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zainhas/skills_draft · Archived

together-dedicated-containers

Custom Dockerized inference workers on Together AI's managed GPU infrastructure.

First seen Feb 27, 2026

Installation

$ npx skills add zainhas/skills_draft --skill together-dedicated-containers
View on skills.sh ↗ GitHub repository ↗

Summary

  • 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.

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Also in this package

Other skills from zainhas/skills_draft · top by installs.

npx skills add zainhas/skills_draft
together-code-interpreter 33 together-images 15 together-dedicated-endpoints 14 together-audio 14 together-chat-completions 13 together-video 12 together-evaluations 11 together-fine-tuning 11 together-batch-inference 10 together-gpu-clusters 10

Browse all from zainhas/skills_draft

More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 2
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,235 B
  • docs SUMMARY.md 324 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 10 installs

SKILL.md

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

Quick Routing

  • Minimal worker template

- Start with [scripts/sprockethelloworld.py](scripts/sprockethelloworld.py) - Read [references/sprocket-sdk.md](references/sprocket-sdk.md)

  • Build, deploy, logs, queue, and secrets

- Read [references/jig-cli.md](references/jig-cli.md)

  • Queue submission and polling

- Start with [scripts/queueclient.py](scripts/queueclient.py) or [scripts/queueclient.ts](scripts/queueclient.ts)

Workflow

  1. Confirm that the user truly needs a custom container runtime.
  2. Implement the worker with Sprocket's request lifecycle.
  3. Configure pyproject.toml for image, runtime, autoscaling, and mounts.
  4. Deploy with Jig.
  5. 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.

Resource Map

  • Jig CLI: [references/jig-cli.md](references/jig-cli.md)
  • Sprocket SDK: [references/sprocket-sdk.md](references/sprocket-sdk.md)
  • Python queue client: [scripts/queueclient.py](scripts/queueclient.py)
  • TypeScript queue client: [scripts/queueclient.ts](scripts/queueclient.ts)
  • Worker template: [scripts/sprockethelloworld.py](scripts/sprockethelloworld.py)

Official Docs