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## Examples to get started with Azure Machine Learning service
Learn how to use Azure Machine Learning services for experimentation and model management.
As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
* [train-within-notebook](train-within-notebook/train-within-notebook.ipynb): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
* [train-on-local](train-on-local/train-on-local.ipynb): Learn how to submit a run and use Azure ML managed run configuration.
* [train-on-aci](train-on-aci/train-on-aci.ipynb): Submit a remote run on serverless Docker-based compute.
* [train-on-remote-vm](train-on-remote-vm/train-on-remote-vm.ipynb): Use Data Science Virtual Machine as a target for remote runs.
* [logging-api](logging-api/logging-api.ipynb): Learn about the details of logging metrics to run history.
* [register-model-create-image-deploy-service](register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb): Learn about the details of model management.
* [production-deploy-to-aks](production-deploy-to-aks/production-deploy-to-aks.ipynb) Deploy a model to production at scale on Azure Kubernetes Service.
* [enable-data-collection-for-models-in-aks](enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb) Learn about data collection APIs for deployed model.
* [enable-app-insights-in-production-service](enable-app-insights-in-production-serviceenable-app-insights-in-production-service.ipynb) Learn how to use App Insights with production web service.

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enabling App Insights for Services in Production\n",
"With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model. \n",
"\n",
"\n",
"## What does Application Insights monitor?\n",
"It monitors request rates, response times, failure rates, etc. For more information visit [App Insights docs.](https://docs.microsoft.com/en-us/azure/application-insights/app-insights-overview)\n",
"\n",
"\n",
"## What is different compared to standard production deployment process?\n",
"If you want to enable generic App Insights for a service run:\n",
"```python\n",
"aks_service= Webservice(ws, \"aks-w-dc2\")\n",
"aks_service.update(enable_app_insights=True)```\n",
"Where \"aks-w-dc2\" is your service name. You can also do this from the Azure Portal under your Workspace--> deployments--> Select deployment--> Edit--> Advanced Settings--> Select \"Enable AppInsights diagnostics\"\n",
"\n",
"If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
"1. Update scoring file.\n",
"2. Update aks configuration.\n",
"3. Build new image and deploy it. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import your dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace, Run\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model\n",
"\n",
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Set up your configuration and create a workspace\n",
"Follow Notebook 00 instructions to do this.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Register Model\n",
"Register an existing trained model, add descirption and tags."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
" model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. *Update your scoring file with custom print statements*\n",
"Here is an example:\n",
"### a. In your init function add:\n",
"```python\n",
"print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))```\n",
"\n",
"### b. In your run function add:\n",
"```python\n",
"print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
"print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy \n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"from azureml.monitoring import ModelDataCollector\n",
"import time\n",
"\n",
"def init():\n",
" global model\n",
" #Print statement for appinsights custom traces:\n",
" print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
" \n",
" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
" # this call should return the path to the model.pkl file on the local disk.\n",
" model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
" \n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
" \n",
" global inputs_dc, prediction_dc\n",
" \n",
" # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
" inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
" \n",
" # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
" prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
" \n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" global inputs_dc, prediction_dc\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" \n",
" #Print statement for appinsights custom traces:\n",
" print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
" \n",
" #this call is saving our input data into our blob\n",
" inputs_dc.collect(data) \n",
" #this call is saving our prediction data into our blob\n",
" prediction_dc.collect(result)\n",
" \n",
" #Print statement for appinsights custom traces:\n",
" print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
" # you can return any data type as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" error = str(e)\n",
" print (error + time.strftime(\"%H:%M:%S\"))\n",
" return error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. *Create myenv.yml file*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Create your new Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Deploy to AKS service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create AKS compute if you haven't done so (Notebook 11)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the default configuration (can also provide parameters to customize)\n",
"prov_config = AksCompute.provisioning_configuration()\n",
"\n",
"aks_name = 'my-aks-test2' \n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you already have a cluster you can attach the service to it:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python \n",
"%%time\n",
"resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
"create_name= 'myaks4'\n",
"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
"aks_target = ComputeTarget.attach(workspace = ws, \n",
" name = create_name, \n",
" attach_configuration=attach_config)\n",
"## Wait for the operation to complete\n",
"aks_target.wait_for_provisioning(True)```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. *Activate App Insights through updating AKS Webservice configuration*\n",
"In order to enable App Insights in your service you will need to update your AKS configuration file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration\n",
"aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Deploy your service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-w-dc3'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target\n",
" )\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Test your service "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,28,13,45,54,6,57,8,8,10], \n",
" [101,9,8,37,6,45,4,3,2,41]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding='utf8')\n",
"\n",
"prediction = aks_service.run(input_data=test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. See your service telemetry in App Insights\n",
"1. Go to the [Azure Portal](https://portal.azure.com/)\n",
"2. All resources--> Select the subscription/resource group where you created your Workspace--> Select the App Insights type\n",
"3. Click on the AppInsights resource. You'll see a highlevel dashboard with information on Requests, Server response time and availability.\n",
"4. Click on the top banner \"Analytics\"\n",
"5. In the \"Schema\" section select \"traces\" and run your query.\n",
"6. Voila! All your custom traces should be there."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Disable App Insights"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service.update(enable_app_insights=False)"
]
}
],
"metadata": {
"authors": [
{
"name": "marthalc"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Enabling Data Collection for Models in Production\n",
"With this notebook, you can learn how to collect input model data from your Azure Machine Learning service in an Azure Blob storage. Once enabled, this data collected gives you the opportunity:\n",
"\n",
"* Monitor data drifts as production data enters your model\n",
"* Make better decisions on when to retrain or optimize your model\n",
"* Retrain your model with the data collected\n",
"\n",
"## What data is collected?\n",
"* Model input data (voice, images, and video are not supported) from services deployed in Azure Kubernetes Cluster (AKS)\n",
"* Model predictions using production input data.\n",
"\n",
"**Note:** pre-aggregation or pre-calculations on this data are done by user and not included in this version of the product.\n",
"\n",
"## What is different compared to standard production deployment process?\n",
"1. Update scoring file.\n",
"2. Update yml file with new dependency.\n",
"3. Update aks configuration.\n",
"4. Build new image and deploy it. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Import your dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace, Run\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model\n",
"\n",
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Set up your configuration and create a workspace\n",
"Follow Notebook 00 instructions to do this.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Register Model\n",
"Register an existing trained model, add descirption and tags."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
" model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. *Update your scoring file with Data Collection*\n",
"The file below, compared to the file used in notebook 11, has the following changes:\n",
"### a. Import the module\n",
"```python \n",
"from azureml.monitoring import ModelDataCollector```\n",
"### b. In your init function add:\n",
"```python \n",
"global inputs_dc, prediction_d\n",
"inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\", \"feat4\", \"feat5\", \"Feat6\"])\n",
"prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])```\n",
" \n",
"* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide \"raw\" data versus \"processed\".\n",
"* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn't require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n",
"* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n",
"\n",
"### c. In your run function add:\n",
"```python\n",
"inputs_dc.collect(data)\n",
"prediction_dc.collect(result)```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy \n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"from azureml.monitoring import ModelDataCollector\n",
"import time\n",
"\n",
"def init():\n",
" global model\n",
" print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
" # this call should return the path to the model.pkl file on the local disk.\n",
" model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
" global inputs_dc, prediction_dc\n",
" # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
" inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
" # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
" prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
" \n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" global inputs_dc, prediction_dc\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
" inputs_dc.collect(data) #this call is saving our input data into our blob\n",
" prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
" print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
" # you can return any data type as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" error = str(e)\n",
" print (error + time.strftime(\"%H:%M:%S\"))\n",
" return error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. *Update your myenv.yml file with the required module*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"myenv.add_pip_package(\"azureml-monitoring\")\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Create your new Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 7. Deploy to AKS service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create AKS compute if you haven't done so (Notebook 11)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the default configuration (can also provide parameters to customize)\n",
"prov_config = AksCompute.provisioning_configuration()\n",
"\n",
"aks_name = 'my-aks-test1' \n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you already have a cluster you can attach the service to it:"
]
},
{
"cell_type": "markdown",
"metadata": {
"scrolled": true
},
"source": [
"```python \n",
" %%time\n",
" resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
" create_name= 'myaks4'\n",
" attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
" aks_target = ComputeTarget.attach(workspace = ws, \n",
" name = create_name, \n",
" attach_configuration=attach_config)\n",
" ## Wait for the operation to complete\n",
" aks_target.wait_for_provisioning(True)```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. *Activate Data Collection and App Insights through updating AKS Webservice configuration*\n",
"In order to enable Data Collection and App Insights in your service you will need to update your AKS configuration file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration\n",
"aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Deploy your service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-w-dc2'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target\n",
" )\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 8. Test your service and send some data\n",
"**Note**: It will take around 15 mins for your data to appear in your blob.\n",
"The data will appear in your Azure Blob following this format:\n",
"\n",
"/modeldata/subscriptionid/resourcegroupname/workspacename/webservicename/modelname/modelversion/identifier/year/month/day/data.csv "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,54,6,7,8,88,10], \n",
" [10,9,8,37,36,45,4,33,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aks_service.run(input_data = test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 9. Validate you data and analyze it\n",
"You can look into your data following this path format in your Azure Blob (it takes up to 15 minutes for the data to appear):\n",
"\n",
"/modeldata/**subscriptionid>**/**resourcegroupname>**/**workspacename>**/**webservicename>**/**modelname>**/**modelversion>>**/**identifier>**/*year/month/day*/data.csv \n",
"\n",
"For doing further analysis you have multiple options:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### a. Create DataBricks cluter and connect it to your blob\n",
"https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal or in your databricks workspace you can look for the template \"Azure Blob Storage Import Example Notebook\".\n",
"\n",
"\n",
"Here is an example for setting up the file location to extract the relevant data:\n",
"\n",
"<code> file_location = \"wasbs://mycontainer@storageaccountname.blob.core.windows.net/unknown/unknown/unknown-bigdataset-unknown/my_iterate_parking_inputs/2018/&deg;/&deg;/data.csv\" \n",
"file_type = \"csv\"</code>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### b. Connect Blob to Power Bi (Small Data only)\n",
"1. Download and Open PowerBi Desktop\n",
"2. Select “Get Data” and click on “Azure Blob Storage” >> Connect\n",
"3. Add your storage account and enter your storage key.\n",
"4. Select the container where your Data Collection is stored and click on Edit. \n",
"5. In the query editor, click under “Name” column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
"6. Click on the double arrow aside the “Content” column to combine the files. \n",
"7. Click OK and the data will preload.\n",
"8. You can now click Close and Apply and start building your custom reports on your Model Input data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Disable Data Collection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service.update(collect_model_data=False)"
]
}
],
"metadata": {
"authors": [
{
"name": "marthalc"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"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": [
"# 06. Logging APIs\n",
"This notebook showcase various ways to use the Azure Machine Learning service run logging APIs, and view the results in the Azure portal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. Also make sure you have tqdm and matplotlib installed in the current kernel.\n",
"\n",
"```\n",
"(myenv) $ conda install -y tqdm matplotlib\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validate Azure ML SDK installation and get version number for debugging purposes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"install"
]
},
"outputs": [],
"source": [
"from azureml.core import Experiment, Run, Workspace\n",
"import azureml.core\n",
"import numpy as np\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set experiment\n",
"Create a new experiment (or get the one with such name)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"exp = Experiment(workspace=ws, name='logging-api-test')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Log metrics\n",
"We will start a run, and use the various logging APIs to record different types of metrics during the run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from tqdm import tqdm\n",
"\n",
"# start logging for the run\n",
"run = exp.start_logging()\n",
"\n",
"# log a string value\n",
"run.log(name='Name', value='Logging API run')\n",
"\n",
"# log a numerical value\n",
"run.log(name='Magic Number', value=42)\n",
"\n",
"# Log a list of values. Note this will generate a single-variable line chart.\n",
"run.log_list(name='Fibonacci', value=[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89])\n",
"\n",
"# create a dictionary to hold a table of values\n",
"sines = {}\n",
"sines['angle'] = []\n",
"sines['sine'] = []\n",
"\n",
"for i in tqdm(range(-10, 10)):\n",
" # log a metric value repeatedly, this will generate a single-variable line chart.\n",
" run.log(name='Sigmoid', value=1 / (1 + np.exp(-i)))\n",
" angle = i / 2.0\n",
" \n",
" # log a 2 (or more) values as a metric repeatedly. This will generate a 2-variable line chart if you have 2 numerical columns.\n",
" run.log_row(name='Cosine Wave', angle=angle, cos=np.cos(angle))\n",
" \n",
" sines['angle'].append(angle)\n",
" sines['sine'].append(np.sin(angle))\n",
"\n",
"# log a dictionary as a table, this will generate a 2-variable chart if you have 2 numerical columns\n",
"run.log_table(name='Sine Wave', value=sines)\n",
"\n",
"run.complete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even after the run is marked completed, you can still log things."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Log an image\n",
"This is how to log a _matplotlib_ pyplot object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"angle = np.linspace(-3, 3, 50)\n",
"plt.plot(angle, np.tanh(angle), label='tanh')\n",
"plt.legend(fontsize=12)\n",
"plt.title('Hyperbolic Tangent', fontsize=16)\n",
"plt.grid(True)\n",
"\n",
"run.log_image(name='Hyperbolic Tangent', plot=plt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload a file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also upload an abitrary file. First, let's create a dummy file locally."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile myfile.txt\n",
"\n",
"This is a dummy file."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's upload this file into the run record as a run artifact, and display the properties after the upload."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"props = run.upload_file(name='myfile_in_the_cloud.txt', path_or_stream='./myfile.txt')\n",
"props.serialize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Examine the run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's take a look at the run detail page in Azure portal. Make sure you checkout the various charts and plots generated/uploaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can get all the metrics in that run back."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also see the files uploaded for this run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_file_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also download all the files locally."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.makedirs('files', exist_ok=True)\n",
"\n",
"for f in run.get_file_names():\n",
" dest = os.path.join('files', f.split('/')[-1])\n",
" print('Downloading file {} to {}...'.format(f, dest))\n",
" run.download_file(f, dest) "
]
}
],
"metadata": {
"authors": [
{
"name": "haining"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

343
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{
"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": [
"# Deploying a web service to Azure Kubernetes Service (AKS)\n",
"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",
"We then test and delete the service, image and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"print(azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get workspace\n",
"Load existing workspace from the config file info."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.workspace 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": [
"# Register the model\n",
"Register an existing trained model, add descirption and tags."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Register the model\n",
"from azureml.core.model import Model\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
" model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)\n",
"\n",
"print(model.name, model.description, model.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create an image\n",
"Create an image using the registered model the script that will load and run the model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
" # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
" model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" # you can return any data type as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" error = str(e)\n",
" return error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Provision the AKS Cluster\n",
"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."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use the default configuration (can also provide parameters to customize)\n",
"prov_config = AksCompute.provisioning_configuration()\n",
"\n",
"aks_name = 'my-aks-9' \n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
" provisioning_configuration = prov_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optional step: Attach existing AKS cluster\n",
"\n",
"If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Use the default configuration (can also provide parameters to customize)\n",
"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
"\n",
"create_name='my-existing-aks' \n",
"# Create the cluster\n",
"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
"aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
"# Wait for the operation to complete\n",
"aks_target.wait_for_completion(True)\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy web service to AKS"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service_name ='aks-service-1'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test the web service\n",
"We test the web sevice by passing data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,5,6,7,8,9,10], \n",
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aks_service.run(input_data = test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Clean up\n",
"Delete the service, image and model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_service.delete()\n",
"image.delete()\n",
"model.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "raymondl"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"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": [
"## 10. Register Model, Create Image and Deploy Service\n",
"\n",
"This example shows how to deploy a web service in step-by-step fashion:\n",
"\n",
" 1. Register model\n",
" 2. Query versions of models and select one to deploy\n",
" 3. Create Docker image\n",
" 4. Query versions of images\n",
" 5. Deploy the image as web service\n",
" \n",
"**IMPORTANT**:\n",
" * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
" \n",
"The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"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": [
"### Register Model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
"\n",
"Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from file"
]
},
"outputs": [],
"source": [
"from azureml.core.model import Model\n",
"import sklearn\n",
"\n",
"library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
"\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
" model_name = \"sklearn_regression_model.pkl\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from file"
]
},
"outputs": [],
"source": [
"regression_models = Model.list(workspace=ws, tags=['area'])\n",
"for m in regression_models:\n",
" print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can pick a specific model to deploy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.name, model.description, model.version, sep = '\\t')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Docker Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile score.py\n",
"import pickle\n",
"import json\n",
"import numpy\n",
"from sklearn.externals import joblib\n",
"from sklearn.linear_model import Ridge\n",
"from azureml.core.model import Model\n",
"\n",
"def init():\n",
" global model\n",
" # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
" # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
" model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
" # deserialize the model file back into a sklearn model\n",
" model = joblib.load(model_path)\n",
"\n",
"# note you can pass in multiple rows for scoring\n",
"def run(raw_data):\n",
" try:\n",
" data = json.loads(raw_data)['data']\n",
" data = numpy.array(data)\n",
" result = model.predict(data)\n",
" # you can return any datatype as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" error = str(e)\n",
" return error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that following command can take few minutes. \n",
"\n",
"You can add tags and descriptions to images. Also, an image can contain multiple models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script=\"score.py\",\n",
" conda_file=\"myenv.yml\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Image with ridge regression model\")\n",
"\n",
"image = Image.create(name = \"myimage1\",\n",
" # this is the model object \n",
" models = [model],\n",
" image_config = image_config, \n",
" workspace = ws)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"outputs": [],
"source": [
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List images by tag and find out the detailed build log for debugging."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create image"
]
},
"outputs": [],
"source": [
"for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
" print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy image as web service on Azure Container Instance\n",
"\n",
"Note that the service creation can take few minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
" description = 'Predict diabetes using regression model')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'my-aci-service-2'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test web service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call the web service with some dummy input data to get a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,5,6,7,8,9,10], \n",
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"prediction = aci_service.run(input_data=test_sample)\n",
"print(prediction)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Delete ACI to clean up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"aci_service.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "raymondl"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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{
"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": [
"# 03. Train on Azure Container Instance\n",
"\n",
"* Create Workspace\n",
"* Create `train.py` in the project folder.\n",
"* Configure an ACI (Azure Container Instance) run\n",
"* Execute in ACI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"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": [
"## Create An Experiment\n",
"\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'train-on-aci'\n",
"experiment = Experiment(workspace = ws, name = experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Remote execution on ACI\n",
"\n",
"The training script `train.py` is already created for you. Let's have a look."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('./train.py', 'r') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure for using ACI\n",
"Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"configure run"
]
},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new runconfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"# signal that you want to use ACI to execute script.\n",
"run_config.target = \"containerinstance\"\n",
"\n",
"# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
"run_config.container_instance.region = 'eastus2'\n",
"\n",
"# set the ACI CPU and Memory \n",
"run_config.container_instance.cpu_cores = 1\n",
"run_config.container_instance.memory_gb = 2\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# set Docker base image to the default CPU-based image\n",
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
"run_config.auto_prepare_environment = True\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit the Experiment\n",
"Finally, run the training job on the ACI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"remote run",
"aci"
]
},
"outputs": [],
"source": [
"%%time \n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"script_run_config = ScriptRunConfig(source_directory='./',\n",
" script='train.py',\n",
" run_config=run_config)\n",
"\n",
"run = experiment.submit(script_run_config)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"# Show run details\n",
"run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"remote run",
"aci"
]
},
"outputs": [],
"source": [
"%%time\n",
"# Shows output of the run on stdout.\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"get metrics"
]
},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" min(metrics['mse']), \n",
" metrics['alpha'][np.argmin(metrics['mse'])]\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# show all the files stored within the run record\n",
"run.get_file_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can take a model produced here, register it and then deploy as a web service."
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core.run import Run
from sklearn.externals import joblib
import os
import numpy as np
os.makedirs('./outputs', exist_ok=True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_context()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2,
random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = np.arange(0.0, 1.0, 0.05)
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('mse', mse)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
# save model in the outputs folder so it automatically get uploaded
with open(model_file_name, "wb") as file:
joblib.dump(value=reg, filename=os.path.join('./outputs/',
model_file_name))
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

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getting-started/train-on-local/mylib.py Normal file
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# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import numpy as np
def get_alphas():
# list of numbers from 0.0 to 1.0 with a 0.05 interval
return np.arange(0.0, 1.0, 0.05)

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getting-started/train-on-local/train-on-local.ipynb Normal file
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@@ -0,0 +1,477 @@
{
"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": [
"# 02. Train locally\n",
"* Create or load workspace.\n",
"* Create scripts locally.\n",
"* Create `train.py` in a folder, along with a `my.lib` file.\n",
"* Configure & execute a local run in a user-managed Python environment.\n",
"* Configure & execute a local run in a system-managed Python environment.\n",
"* Configure & execute a local run in a Docker environment.\n",
"* Query run metrics to find the best model\n",
"* Register model for operationalization."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.workspace 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": [
"## Create An Experiment\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'train-on-local'\n",
"exp = Experiment(workspace=ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View `train.py`\n",
"\n",
"`train.py` is already created for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('./train.py', 'r') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note `train.py` also references a `mylib.py` file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('./mylib.py', 'r') as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run\n",
"### User-managed environment\n",
"Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"\n",
"# Editing a run configuration property on-fly.\n",
"run_config_user_managed = RunConfiguration()\n",
"\n",
"run_config_user_managed.environment.python.user_managed_dependencies = True\n",
"\n",
"# You can choose a specific Python environment by pointing to a Python path \n",
"#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Submit script to run in the user-managed environment\n",
"Note whole script folder is submitted for execution, including the `mylib.py` file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
"run = exp.submit(src)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Block to wait till run finishes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### System-managed environment\n",
"You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"run_config_system_managed = RunConfiguration()\n",
"\n",
"run_config_system_managed.environment.python.user_managed_dependencies = False\n",
"run_config_system_managed.auto_prepare_environment = True\n",
"\n",
"# Specify conda dependencies with scikit-learn\n",
"cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
"run_config_system_managed.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Submit script to run in the system-managed environment\n",
"A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
"run = exp.submit(src)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Block and wait till run finishes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Docker-based execution\n",
"**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
"\n",
"You can also ask the system to pull down a Docker image and execute your scripts in it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run_config_docker = RunConfiguration()\n",
"run_config_docker.environment.python.user_managed_dependencies = False\n",
"run_config_docker.auto_prepare_environment = True\n",
"run_config_docker.environment.docker.enabled = True\n",
"run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"# Specify conda dependencies with scikit-learn\n",
"cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
"run_config_docker.environment.python.conda_dependencies = cd\n",
"\n",
"src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Submit script to run in the system-managed environment\n",
"A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import subprocess\n",
"\n",
"# Check if Docker is installed and Linux containers are enables\n",
"if subprocess.run(\"docker -v\", shell=True) == 0:\n",
" out = subprocess.check_output(\"docker system info\", shell=True, encoding=\"ascii\").split(\"\\n\")\n",
" if not \"OSType: linux\" in out:\n",
" print(\"Switch Docker engine to use Linux containers.\")\n",
" else:\n",
" run = exp.submit(src)\n",
"else:\n",
" print(\"Docker engine not installed.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Get run history details\n",
"run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Query run metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history",
"get metrics"
]
},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's find the model that has the lowest MSE value logged."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
"\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" min(metrics['mse']), \n",
" best_alpha\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also list all the files that are associated with this run record"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.get_file_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# supply a model name, and the full path to the serialized model file.\n",
"model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(model.name, model.version, model.url)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now you can deploy this model following the example in the 01 notebook."
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

45
getting-started/train-on-local/train.py Normal file
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@@ -0,0 +1,45 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
from sklearn.datasets import load_diabetes
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core.run import Run
from sklearn.externals import joblib
import os
import numpy as np
import mylib
os.makedirs('./outputs', exist_ok=True)
X, y = load_diabetes(return_X_y=True)
run = Run.get_context()
X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.2,
random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = mylib.get_alphas()
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('mse', mse)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
# save model in the outputs folder so it automatically get uploaded
with open(model_file_name, "wb") as file:
joblib.dump(value=reg, filename=os.path.join('./outputs/',
model_file_name))
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

630
getting-started/train-on-remote-vm/train-on-remote-vm.ipynb Normal file
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@@ -0,0 +1,630 @@
{
"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": [
"# 04. Train in a remote Linux VM\n",
"* Create Workspace\n",
"* Create `train.py` file\n",
"* Create (or attach) DSVM as compute resource.\n",
"* Upoad data files into default datastore\n",
"* Configure & execute a run in a few different ways\n",
" - Use system-built conda\n",
" - Use existing Python environment\n",
" - Use Docker \n",
"* Find the best model in the run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"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": [
"## Create Experiment\n",
"\n",
"**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'train-on-remote-vm'\n",
"\n",
"from azureml.core import Experiment\n",
"exp = Experiment(workspace=ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also create a local folder to hold the training script."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"script_folder = './vm-run'\n",
"os.makedirs(script_folder, exist_ok=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload data files into datastore\n",
"Every workspace comes with a default datastore (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get the default datastore\n",
"ds = ws.get_default_datastore()\n",
"print(ds.name, ds.datastore_type, ds.account_name, ds.container_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load diabetes data from `scikit-learn` and save it as 2 local files."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_diabetes\n",
"import numpy as np\n",
"\n",
"training_data = load_diabetes()\n",
"np.save(file='./features.npy', arr=training_data['data'])\n",
"np.save(file='./labels.npy', arr=training_data['target'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's upload the 2 files into the default datastore under a path named `diabetes`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ds.upload_files(['./features.npy', './labels.npy'], target_path='diabetes', overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View `train.py`\n",
"\n",
"For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train.py` in a cell to show the file. Please pay special attention on how we are loading the features and labels from files in the `data_folder` path, which is passed in as an argument of the training script (shown later)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# copy train.py into the script folder\n",
"import shutil\n",
"shutil.copy('./train.py', os.path.join(script_folder, 'train.py'))\n",
"\n",
"with open(os.path.join(script_folder, './train.py'), 'r') as training_script:\n",
" print(training_script.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Linux DSVM as a compute target\n",
"\n",
"**Note**: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
" \n",
"**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can specify the port number in the provisioning configuration object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import DsvmCompute\n",
"from azureml.core.compute_target import ComputeTargetException\n",
"\n",
"compute_target_name = 'cpudsvm'\n",
"\n",
"try:\n",
" dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
" print('found existing:', dsvm_compute.name)\n",
"except ComputeTargetException:\n",
" print('creating new.')\n",
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
" dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
" dsvm_compute.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attach an existing Linux DSVM\n",
"You can also attach an existing Linux VM as a compute target. To create one, you can use Azure CLI command:\n",
"\n",
"```\n",
"az vm create -n cpudsvm -l eastus2 -g <my-resource-group> --size Standard_D2_v2 --image microsoft-dsvm:linux-data-science-vm-ubuntu:linuxdsvmubuntu:latest --generate-ssh-keys\n",
"```\n",
"\n",
"The ```--generate-ssh-keys``` automatically places the ssh keys to standard location, typically to ~/.ssh folder. The default port is 22."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"from azureml.core.compute import ComputeTarget, RemoteCompute \n",
"attach_config = RemoteCompute.attach_configuration(username='<my_username>',\n",
" address='<ip_adress_or_fqdn>',\n",
" ssh_port=22,\n",
" private_key_file='./.ssh/id_rsa')\n",
"attached_dsvm_compute = ComputeTarget.attach(workspace=ws,\n",
" name='attached_vm',\n",
" attach_configuration=attach_config)\n",
"attached_dsvm_compute.wait_for_completion(show_output=True)\n",
"'''\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure & Run\n",
"First let's create a `DataReferenceConfiguration` object to inform the system what data folder to download to the copmute target."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import DataReferenceConfiguration\n",
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
" path_on_datastore='diabetes', \n",
" mode='download', # download files from datastore to compute target\n",
" overwrite=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can try a few different ways to run the training script in the VM."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conda run\n",
"You can ask the system to build a conda environment based on your dependency specification, and submit your script to run there. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"conda_run_config.target = dsvm_compute.name\n",
"\n",
"# set the data reference of the run configuration\n",
"conda_run_config.data_references = {ds.name: dr}\n",
"\n",
"# specify CondaDependencies obj\n",
"conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Run\n",
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=script_folder, \n",
" script='train.py', \n",
" run_config=conda_run_config, \n",
" # pass the datastore reference as a parameter to the training script\n",
" arguments=['--data-folder', str(ds.as_download())] \n",
" ) \n",
"run = exp.submit(config=src)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the run object. You can navigate to the Azure portal to see detailed information about the run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Native VM run\n",
"You can also configure to use an exiting Python environment in the VM to execute the script without asking the system to create a conda environment for you."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# create a new RunConfig object\n",
"vm_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"vm_run_config.target = dsvm_compute.name\n",
"\n",
"# set the data reference of the run coonfiguration\n",
"conda_run_config.data_references = {ds.name: dr}\n",
"\n",
"# Let system know that you will configure the Python environment yourself.\n",
"vm_run_config.environment.python.user_managed_dependencies = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The below run will likely fail because `train.py` needs dependency `azureml`, `scikit-learn` and others, which are not found in that Python environment. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"src = ScriptRunConfig(source_directory=script_folder, \n",
" script='train.py', \n",
" run_config=vm_run_config,\n",
" # pass the datastore reference as a parameter to the training script\n",
" arguments=['--data-folder', str(ds.as_download())])\n",
"run = exp.submit(config=src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to create another script `train2.py` that doesn't have azureml dependencies, and submit it instead."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile $script_folder/train2.py\n",
"\n",
"print('####################################')\n",
"print('Hello World (without Azure ML SDK)!')\n",
"print('####################################')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's try again. And this time it should work fine."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"src = ScriptRunConfig(source_directory=script_folder, \n",
" script='train2.py', \n",
" run_config=vm_run_config)\n",
"run = exp.submit(config=src)\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note even in this case you get a run record with some basic statistics."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure a Docker run with new conda environment on the VM\n",
"You can execute in a Docker container in the VM. If you choose this option, the system will pull down a base Docker image, build a new conda environment in it if you ask for (you can also skip this if you are using a customer Docker image when a preconfigured Python environment), start a container, and run your script in there. This image is also uploaded into your ACR (Azure Container Registry) assoicated with your workspace, an reused if your dependencies don't change in the subsequent runs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"\n",
"# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
"docker_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to the Linux DSVM\n",
"docker_run_config.target = dsvm_compute.name\n",
"\n",
"# Use Docker in the remote VM\n",
"docker_run_config.environment.docker.enabled = True\n",
"\n",
"# Use CPU base image from DockerHub\n",
"docker_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"print('Base Docker image is:', docker_run_config.environment.docker.base_image)\n",
"\n",
"# set the data reference of the run coonfiguration\n",
"docker_run_config.data_references = {ds.name: dr}\n",
"\n",
"# specify CondaDependencies obj\n",
"docker_run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit the Experiment\n",
"Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"src = ScriptRunConfig(source_directory=script_folder, \n",
" script='train.py', \n",
" run_config=docker_run_config,\n",
" # pass the datastore reference as a parameter to the training script\n",
" arguments=['--data-folder', str(ds.as_download())])\n",
"run = exp.submit(config=src)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View run history details"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the best model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have tried various execution modes, we can find the best model from the last run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get all metris logged in the run\n",
"run.get_metrics()\n",
"metrics = run.get_metrics()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# find the index where MSE is the smallest\n",
"indices = list(range(0, len(metrics['mse'])))\n",
"min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n",
"\n",
"print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
" metrics['mse'][min_mse_index], \n",
" metrics['alpha'][min_mse_index]\n",
"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clean up compute resource\n",
"\n",
"Use ```detach()``` to detach an existing DSVM from Workspace without deleting it. Use ```delete()``` if you created a new ```DsvmCompute``` and want to delete it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# dsvm_compute.detach()\n",
"# dsvm_compute.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "haining"
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import os
import argparse
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from azureml.core.run import Run
from sklearn.externals import joblib
import numpy as np
os.makedirs('./outputs', exist_ok=True)
parser = argparse.ArgumentParser()
parser.add_argument('--data-folder', type=str,
dest='data_folder', help='data folder')
args = parser.parse_args()
print('Data folder is at:', args.data_folder)
print('List all files: ', os.listdir(args.data_folder))
X = np.load(os.path.join(args.data_folder, 'features.npy'))
y = np.load(os.path.join(args.data_folder, 'labels.npy'))
run = Run.get_context()
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=0)
data = {"train": {"X": X_train, "y": y_train},
"test": {"X": X_test, "y": y_test}}
# list of numbers from 0.0 to 1.0 with a 0.05 interval
alphas = np.arange(0.0, 1.0, 0.05)
for alpha in alphas:
# Use Ridge algorithm to create a regression model
reg = Ridge(alpha=alpha)
reg.fit(data["train"]["X"], data["train"]["y"])
preds = reg.predict(data["test"]["X"])
mse = mean_squared_error(preds, data["test"]["y"])
run.log('alpha', alpha)
run.log('mse', mse)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
with open(model_file_name, "wb") as file:
joblib.dump(value=reg, filename='outputs/' + model_file_name)
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))

29
getting-started/train-within-notebook/score.py Normal file
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import pickle
import json
import numpy as np
from sklearn.externals import joblib
from sklearn.linear_model import Ridge
from azureml.core.model import Model
def init():
global model
# note here "best_model" is the name of the model registered under the workspace
# this call should return the path to the model.pkl file on the local disk.
model_path = Model.get_model_path(model_name='best_model')
# deserialize the model file back into a sklearn model
model = joblib.load(model_path)
# note you can pass in multiple rows for scoring
def run(raw_data):
try:
data = json.loads(raw_data)['data']
data = np.array(data)
result = model.predict(data)
# you can return any data type as long as it is JSON-serializable
return result.tolist()
except Exception as e:
result = str(e)
return result

808
getting-started/train-within-notebook/train-within-notebook.ipynb Normal file
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@@ -0,0 +1,808 @@
{
"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": [
"# 01. Train in the Notebook & Deploy Model to ACI\n",
"\n",
"* Load workspace\n",
"* Train a simple regression model directly in the Notebook python kernel\n",
"* Record run history\n",
"* Find the best model in run history and download it.\n",
"* Deploy the model as an Azure Container Instance (ACI)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"1. Make sure you go through the [00. Installation and Configuration](../../00.configuration.ipynb) Notebook first if you haven't. \n",
"\n",
"2. Install following pre-requisite libraries to your conda environment and restart notebook.\n",
"```shell\n",
"(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
"```\n",
"\n",
"3. Check that ACI is registered for your Azure Subscription. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!az provider show -n Microsoft.ContainerInstance -o table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If ACI is not registered, run following command to register it. Note that you have to be a subscription owner, or this command will fail."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!az provider register -n Microsoft.ContainerInstance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Validate Azure ML SDK installation and get version number for debugging purposes"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"install"
]
},
"outputs": [],
"source": [
"from azureml.core import Experiment, Run, Workspace\n",
"import azureml.core\n",
"\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set experiment name\n",
"Choose a name for experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'train-in-notebook'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a training run in local Notebook"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load diabetes dataset, a well-known small dataset that comes with scikit-learn\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.linear_model import Ridge\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.externals import joblib\n",
"\n",
"X, y = load_diabetes(return_X_y = True)\n",
"columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"data = {\n",
" \"train\":{\"X\": X_train, \"y\": y_train}, \n",
" \"test\":{\"X\": X_test, \"y\": y_test}\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train a simple Ridge model\n",
"Train a very simple Ridge regression model in scikit-learn, and save it as a pickle file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"reg = Ridge(alpha = 0.03)\n",
"reg.fit(X=data['train']['X'], y=data['train']['y'])\n",
"preds = reg.predict(data['test']['X'])\n",
"print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
"joblib.dump(value=reg, filename='model.pkl');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Add experiment tracking\n",
"Now, let's add Azure ML experiment logging, and upload persisted model into run record as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"local run",
"outputs upload"
]
},
"outputs": [],
"source": [
"experiment = Experiment(workspace=ws, name=experiment_name)\n",
"run = experiment.start_logging()\n",
"\n",
"run.tag(\"Description\",\"My first run!\")\n",
"run.log('alpha', 0.03)\n",
"reg = Ridge(alpha=0.03)\n",
"reg.fit(data['train']['X'], data['train']['y'])\n",
"preds = reg.predict(data['test']['X'])\n",
"run.log('mse', mean_squared_error(data['test']['y'], preds))\n",
"joblib.dump(value=reg, filename='model.pkl')\n",
"run.upload_file(name='outputs/model.pkl', path_or_stream='./model.pkl')\n",
"\n",
"run.complete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can browse to the recorded run. Please make sure you use Chrome to navigate the run history page."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simple parameter sweep\n",
"Sweep over alpha values of a sklearn ridge model, and capture metrics and trained model in the Azure ML experiment."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
"from tqdm import tqdm\n",
"\n",
"model_name = \"model.pkl\"\n",
"\n",
"# list of numbers from 0 to 1.0 with a 0.05 interval\n",
"alphas = np.arange(0.0, 1.0, 0.05)\n",
"\n",
"# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
"for alpha in tqdm(alphas):\n",
" # create a bunch of runs, each train a model with a different alpha value\n",
" with experiment.start_logging() as run:\n",
" # Use Ridge algorithm to build a regression model\n",
" reg = Ridge(alpha=alpha)\n",
" reg.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
" preds = reg.predict(X=data[\"test\"][\"X\"])\n",
" mse = mean_squared_error(y_true=data[\"test\"][\"y\"], y_pred=preds)\n",
"\n",
" # log alpha, mean_squared_error and feature names in run history\n",
" run.log(name=\"alpha\", value=alpha)\n",
" run.log(name=\"mse\", value=mse)\n",
" run.log_list(name=\"columns\", value=columns)\n",
"\n",
" with open(model_name, \"wb\") as file:\n",
" joblib.dump(value=reg, filename=file)\n",
" \n",
" # upload the serialized model into run history record\n",
" run.upload_file(name=\"outputs/\" + model_name, path_or_stream=model_name)\n",
"\n",
" # now delete the serialized model from local folder since it is already uploaded to run history \n",
" os.remove(path=model_name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# now let's take a look at the experiment in Azure portal.\n",
"experiment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select best model from the experiment\n",
"Load all experiment run metrics recursively from the experiment into a dictionary object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"runs = {}\n",
"run_metrics = {}\n",
"\n",
"for r in tqdm(experiment.get_runs()):\n",
" metrics = r.get_metrics()\n",
" if 'mse' in metrics.keys():\n",
" runs[r.id] = r\n",
" run_metrics[r.id] = metrics"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now find the run with the lowest Mean Squared Error value"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run_id = min(run_metrics, key = lambda k: run_metrics[k]['mse'])\n",
"best_run = runs[best_run_id]\n",
"print('Best run is:', best_run_id)\n",
"print('Metrics:', run_metrics[best_run_id])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can add tags to your runs to make them easier to catalog"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"best_run.tag(key=\"Description\", value=\"The best one\")\n",
"best_run.get_tags()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot MSE over alpha\n",
"\n",
"Let's observe the best model visually by plotting the MSE values over alpha values:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"best_alpha = run_metrics[best_run_id]['alpha']\n",
"min_mse = run_metrics[best_run_id]['mse']\n",
"\n",
"alpha_mse = np.array([(run_metrics[k]['alpha'], run_metrics[k]['mse']) for k in run_metrics.keys()])\n",
"sorted_alpha_mse = alpha_mse[alpha_mse[:,0].argsort()]\n",
"\n",
"plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'r--')\n",
"plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'bo')\n",
"\n",
"plt.xlabel('alpha', fontsize = 14)\n",
"plt.ylabel('mean squared error', fontsize = 14)\n",
"plt.title('MSE over alpha', fontsize = 16)\n",
"\n",
"# plot arrow\n",
"plt.arrow(x = best_alpha, y = min_mse + 39, dx = 0, dy = -26, ls = '-', lw = 0.4,\n",
" width = 0, head_width = .03, head_length = 8)\n",
"\n",
"# plot \"best run\" text\n",
"plt.text(x = best_alpha - 0.08, y = min_mse + 50, s = 'Best Run', fontsize = 14)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register the best model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the model file saved in the run record of best run."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"query history"
]
},
"outputs": [],
"source": [
"for f in best_run.get_file_names():\n",
" print(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can register this model in the model registry of the workspace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from history"
]
},
"outputs": [],
"source": [
"model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify that the model has been registered properly. If you have done this several times you'd see the version number auto-increases each time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"register model from history"
]
},
"outputs": [],
"source": [
"from azureml.core.model import Model\n",
"models = Model.list(workspace=ws, name='best_model')\n",
"for m in models:\n",
" print(m.name, m.version)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also download the registered model. Afterwards, you should see a `model.pkl` file in the current directory. You can then use it for local testing if you'd like."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"download file"
]
},
"outputs": [],
"source": [
"# remove the model file if it is already on disk\n",
"if os.path.isfile('model.pkl'): \n",
" os.remove('model.pkl')\n",
"# download the model\n",
"model.download(target_dir=\"./\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scoring script\n",
"\n",
"Now we are ready to build a Docker image and deploy the model in it as a web service. The first step is creating the scoring script. For convenience, we have created the scoring script for you. It is printed below as text, but you can also run `%pfile ./score.py` in a cell to show the file.\n",
"\n",
"Tbe scoring script consists of two functions: `init` that is used to load the model to memory when starting the container, and `run` that makes the prediction when web service is called. Please pay special attention to how the model is loaded in the `init()` function. When Docker image is built for this model, the actual model file is downloaded and placed on disk, and `get_model_path` function returns the local path where the model is placed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('./score.py', 'r') as scoring_script:\n",
" print(scoring_script.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create environment dependency file\n",
"\n",
"We need a environment dependency file `myenv.yml` to specify which libraries are needed by the scoring script when building the Docker image for web service deployment. We can manually create this file, or we can use the `CondaDependencies` API to automatically create this file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=[\"scikit-learn\"])\n",
"print(myenv.serialize_to_string())\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy web service into an Azure Container Instance\n",
"The deployment process takes the registered model and your scoring scrip, and builds a Docker image. It then deploys the Docker image into Azure Container Instance as a running container with an HTTP endpoint readying for scoring calls. Read more about [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/).\n",
"\n",
"Note ACI is great for quick and cost-effective dev/test deployment scenarios. For production workloads, please use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Please follow in struction in [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n",
" \n",
"** Note: ** The web service creation can take 6-7 minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={'sample name': 'AML 101'}, \n",
" description='This is a great example.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note the below `WebService.deploy_from_model()` function takes a model object registered under the workspace. It then bakes the model file in the Docker image so it can be looked-up using the `Model.get_model_path()` function in `score.py`. \n",
"\n",
"If you have a local model file instead of a registered model object, you can also use the `WebService.deploy()` function which would register the model and then deploy."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
" runtime=\"python\", \n",
" conda_file=\"myenv.yml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"%%time\n",
"# this will take 5-10 minutes to finish\n",
"# you can also use \"az container list\" command to find the ACI being deployed\n",
"service = Webservice.deploy_from_model(name='my-aci-svc',\n",
" deployment_config=aciconfig,\n",
" models=[model],\n",
" image_config=image_config,\n",
" workspace=ws)\n",
"\n",
"service.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Test web service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"print('web service is hosted in ACI:', service.scoring_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the `run` API to call the web service with one row of data to get a prediction."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"import json\n",
"# score the first row from the test set.\n",
"test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
"service.run(input_data = test_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Feed the entire test set and calculate the errors (residual values)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"# score the entire test set.\n",
"test_samples = json.dumps({'data': X_test.tolist()})\n",
"\n",
"result = service.run(input_data = test_samples)\n",
"residual = result - y_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also send raw HTTP request to test the web service."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"import requests\n",
"import json\n",
"\n",
"# 2 rows of input data, each with 10 made-up numerical features\n",
"input_data = \"{\\\"data\\\": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\"\n",
"\n",
"headers = {'Content-Type':'application/json'}\n",
"\n",
"# for AKS deployment you'd need to the service key in the header as well\n",
"# api_key = service.get_key()\n",
"# headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)} \n",
"\n",
"resp = requests.post(service.scoring_uri, input_data, headers = headers)\n",
"print(resp.text)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Residual graph\n",
"Plot a residual value graph to chart the errors on the entire test set. Observe the nice bell curve."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
"f.suptitle('Residual Values', fontsize = 18)\n",
"\n",
"f.set_figheight(6)\n",
"f.set_figwidth(14)\n",
"\n",
"a0.plot(residual, 'bo', alpha=0.4);\n",
"a0.plot([0,90], [0,0], 'r', lw=2)\n",
"a0.set_ylabel('residue values', fontsize=14)\n",
"a0.set_xlabel('test data set', fontsize=14)\n",
"\n",
"a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step');\n",
"a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10);\n",
"a1.set_yticklabels([])\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Delete ACI to clean up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deleting ACI is super fast!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"deploy service",
"aci"
]
},
"outputs": [],
"source": [
"%%time\n",
"service.delete()"
]
}
],
"metadata": {
"authors": [
{
"name": "roastala"
}
],
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"display_name": "Python 3.6",
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"file_extension": ".py",
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