deploy-sagemaker

name	deploy-sagemaker
description	Deploys the voice agent to AWS with self-hosted Deepgram STT/TTS on SageMaker GPU endpoints. Guides through GPU quota checks, Marketplace subscriptions, model package ARN configuration, and CDK deployment. Use for production deployments or when audio must stay within the VPC.

Deploy — SageMaker Mode

You are guiding the user through deploying the voice agent with self-hosted Deepgram STT/TTS on SageMaker GPU endpoints. Audio never leaves the VPC.

When This Skill Activates

• User wants a production deployment
• User mentions SageMaker, self-hosted, data residency, or VPC-only
• User has already subscribed to Deepgram on AWS Marketplace

What To Do

Phase 1: Pre-Flight Checks

Run the same checks as deploy-cloud-api (AWS credentials, Node.js, Docker, Bedrock access) and additionally:

1. Confirm account:

   aws sts get-caller-identity
   

   Show account ID and region. Get explicit confirmation this is the right account.

2. Check SageMaker GPU quotas:

   aws service-quotas get-service-quota --service-code sagemaker --quota-code "L-1B43B3DD" --query 'Quota.Value' --output text
   aws service-quotas get-service-quota --service-code sagemaker --quota-code "L-E460AE79" --query 'Quota.Value' --output text
   

   • STT needs ml.g6.2xlarge quota >= 2
   • TTS needs ml.g6.12xlarge quota >= 2
   • If insufficient: suggest deploying cloud API mode first while quotas are pending (24-48 hours)

Report all results as a summary checklist.

Phase 2: Verify Marketplace Subscriptions

Ask for two model package ARNs. If the user doesn't have them, direct them to docs/reference/deepgram-marketplace-setup.md.

Expected format:

• STT: arn:aws:sagemaker:<region>:865070037744:model-package/deepgram-streaming-stt-...
• TTS: arn:aws:sagemaker:<region>:865070037744:model-package/deepgram-streaming-tts-...

Validate: both must start with arn:aws:sagemaker: and contain model-package/. Region must match deployment region.

Phase 3: Configure Environment

cd infrastructure && cp .env.example .env

Set model package ARNs in .env. Region in the ARNs must match AWS_REGION.

Phase 4: Explain What Will Be Created

Same resources as cloud-api mode, plus:

Resource	Purpose
SageMaker STT (ml.g6.2xlarge)	Deepgram Nova-3 on 1x L4 GPU
SageMaker TTS (ml.g6.12xlarge)	Deepgram Aura on 4x L4 GPU

Deployment takes 20-25 minutes (SageMaker endpoints ~15 min).

Cost responsibility: The user is responsible for all AWS charges incurred by these resources. SageMaker GPU endpoints incur charges while running. Remind them to use the destroy-project skill to tear down resources when done.

Get explicit confirmation before deploying.

Phase 5: Deploy Foundation + Configure Secrets + Deploy Remaining

Deploy in two stages so the ECS container picks up the Daily API key on first boot.

1. Install and bootstrap:

   cd infrastructure && npm install
   

   Check if CDK is bootstrapped; if not, run npx cdk bootstrap.

2. Deploy foundation stacks (Network + Storage):

   npx cdk deploy VoiceAgentNetwork VoiceAgentStorage --require-approval never
   

3. Configure secrets now, before deploying ECS: SageMaker mode only needs DAILY_API_KEY (no Deepgram/Cartesia cloud keys). Write to backend/voice-agent/.env, then push:

   ./scripts/init-secrets.sh
   

4. Deploy remaining stacks (SageMaker + ECS + BotRunner):

   npx cdk deploy VoiceAgentSageMaker VoiceAgentEcs VoiceAgentBotRunner --require-approval never
   

   SageMaker endpoints take 10-15 minutes to provision. This is normal.

   • ResourceLimitExceeded = GPU quota insufficient
   • Model package not found = wrong ARN or region mismatch

Phase 6: Verify SageMaker Endpoints

STT_ENDPOINT=$(aws ssm get-parameter --name "/voice-agent/sagemaker/stt-endpoint-name" --query 'Parameter.Value' --output text)
TTS_ENDPOINT=$(aws ssm get-parameter --name "/voice-agent/sagemaker/tts-endpoint-name" --query 'Parameter.Value' --output text)

aws sagemaker describe-endpoint --endpoint-name "$STT_ENDPOINT" --query 'EndpointStatus' --output text
aws sagemaker describe-endpoint --endpoint-name "$TTS_ENDPOINT" --query 'EndpointStatus' --output text

Both should show "InService". If "Creating", wait and recheck.

Phase 7: Show Progress and Next Steps

Same as deploy-cloud-api Phase 7 -- show progress checklist, direct to configure-daily. Remind user to use the destroy-project skill when done to release Daily phone numbers and tear down all AWS resources.