Add baseline

Author: MDUYN

Dockerfile

@@ -0,0 +1,19 @@
+FROM mcr.microsoft.com/azure-cli
+
+# Install jq (for parsing JSON)
+RUN apt-get update && apt-get install -y jq
+
+# Install Azure ML CLI extension
+RUN az extension add --name ml -y
+
+# Set the working directory
+WORKDIR /workspace
+
+# Copy the entrypoint script into the container
+COPY entrypoint.sh /workspace/entrypoint.sh
+
+# Set the entrypoint script to be executable
+RUN chmod +x /workspace/entrypoint.sh
+
+# Define default command
+ENTRYPOINT ["/workspace/entrypoint.sh"]

README.md

@@ -0,0 +1,25 @@
+# Deploy AML Online Endpoint - GitHub Action
+
+This GitHub Action deploys an **Azure Machine Learning Online Endpoint** using **Azure CLI** (via a Bash script).
+
+## 🚀 Usage
+
+### **1. Add to Your Workflow**
+
+```yaml
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Deploy AML Online Endpoint
+        uses: coding-kitties/create-aml-online-endpoint@v1
+        with:
+          azure_credentials: ${{ secrets.AZURE_CREDENTIALS }}
+          resource_group: 'my-resource-group'
+          workspace_name: 'my-workspace'
+          endpoint_file: './config/endpoint.yml'
+          deployment_file: './config/deployment.yml'
+```

action.yaml

@@ -0,0 +1,39 @@
+name: 'Deploy AML Online Endpoint with Docker'
+description: 'Deploy an Azure Machine Learning Online Endpoint using Docker, Azure CLI, and Azure Login'
+author: 'Marc van Duyn'
+branding:
+  icon: 'cloud'
+  color: 'blue'
+
+inputs:
+  azure_credentials:
+    description: 'Azure credentials in JSON format (Service Principal)'
+    required: true
+  endpointName:
+    description: 'Name of the endpoint'
+    required: true
+  resource_group:
+    description: 'Azure Resource Group'
+    required: true
+  workspace_name:
+    description: 'Azure ML Workspace Name'
+    required: true
+
+runs:
+  using: 'composite'
+  steps:
+    # Step 1: Log into Azure using Azure Login action
+    - name: Log into Azure
+      uses: azure/login@v1
+      with:
+        creds: ${{ secrets.AZURE_CREDENTIALS }}
+
+    # Step 2: Checkout repository
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Step 3: Set up Docker (if needed) and run the entrypoint script
+    - name: Run Docker container with entrypoint.sh
+      run: |
+        docker build -t aml-deploy .
+        docker run -v $PWD:/workspace -e AZURE_CREDENTIALS="${{ secrets.AZURE_CREDENTIALS }}" aml-deploy /workspace/entrypoint.sh "${{ inputs.azure_credentials }}" "${{ inputs.endpointName }}" "${{ inputs.workspace_name }}" "${{ inputs.resource_group }}"

entrypoint.sh

@@ -0,0 +1,65 @@
+#!/bin/bash
+set -e
+
+# Log into Azure using the provided credentials
+echo "Logging into Azure..."
+echo "$1" > azure_credentials.json
+az login --service-principal --username $(jq -r .clientId azure_credentials.json) --password $(jq -r .clientSecret azure_credentials.json) --tenant $(jq -r .tenantId azure_credentials.json)
+
+local endpoint="$2"                 # The endpoint to which the deployment belongs
+local workspace="$3"                # The machine learning workspace name where the endpoint belongs
+local resourceGroup="$4"            # The resource group where the deployment and endpoint belong
+
+# Set up Azure CLI and ML extension
+echo "Setting up Azure CLI..."
+az account show
+az extension add --name ml -y
+
+echo "Creating Endpoint $endpoint in Workspace $workspace in Resource Group $resourceGroup..."
+
+# Check if workspace exists
+local workspaceExists
+workspaceExists=$(az ml workspace show \
+    --resource-group "$resourceGroup" \
+    --workspace-name "$workspace" 2>/dev/null)
+
+if [ -z "$workspaceExists" ]; then
+    echo "Workspace $workspace does not exist. Cannot deploy endpoint."
+    exit 1
+fi
+
+# Check if endpoint already exists
+local endpointExists
+endpointExists=$(az ml online-endpoint show \
+    --name "$endpoint" \
+    --resource-group "$resourceGroup" \
+    --workspace-name "$workspace" 2>/dev/null)
+
+if [ -n "$endpointExists" ]; then
+    echo "Endpoint $endpoint exists; updating the endpoint"
+    az ml online-endpoint update \
+        --name "$endpoint" \
+        --file endpoint-apply.yaml \
+        --resource-group "$resourceGroup" \
+        --workspace-name "$workspace"
+else
+    echo "Creating endpoint $endpoint"
+    cat endpoint-apply.yml
+    az ml online-endpoint create \
+        --file endpoint-apply.yaml \
+        --resource-group "$resourceGroup" \
+        --workspace-name "$workspace"
+fi
+
+endpointExists=$(az ml online-endpoint show \
+        --name "$endpoint" \
+        --resource-group "$resourceGroup" \
+        --workspace-name "$workspace" 2>/dev/null)
+
+if [ -n "$endpointExists" ]; then
+    echo "Endpoint $endpoint created successfully"
+    exit 0
+else
+    echo "Endpoint $endpoint creation failed"
+    exit 1
+fi