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Thông minh triển khai các mô hình Azure OpenAI đến các khu vực tối ưu bằng cách phân tích dung lượng trên tất cả các khu vực khả dụng. Tự động kiểm tra khu vực hiện tại trước và…

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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_ID env 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

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