Skip to content

roalexan/azureml

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

40 Commits
pipelines
pipelines
 
 
README.md
README.md
 
 

Repository files navigation

Options for Scheduling a Batch Scoring Pipeline

Prerequisites

  • An Azure subscription
  • A resource group

Terms

  • DSVM = Data Science Virtual Machine
  • AML = Azure Machine Learning
  • ACI = Azure Container Instance
  • ARM = Azure Resource Manager
  • AKS = Azure Kubernetes Service

Create Dev Environment

Use ARM Script Locally to Create Windows DSVM

  • TODO

Use ARM Script Locally to Create Linux DSVM

  • TODO

Use Python Locally to Create Windows DSVM

  • TODO

Use Python Locally to Create Linux DSVM

  • TODO

Use Portal to Create Windows DSVM

  • Browse: portal.azure.com
  • Select: your resource group
  • Click: + Add
  • Type: dsvm
  • Click: Data Science Virtual Machine - Windows 2016 > Create
  • Select: Subscription: <your subscription>
  • Select: Resource Group: <your resource group>
  • Type: Virtual machine name: <your vm name>
  • Select: Region: <your region>
  • Type: Username: <your user name>
  • Type: Password: <your password>
  • Click: Review + create > Create

or

Use Portal to Create Linux DSVM

  • Browse: portal.azure.com
  • Select: <your resource group>
  • Click: + Add
  • Type: dsvm
  • Click: Data Science Virtual Machine for Linux (Ubuntu) > Create
  • Select: Subscription: <your subscription>
  • Select: Resource Group: <your resource group>
  • Type: Virtual machine name: <your vm name>
  • Select: Region: <your region>
  • Select: Authentication
  • Type: Password
  • Type: Username: <your user name>
  • Type: Password: <your password>
  • Click: Review + create > Create

Open Jupyter Notebook on Dev Environment

On Windows DSVM

  • Login
    • Click: <your vm> > Connect
    • Click: Download RDP File
    • Save: locally
    • Double click: <your RDP file>
    • Click: OK > Connect
    • Click: More choices
    • Select: Use a different account
    • Type: User name: <your user name>
    • Type: Password: <your password>
    • Click: OK > Yes
  • Double click: Jupyter (takes a minute)

or

On Linux DSVM

  • Setup port forwarding (to access Jupyter Notebook on local browser)
    • Open: PuTTY
    • Expand: Connection > SSH > Tunnels
    • Type: Source port: 8000
    • Type: Destination: localhost:8000
    • Click: Add > Save
    • SSH into DSVM (while open, port forwarding is active)
  • Browse: https://localhost:8000
  • Login using VM's username/password

Register Subscription with ACI Using Jupyter Notebook on Dev Environment

  • Expand: AzureML
  • Click: configuration.ipynb
  • Run: first cell to verify SDK version (should see output like version 1.0.2) 
    import azureml.core

print("This notebook was created using version 1.0.2 of the Azure ML SDK")
print("You are currently using version", azureml.core.VERSION, "of the Azure ML SDK")
  • Insert new cell to login 
    !az login
  • Insert new cell to see if your subscription is registered with ACI 
    !az provider show -n Microsoft.ContainerInstance -o table
  • If not registered, insert new cell to register your subscription with ACI 
    !az provider register -n Microsoft.ContainerInstance
  • Replace default values in next cell and run to set workspace parameters 
    import os

subscription_id = os.getenv("SUBSCRIPTION_ID", default="<my-subscription-id>")
resource_group = os.getenv("RESOURCE_GROUP", default="<my-resource-group>")
workspace_name = os.getenv("WORKSPACE_NAME", default="<my-workspace-name>")
workspace_region = os.getenv("WORKSPACE_REGION", default="eastus2")

Create AML workspace

Using Jupyter Notebook on Dev Environment

  • Run next cell to create new workspace

or

Using Portal (no hiccups)

  • Browse: portal.azure.com
  • Select: <your resource group>
  • Click: + Add
  • Type: machine learning
  • Click: Machine Learning service workspace > Create
  • Type: Workspace name: <your workspace name>
  • Select: Subscription: <your subscription>
  • Select: Resource group: <your resource group>
  • Select: Location: <your location>

or

Using Portal (hiccup - policy requires "Secure Transfer Required" for Storage Accounts)

  • Browse: portal.azure.com
  • Select: <your resource group>
  • Click: + Add
  • Type: machine learning
  • Click: Machine Learning service workspace > Create
  • Type: Workspace name: <your workspace name>
  • Select: Subscription: <your subscription>
  • Select: Resource group: <your resource group>
  • Select: Location: <your location>
  • Click: Automation options (Don't click Create)
  • Click: Download > Save As > <your local folder>
  • Copy and paste zip file to your DSVM (drag and drop won't work, but copy and paste will)
  • Extract contents of zip file
  • Open a command prompt
  • Change to the folder containing your extracted contents
  • Type: code . (this will open Visual Studio Code)
  • Modify template.json by inserting the below snippet where the Storage Account is created
"properties": {
	"supportsHttpsTrafficOnly": true
},
  • From command prompt, run:
az group deployment create -g ``<your resource group>`` --subscription ``<your subscription id>`` --template-file template.json --parameters @parameters.json

Create Compute Cluster Using Jupyter Notebook on Dev Environment

  • Run: next cell to create CPU cluster
  • Run: next cell to create GPU cluster (optional)

Create and Deploy Model Using Jupyter Notebook on Dev Environment

  • Expand: how-to-use-azureml > machine-learning-pipelines > pipeline-batch-scoring
  • Click: pipeline-batch-scoring.ipynb
  • Run each cell individually (the first experiment run takes about 20 minutes, subsequent runs take a few seconds)

Test calling REST AKS AML endpoint

  • Insert new cell to set subscription id 
    # https://docs.microsoft.com/en-us/cli/azure/account?view=azure-cli-latest
!az account set -s "{ws.subscription_id}"
!az account show
  • Insert new cell to create service principal 
    # https://docs.microsoft.com/en-us/cli/azure/ad/sp?view=azure-cli-latest
!az ad sp create-for-rbac --name ``<your app name>``
  • Insert new cell to generate token 
    # https://docs.microsoft.com/en-us/cli/azure/create-an-azure-service-principal-azure-cli?view=azure-cli-latest
from azure.common.credentials import ServicePrincipalCredentials

# "client_id" is the value for "appId" show above. you can see it the registered app on AAD in the portal
# the "secret" is the value for "password" shown above. You can manage passwords for registered apps on AAD in the portal
# the "tenant" is show above. It's also known as the "directory id" and you can get it on AAD in the portal and clicking "properties" 
credentials = ServicePrincipalCredentials(client_id='{appId}',
                                          secret='{password}',
                                          tenant='{tenant}')
api_key = credentials.token['access_token']
aad_token = {'Authorization':('Bearer '+ api_key)}
  • Insert new cell to call endpoint 
    response = requests.post(rest_endpoint, 
                      headers=aad_token, 
                      json={"ExperimentName": "batch_scoring",
                            "ParameterAssignments": {"param_batch_size": 50}})
run_id = response.json()["Id"]

Schedule using ADF

  • Create ADF
    • Browse: portal.azure.com
    • Select: <your resource group>
    • Click: + Add
    • Type: data factory
    • Click: Data Factory > Create
    • Type: Name: <your name>
    • Select: Subscription: <your subscription>
    • Select: Resource Group: Use existing
    • Select: <your resource group>
    • Select: Version: V2
    • Select: Location: East US
    • Click: Create
  • Authorize ADF access to AML workspace using Managed Service Identity
    • Click on <your AML workspace> > Access control (IAM) > Add role assignment
    • Select: Role: Contributor
    • Type: Select: <your ADF name>
    • Click: <your ADF name> > Save
  • Configure ADF
    • Click: Author & Monitor
    • Click: Create pipeline
    • Expand: General
    • Drag: Web
    • Click: Settings
    • Paste: URL: <your endpoint>
    • Select: Method: POST
    • Click: Headers: + New: NAME: Content-Type, VALUE: application/json
    • Paste: Body: {"ExperimentName": "batch_scoring", "ParameterAssignments": {"param_batch_size": 50}}
    • Expand: Advanced
    • Select: MSI
    • Type: Resource: https://management.azure.com/
    • Click: Validate
    • Click: Publish All
    • Click: Trigger > Trigger Now
  • Add Trigger
    • Click: Trigger > New/Edit
    • Select: Choose trigger... > + New
    • Type: Name: <your trigger name>
    • Select: Schedule
    • Type: Start Date (UTC): <your start date>
    • Type: Recurrence: Every: <your interval>
    • Click: Next > Finish > Publish All

Schedule using AzureFunctions

  • Setup Local Build Environment
    • Install Python 3.6
    • Install [Azure Functions Core Tools]
    • Install .NET Core 2.x SDK for Windows
    • Install Node.js
    • Install Core Tools package 
      npm install -g azure-functions-core-tools
    • Install the Azure CLI (needed to publish and run in Azure)
    • Open a command prompt
    • Type (set variables) set SUBSCRIPTION_ID=<your subscription id> set RESOURCE_GROUP=<your resource group> set STORAGE_ACCOUNT=<your storage account> set APP_PLAN=<your app plan> set FUNCTION_APP=<your function app>
    • Type (create and activate a virtual environment) 
      py -3.6 -m venv .env (Create and activate a virtual environment)
.env\scripts\activate
  • Create a Local Functions Project (contains your functions)
    • Type: 
      func init %FUNCTION_APP%
    • Mouse down and select: python (preview)
    • Click: Enter
  • Create, Deploy, and Test EchoName Function Locally
    • Type: 
      cd %FUNCTION_APP%
    • Type: (create a new function) 
      func new
    • Choose template: HTTP trigger
    • Type: Function name: EchoName
    • Click: Enter
    • Type: func host start (start local functions)
    • Copy and paste URL in browser, and replace with a name of your choosing: 
      http://localhost:7071/api/EchoNameFunction?name=<yourname>
  • Deploy and Test EchoName Function on Azure
    • Type: 
      az login
    • Type: (set your subscription id) 
      az account set --subscription %SUBSCRIPTION_ID%
    • Type: (show account) 
      az account show
    • Type: (create a resource group) 
      az group create --name %RESOURCE_GROUP% --location eastus
    • Type: (create a storage account) 
      az storage account create --name %STORAGE_ACCOUNT% --location eastus --resource-group %RESOURCE_GROUP% --sku Standard_LRS
    • Type: (create an app service plan) 
      az appservice plan create -g %RESOURCE_GROUP% -n %APP_PLAN% --is-linux --number-of-workers 4 --sku S1
    • Type: (Create a Python function app running on Linux that will contain the functions. This uses a consumption plan) 
      az functionapp create --resource-group %RESOURCE_GROUP% --os-type Linux --consumption-plan-location eastus --runtime python --name %FUNCTION_APP% --storage-account %STORAGE_ACCOUNT%
    • Type: (Create a Python function app running on Linux that will contain the functions. This uses an existing app service plan) 
      az functionapp create --resource-group %RESOURCE_GROUP% --os-type Linux --plan %APP_PLAN% --runtime python --name %FUNCTION_APP% --storage-account %STORAGE_ACCOUNT% --deployment-container-image-name azure-functions-python3.6:2.0
    • Type: (Deploy the function app project) 
      func azure functionapp publish %FUNCTION_APP%
    • Expand: <function app> > Functions > EchoName
    • Click: Get function URL > Copy
    • Paste URL into browser, and add: 
      ?name=<your name>
  • Create, Deploy, and Test BatchScoring Function Locally
    • Change directory: FunctionsProject
    • Type: (create a new function) 
      func new
    • Choose template: HTTP trigger
    • Type: Function name: BatchScoringFunction
    • Click: Enter
    • Type: (Add requests library) 
      pip install requests
    • Edit init.py 
      def main(req: func.HttpRequest) -> func.HttpResponse:
logging.info('Python HTTP trigger function processed a request.')

url = "<your endpoint>"
headers = {"Content-Type": "application/json",
    "Authorization": "<your token>"}
body = {"ExperimentName": "batch_scoring", "ParameterAssignments": {"param_batch_size": 50}}
r = requests.post(url, data=body, headers=headers)

return func.HttpResponse(r.text)
    • Type: func host start (start local functions)
    • Copy and paste URL in browser: 
      http://localhost:7071/api/BatchScoringFunction
  • Deploy and Test BatchScoring Function on Azure
    • Edit init.py 
      import logging
import requests
import azure.functions as func

def main(req: func.HttpRequest) -> func.HttpResponse:
   logging.info('Python HTTP trigger function processed a request..')    
   url = "<your function url>"
   headers = {"Content-Type": "application/json"}
   body = {"ExperimentName": "batch_scoring", "ParameterAssignments": {"param_batch_size": 50}}
   try:       
      response = str(requests.post(url, 
         headers=headers, 
         json=body))        
      return func.HttpResponse(response)
   except Exception as e:
      return func.HttpResponse("exception: " + str(e))
    • goto function app
    • click Platform features > All Settings > Authentication/Authorization
    • toggle: App Service Authentication: On

Schedule using Pipeline Scheduling

  • Create a new schedule 
    from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule

SCHED_FREQUENCY = "<your time unit>"
SCHED_INTERVAL = <number of time units>
RESOURCE_GROUP_NAME = "<your resource group name>"
experiment_name = "<your experiment name>"
recurrence = ScheduleRecurrence(frequency=SCHED_FREQUENCY, interval=SCHED_INTERVAL)
schedule = Schedule.create(
    workspace=ws,
    name="{}_sched".format(RESOURCE_GROUP_NAME),
    pipeline_id=published_id,
    experiment_name=experiment_name,
    recurrence=recurrence,
    description="{}_sched".format(RESOURCE_GROUP_NAME),
)
  • Disable the schedule (if needed) 
    schedule.disable()

Schedule using LogicApps

  • TODO

Links

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

3 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

No releases published

Packages

No packages published

Languages