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preset

Automates Azure OpenAI model deployment by checking capacity across regions and deploying to the best available option.

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含まれるファイル（4）

  • SKILL.md4.3 KB
  • EXAMPLES.md2.5 KB
  • references/preset-workflow.md16.9 KB
  • references/workflow.md5.1 KB

SKILL.md（原文）

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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

  1. Verifies Azure authentication and project scope
  2. Checks capacity in current project's region
  3. If no capacity: analyzes all regions and shows available alternatives
  4. Filters projects by selected region
  5. Supports creating new projects if needed
  6. Deploys model with GlobalStandard SKU
  7. 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

PhaseActionKey Commands
1. Verify AuthCheck Azure CLI login and subscriptionaz account show, az login
2. Get ProjectParse PROJECT_RESOURCE_ID ARM ID, verify existsaz cognitiveservices account show
3. Get ModelList available models, user selects model + versionaz cognitiveservices account list-models
4. Check Current RegionQuery capacity using GlobalStandard SKUaz rest --method GET .../modelCapacities
5. Multi-Region QueryIf no local capacity, query all regionsSame capacity API without location filter
6. Select Region + ProjectUser picks region; find or create projectaz cognitiveservices account list, az cognitiveservices account create
7. DeployGenerate unique name, calculate capacity (50% available, min 50 TPM), create deploymentaz cognitiveservices account deployment create

For detailed step-by-step instructions, see workflow reference.

Error Handling

ErrorSymptomResolution
Auth failureaz account show returns errorRun az login then az account set --subscription <id>
No quotaAll regions show 0 capacityDefer to the quota skill for increase requests and troubleshooting; check existing deployments; try alternative models
Model not foundEmpty capacity listVerify 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 unavailableRegion doesn't support modelSelect 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

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