pyfagorass Deploy Model to Optimal Region
Automates intelligent Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.
What This Skill Does
- Verifies Azure authentication and project scope
- Checks capacity in current project's region
- If no capacity: analyzes all regions and shows available alternatives
- Filters projects by selected region
- Supports creating new projects if needed
- Deploys model with GlobalStandard SKU
- Monitors deployment progress
Prerequisites
- Azure CLI installed and configured
- Active Azure subscription with Cognitive Services read/create permissions
- Azure AI Foundry project resource ID (
PROJECT_RESOURCE_IDenv var or provided interactively)- Format:
/subscriptions/{sub-id}/resourceGroups/{rg}/providers/Microsoft.CognitiveServices/accounts/{account}/projects/{project} - Found in: Azure AI Foundry portal → Project → Overview → Resource ID
- Format:
Quick Workflow
Fast Path (Current Region Has Capacity)
1. Check authentication → 2. Get project → 3. Check current region capacity
→ 4. Deploy immediately
Alternative Region Path (No Capacity)
1. Check authentication → 2. Get project → 3. Check current region (no capacity)
→ 4. Query all regions → 5. Show alternatives → 6. Select region + project
→ 7. Deploy
Deployment Phases
| Phase | Action | Key Commands |
|---|---|---|
| 1. Verify Auth | Check Azure CLI login and subscription | az account show, az login |
| 2. Get Project | Parse PROJECT_RESOURCE_ID ARM ID, verify exists |
az cognitiveservices account show |
| 3. Get Model | List available models, user selects model + version | az cognitiveservices account list-models |
| 4. Check Current Region | Query capacity using GlobalStandard SKU | az rest --method GET .../modelCapacities |
| 5. Multi-Region Query | If no local capacity, query all regions | Same capacity API without location filter |
| 6. Select Region + Project | User picks region; find or create project | az cognitiveservices account list, az cognitiveservices account create |
| 7. Deploy | Generate unique name, calculate capacity (50% available, min 50 TPM), create deployment | az cognitiveservices account deployment create |
For detailed step-by-step instructions, see workflow reference.
Error Handling
| Error | Symptom | Resolution |
|---|---|---|
| Auth failure | az account show returns error |
Run az login then az account set --subscription <id> |
| No quota | All regions show 0 capacity | Defer to the quota skill for increase requests and troubleshooting; check existing deployments; try alternative models |
| Model not found | Empty capacity list | Verify model name with az cognitiveservices account list-models; check case sensitivity |
| Name conflict | "deployment already exists" | Append suffix to deployment name (handled automatically by generate_deployment_name script) |
| Region unavailable | Region doesn't support model | Select a different region from the available list |
| Permission denied | "Forbidden" or "Unauthorized" | Verify Cognitive Services Contributor role: az role assignment list --assignee <user> |
Advanced Usage
# Custom capacity
az cognitiveservices account deployment create ... --sku-capacity <value>
# Check deployment status
az cognitiveservices account deployment show --name <acct> --resource-group <rg> --deployment-name <name> --query "{Status:properties.provisioningState}"
# Delete deployment
az cognitiveservices account deployment delete --name <acct> --resource-group <rg> --deployment-name <name>
Notes
- SKU: GlobalStandard only — API Version: 2024-10-01 (GA stable)
Related Skills
- microsoft-foundry - Parent skill for Azure AI Foundry operations
- quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
- azure-quick-review - Review Azure resources for compliance
- azure-cost-estimation - Estimate costs for Azure deployments
- azure-validate - Validate Azure infrastructure before deployment