Custom Dockerized inference workers on Together AI's managed GPU infrastructure. Build with Sprocket SDK, configure with Jig CLI, submit async queue jobs, and poll results. Reach for it whenever the user needs container-level control rather than a standard model endpoint or raw cluster.
インストール
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Custom Dockerized inference workers on Together AI's managed GPU infrastructure. Build with Sprocket SDK, configure with Jig CLI, submit async queue jobs, and poll results. Reach for it whenever the user needs container-level control rather than a standard model endpoint or raw cluster.
Together Dedicated Containers
Overview
Use Dedicated Container Inference when the user needs a custom runtime, not just managed model
hosting.
Core building blocks:
Jig CLI for build and deployment
Sprocket SDK for request handling inside the container
Queue API for async jobs
When This Skill Wins
Deploy a custom inference worker
Bundle custom dependencies or runtime logic into a container
Use queue-based async processing with progress tracking
Run a specialized image, video, or multimodal pipeline
Hand Off To Another Skill
Use together-dedicated-endpoints for standard model hosting without custom containers
Use together-gpu-clusters for full cluster ownership and orchestration control
Use together-chat-completions, together-images, or together-video when a serverless product already covers the task
Confirm that the user truly needs a custom container runtime.
Implement the worker with Sprocket's request lifecycle.
Configure pyproject.toml for image, runtime, autoscaling, and mounts.
Deploy with Jig.
Submit jobs through the queue API and poll until completion.
High-Signal Rules
Python scripts require the Together v2 SDK (together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".
Prefer dedicated endpoints over containers unless the runtime or pipeline is genuinely custom.
Treat the worker contract and pyproject.toml as the source of truth for deployment behavior.
Parameterize deployment name, queue inputs, and resource sizing instead of hardcoding them.
Queue-based jobs are asynchronous by default; account for polling and result retrieval in client code.
