment_config = BrainwaveWebservice.deploy_configuration()\n",
|
||||
" service = Webservice.deploy_from_model(ws, service_name, [registered_model], image_config, deployment_config)\n",
|
||||
" service.wait_for_deployment(true)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Client\n",
|
||||
"The service supports gRPC and the TensorFlow Serving \"predict\" API. We provide a client that can call the service to get predictions on aka.ms/rtai. You can also invoke the service like any other web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To understand the results we need a mapping to the human readable imagenet classes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"classes_entries = requests.get(\"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\").text.splitlines()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now send an image to the service and get the predictions. Let's see if it can identify a snow leopard.\n",
|
||||
"\n",
|
||||
"Snow leopard in a zoo. Photo by Peter Bolliger.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results = service.run('snowleopardgaze.jpg')\n",
|
||||
"# map results [class_id] => [confidence]\n",
|
||||
"results = enumerate(results)\n",
|
||||
"# sort results by confidence\n",
|
||||
"sorted_results = sorted(results, key=lambda x: x[1], reverse=True)\n",
|
||||
"# print top 5 results\n",
|
||||
"for top in sorted_results[:5]:\n",
|
||||
" print(classes_entries[top[0]], 'confidence:', top[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Cleanup\n",
|
||||
"Run the cell below to delete your service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"service.delete()\n",
|
||||
" \n",
|
||||
"registered_model.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Congratulations! You've just created a service that does predictions using an FPGA. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to customize the service using transfer learning to classify different types of images."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "coverste"
|
||||
}
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Azure ML Hardware Accelerated Models Quickstart"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This tutorial will show you how to deploy an image recognition service based on the ResNet 50 classifier in just a few minutes using the Azure Machine Learning Accelerated AI service. Get more help from our [documentation](https://aka.ms/aml-real-time-ai) or [forum](https://aka.ms/aml-forum).\n",
|
||||
"\n",
|
||||
"We will use an accelerated ResNet50 featurizer running on an FPGA. This functionality is powered by Project Brainwave, which handles translating deep neural networks (DNN) into an FPGA program.\n",
|
||||
"\n",
|
||||
"## Request Quota\n",
|
||||
"**IMPORTANT:** You must [request quota](https://aka.ms/aml-real-time-ai-request) and be approved before you can successfully run this notebook. Notebook 00 will show you how to create a workspace which you can use to request quota."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Imports"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import tensorflow as tf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Image preprocessing\n",
|
||||
"We'd like our service to accept JPEG images as input. However the input to ResNet50 is a tensor. So we need code that decodes JPEG images and does the preprocessing required by ResNet50. The Accelerated AI service can execute TensorFlow graphs as part of the service and we'll use that ability to do the image preprocessing. This code defines a TensorFlow graph that preprocesses an array of JPEG images (as strings) and produces a tensor that is ready to be featurized by ResNet50."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Input images as a two-dimensional tensor containing an arbitrary number of images represented a strings\n",
|
||||
"import azureml.contrib.brainwave.models.utils as utils\n",
|
||||
"in_images = tf.placeholder(tf.string)\n",
|
||||
"image_tensors = utils.preprocess_array(in_images)\n",
|
||||
"print(image_tensors.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Featurizer\n",
|
||||
"We use ResNet50 as a featurizer. In this step we initialize the model. This downloads a TensorFlow checkpoint of the quantized ResNet50."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.brainwave.models import QuantizedResnet50, Resnet50\n",
|
||||
"model_path = os.path.expanduser('~/models')\n",
|
||||
"model = QuantizedResnet50(model_path, is_frozen = True)\n",
|
||||
"feature_tensor = model.import_graph_def(image_tensors)\n",
|
||||
"print(model.version)\n",
|
||||
"print(feature_tensor.name)\n",
|
||||
"print(feature_tensor.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Classifier\n",
|
||||
"The model we downloaded includes a classifier which takes the output of the ResNet50 and identifies an image. This classifier is trained on the ImageNet dataset. We are going to use this classifier for our service. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to train a classifier for a different data set. The input to the classifier is a tensor matching the output of our ResNet50 featurizer."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"classifier_output = model.get_default_classifier(feature_tensor)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Service Definition\n",
|
||||
"Now that we've definied the image preprocessing, featurizer, and classifier that we will execute on our service we can create a service definition. The service definition is a set of files generated from the model that allow us to deploy to the FPGA service. The service definition consists of a pipeline. The pipeline is a series of stages that are executed in order. We support TensorFlow stages, Keras stages, and BrainWave stages. The stages will be executed in order on the service, with the output of each stage input into the subsequent stage.\n",
|
||||
"\n",
|
||||
"To create a TensorFlow stage we specify a session containing the graph (in this case we are using the default graph) and the input and output tensors to this stage. We use this information to save the graph so that we can execute it on the service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.brainwave.pipeline import ModelDefinition, TensorflowStage, BrainWaveStage\n",
|
||||
"\n",
|
||||
"save_path = os.path.expanduser('~/models/save')\n",
|
||||
"model_def_path = os.path.join(save_path, 'model_def.zip')\n",
|
||||
"\n",
|
||||
"model_def = ModelDefinition()\n",
|
||||
"with tf.Session() as sess:\n",
|
||||
" model_def.pipeline.append(TensorflowStage(sess, in_images, image_tensors))\n",
|
||||
" model_def.pipeline.append(BrainWaveStage(sess, model))\n",
|
||||
" model_def.pipeline.append(TensorflowStage(sess, feature_tensor, classifier_output))\n",
|
||||
" model_def.save(model_def_path)\n",
|
||||
" print(model_def_path)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Deploy\n",
|
||||
"Time to create a service from the service definition. You need a Workspace in the **East US 2** location. In the previous notebooks, you've created this Workspace. The code below will load that Workspace from a configuration file."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config()\n",
|
||||
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Upload the model to the workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.model import Model\n",
|
||||
"model_name = \"resnet-50-rtai\"\n",
|
||||
"registered_model = Model.register(ws, model_def_path, model_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Create a service from the model that we registered. If this is a new service then we create it. If you already have a service with this name then the existing service will be updated to use this model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"from azureml.exceptions import WebserviceException\n",
|
||||
"from azureml.contrib.brainwave import BrainwaveWebservice, BrainwaveImage\n",
|
||||
"service_name = \"imagenet-infer\"\n",
|
||||
"service = None\n",
|
||||
"try:\n",
|
||||
" service = Webservice(ws, service_name)\n",
|
||||
"except WebserviceException:\n",
|
||||
" image_config = BrainwaveImage.image_configuration()\n",
|
||||
" deployment_config = BrainwaveWebservice.deploy_configuration()\n",
|
||||
" service = Webservice.deploy_from_model(ws, service_name, [registered_model], image_config, deployment_config)\n",
|
||||
" service.wait_for_deployment(true)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Client\n",
|
||||
"The service supports gRPC and the TensorFlow Serving \"predict\" API. We provide a client that can call the service to get predictions on aka.ms/rtai. You can also invoke the service like any other web service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To understand the results we need a mapping to the human readable imagenet classes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import requests\n",
|
||||
"classes_entries = requests.get(\"https://raw.githubusercontent.com/Lasagne/Recipes/master/examples/resnet50/imagenet_classes.txt\").text.splitlines()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can now send an image to the service and get the predictions. Let's see if it can identify a snow leopard.\n",
|
||||
"\n",
|
||||
"Snow leopard in a zoo. Photo by Peter Bolliger.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"results = service.run('snowleopardgaze.jpg')\n",
|
||||
"# map results [class_id] => [confidence]\n",
|
||||
"results = enumerate(results)\n",
|
||||
"# sort results by confidence\n",
|
||||
"sorted_results = sorted(results, key=lambda x: x[1], reverse=True)\n",
|
||||
"# print top 5 results\n",
|
||||
"for top in sorted_results[:5]:\n",
|
||||
" print(classes_entries[top[0]], 'confidence:', top[1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Cleanup\n",
|
||||
"Run the cell below to delete your service."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"service.delete()\n",
|
||||
" \n",
|
||||
"registered_model.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Congratulations! You've just created a service that does predictions using an FPGA. The next [notebook](project-brainwave-trainsfer-learning.ipynb) shows how to customize the service using transfer learning to classify different types of images."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "coverste"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.2"
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -544,6 +544,11 @@
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "coverste"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
|
||||
Reference in New Issue
Block a user