together-dedicated-containers
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-endpointsfor standard model hosting without custom containers - Use
together-gpu-clustersfor full cluster ownership and orchestration control - Use
together-chat-completions,together-images, ortogether-videowhen a serverless product already covers the task
Quick Routing
- Minimal worker template
- Start with scripts/sprocket_hello_world.py
- Read references/sprocket-sdk.md
- Build, deploy, logs, queue, and secrets
- Queue submission and polling
- Start with scripts/queue_client.py or scripts/queue_client.ts
Workflow
- Confirm that the user truly needs a custom container runtime.
- Implement the worker with Sprocket's request lifecycle.
- Configure
pyproject.tomlfor 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.tomlas 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.
Resource Map
- Jig CLI: references/jig-cli.md
- Sprocket SDK: references/sprocket-sdk.md
- Python queue client: scripts/queue_client.py
- TypeScript queue client: scripts/queue_client.ts
- Worker template: scripts/sprocket_hello_world.py
Official Docs
More from zainhas/togetherai-skills
together-code-interpreter
Use this skill for Together AI Code Interpreter workflows: remote Python execution, session reuse, file uploads, data analysis, plots, and stateful notebook-like runs through the TCI API. Reach for it whenever the user wants managed remote Python execution on Together AI instead of local execution, raw clusters, or full model hosting.
33together-audio
Text-to-speech and speech-to-text via Together AI, including REST, streaming, and realtime WebSocket TTS, plus transcription, translation, diarization, timestamps, and live STT. Reach for it whenever the user needs audio in or audio out on Together AI rather than chat generation, image or video creation, or model training.
14together-images
Text-to-image generation and image editing via Together AI, including FLUX and Kontext models, LoRA-based styling, reference-image guidance, and local image downloads. Reach for it whenever the user wants to generate or edit images on Together AI rather than create videos or build text-only chat applications.
14together-chat-completions
Real-time and streaming text generation via Together AI's OpenAI-compatible chat/completions API, including multi-turn conversations, tool and function calling, structured JSON outputs, and reasoning models. Reach for it whenever the user wants to build or debug text generation on Together AI, unless they specifically need batch jobs, embeddings, fine-tuning, dedicated endpoints, dedicated containers, or GPU clusters.
13together-dedicated-endpoints
Single-tenant GPU endpoints on Together AI with autoscaling and no rate limits. Deploy fine-tuned or uploaded models, size hardware, and manage endpoint lifecycle. Reach for it whenever the user needs predictable always-on hosting rather than serverless inference, custom containers, or raw clusters.
13together-video
Text-to-video and image-to-video generation via Together AI, including keyframe control, model and dimension selection, asynchronous job polling, and video downloads. Reach for it whenever the user wants motion generation on Together AI rather than still-image generation or text-only inference.
12