File size: 1,489 Bytes
647f69c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | # 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.
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