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Deploys custom Dockerized inference workers on Together AI GPUs with Sprocket SDK and Jig CLI. Handles async queue jobs for custom runtimes needing container control.
How this skill is triggered — by the user, by Claude, or both
Slash command
/togetherai-skills:together-dedicated-containersThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use Dedicated Container Inference when the user needs a custom runtime, not just managed model
Use Dedicated Container Inference when the user needs a custom runtime, not just managed model hosting.
Core building blocks:
together-dedicated-endpoints for standard model hosting without custom containerstogether-gpu-clusters for full cluster ownership and orchestration controltogether-chat-completions, together-images, or together-video when a serverless product already covers the taskpyproject.toml for image, runtime, autoscaling, and mounts.together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".pyproject.toml as the source of truth for deployment behavior.npx claudepluginhub zainhas/skillsDeploys custom Dockerized inference workers on Together AI's managed GPU infrastructure using Jig CLI, Sprocket SDK, and async queue jobs for container-level control.
Runs Python code on Modal's serverless platform with GPUs, autoscaling, and containerized dependencies. Use for ML model deployment, batch processing, scheduled jobs, and GPU-accelerated APIs.
Deploys ML training jobs and inference services to Vast.ai GPU cloud using optimized Docker images, CLI scripting, and automation for GPU instance provisioning.