Lab 5 - Deploy to Managed Endpoint

This lab may not work in an Azure Pass Subscription.
For this lab to work, the Microsoft.PolicyInsights needs to be registered in your subscription. Follow this tutorial​
# Delete the compute cluster (only needed if you have an Azue subscription)
az ml compute delete -n gpu-cluster
In this lab you are going to deploy the model in managed endpoint.
Get the scoring script
# Create a directory
mkdir deploy
cd deploy
​
# Download the scoring script
wget https://raw.githubusercontent.com/GlobalAICommunity/back-together-2021/main/workshop-assets/amls/score.py
Stay in the same directory
Create the scoring environment
Create an empty yml file for the environment configuration.
code conda.yml
Add the content below to the conda.yml file.
name: project_environment
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.6.2
  - pip:
    - azureml-defaults
    - torch
    - torchvision
    - pillow==5.4.1
Create the endpoint configuration
Create an empty yml file for the deployment configuration.
code deployment.yml
Add the content below to the file
$schema: https://azuremlschemas.azureedge.net/latest/managedOnlineEndpoint.schema.json
name: <your-name>
type: online
auth_mode: key
traffic:
  version1: 100
deployments:
  - name: version1    
    app_insights_enabled: true
    model: azureml:SimpsonsClassification-pytorch:1
    code_configuration:
      code: 
        local_path: ./
      scoring_script: score.py
    environment: 
      name: simpsons-inference
      version: 1           
      path: .
      conda_file: file:conda.yml
      docker:
        image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20210727.v1
    instance_type: Standard_F4s_v2
    scale_settings:
      scale_type: Manual
      instance_count: 1
      min_instances: 1
      max_instances: 1
Replace: <your-name> with your unique name, like: henks-endpoint
Use the command below to create a managed endpoint
az ml endpoint create --file deployment.yml
Test the endpoint
Create a new file for the request json.
code request.json
Add the content below to the file
{
    "image":"https://raw.githubusercontent.com/hnky/dataset-lego-figures/master/_test/Bart.jpg"
}
Use the command below to invoke the endpoint!
az ml endpoint invoke -n simpsons-endpoint --request-file request.json
As a result you show see that it has seen Bar Simpson on the image.
Recap

Last modified 1yr ago