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@cda1f27 official
by microsoftmicrosoft/github-copilot-for-azure253 stars
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Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).

  • 4 files
  • 35.4 KB
  • MIT
  • Updated last week
  • GitHub

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SKILL.md

≈139 tokens always: the name and description. ≈1.1k when used: this file. ≈7.8k more on demand in 3 files.

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
  • Microsoft 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: Microsoft 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 Microsoft Foundry operations
  • quota — For quota viewing, increase requests, and troubleshooting quota errors, defer to this skill
  • azure-quick-review - Review Azure resources for compliance
  • cost-estimation - Estimate costs through the separately installed azure-cost plugin
  • azure-validate - Validate Azure infrastructure before deployment

Source: SKILL.md on GitHub

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Provenance

Signed by skilld at cda1f27. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 19 hours ago.

Activeupdated last week

Capability

metadata
{
  "author": "Microsoft",
  "version": "1.0.1"
}

Topics

  • azure
  • openai
  • deployment
  • region-selection
  • capacity-planning
  • azure-cli
  • model-deployment
  • cognitive-services

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What it does

Deploys Azure OpenAI models to the best available region by checking capacity across all options and automatically selecting the optimal location. Handles cases where the current region lacks capacity by querying alternatives and creating projects as needed, using the GlobalStandard SKU and Azure CLI.

Generated from the current SKILL.md.

Frequently asked

What happens if my current region has no capacity?
The skill automatically queries all available regions, displays alternatives with their available capacity, and lets you select a different region to deploy to.
Does this skill support custom SKU or capacity configuration?
No. This skill uses GlobalStandard SKU only with automatic capacity calculation. For custom SKU selection or specific capacity tuning, use the customize skill instead.
What permissions do I need in Azure?
You need Cognitive Services Contributor role on your Azure subscription to read capacity and create deployments.
Can I deploy to a specific model version?
The skill lists available models and versions for selection, but does not support forcing a specific version. Use the customize skill if you need explicit version control.
What should I do if all regions show zero capacity?
Defer to the quota skill to request a quota increase, check for existing deployments consuming quota, or try alternative models.

Generated from the current SKILL.md. These answers refresh after source changes.