CLI Deployments

Ship code from the command line. Python files, Docker images, LLM servers.

Commands

Deployment Sources

The deploy command accepts:

• Python files (.py): Deployed with a configurable base image
• Docker images: Deployed directly

Health Checks

Configure liveness, readiness, and startup probes to ensure your application runs correctly. The CLI supports HTTP path probes with configurable timing.

Key concepts:

• Liveness probe: Restarts container if unhealthy
• Readiness probe: Controls traffic routing
• Startup probe: For slow-starting applications

Use --health-path as shorthand for all probes, or configure each individually.

LLM Inference Servers

vLLM

Deploy OpenAI-compatible inference servers with vLLM. Supports tensor parallelism, quantization (AWQ, GPTQ, FP8), and custom model configurations.

SGLang

Alternative inference server with similar capabilities and SGLang-specific optimizations.

Both commands auto-detect GPU requirements based on model size when not specified.

Troubleshooting

Deployment stuck in Pending

1. Check status with --show-phases for details
2. Check logs for errors
3. Verify GPU availability with ls --compute citadel

Container crash loop

Common causes:

• Application error on startup
• Missing environment variables
• Port binding issues

Check logs for specific errors.

Health check failures

1. Ensure health endpoint responds with 200 OK
2. Increase initial delay for slow-starting apps
3. Verify port matches your application

Next Steps

• LLM Inference: vLLM and SGLang deployment guides
• Account Management: Billing and API tokens
• Python SDK: Programmatic deployments