deployments/azure/README.md

# Azure AI Foundry Deployment

Deploy SAM3 to Azure AI Foundry (pending GPU quota).

## Quick Deploy

```bash
./deployments/azure/deploy.sh
```

This will build and push the image to Azure Container Registry.

## Configuration

- **Registry**: `sam3acr.azurecr.io`
- **Image**: `sam3-foundry:latest`
- **Endpoint**: `sam3-foundry` (to be created)
- **Resource Group**: `productionline-test`
- **Instance Type**: Standard_NC6s_v3 (Tesla V100) or higher

## Status

⏳ **Pending GPU Quota Approval**

Once GPU quota is approved, create the endpoint:

## Create Endpoint (Azure Portal)

1. Navigate to Azure AI Foundry workspace
2. Go to **Endpoints** → **Real-time endpoints**
3. Click **Create**
4. Select **Custom container**
5. Image: `sam3acr.azurecr.io/sam3-foundry:latest`
6. Instance type: **Standard_NC6s_v3** or higher
7. Deploy

## Create Endpoint (Azure CLI)

```bash
# Create endpoint
az ml online-endpoint create \
  --name sam3-foundry \
  --resource-group productionline-test \
  --workspace-name <your-workspace>

# Create deployment
az ml online-deployment create \
  --name sam3-foundry-deployment \
  --endpoint sam3-foundry \
  --model-uri sam3acr.azurecr.io/sam3-foundry:latest \
  --instance-type Standard_NC6s_v3 \
  --instance-count 1
```

## Testing

Once deployed, update the endpoint URL in the test script and run:

```bash
python3 scripts/test/test_api.py
```

## For More Information

See `docs/DEPLOYMENT.md` for complete Azure AI Foundry deployment guide.