| name | local-container-registry |
| description | Run a local Docker registry, publish reusable CUDA-capable ML base images, and handle ML container/runtime or local ML service setup issues. |
Skill: Local Container Registry
Goal
Use a reusable local registry + baked ML image workflow instead of repeatedly rebuilding CUDA/torch stacks.
Use This Skill When
- You need
localhost:5000/... images across repos.
- CUDA/torch libraries are missing in containers.
- You need a reusable ML toolbox image.
- You need to stand up a local ML service like embeddings inference or the Open Hax proxy/model gateway.
Steps
- Start/validate the registry.
- Prefer official CUDA-capable upstream images for the base.
- Build/tag/push a baked ML image.
- Point repo builds at
PROMPTBENCH_BASE_IMAGE.
- Use dedicated service images when the goal is a specific local ML API.
Example
docker compose -f ~/.config/local-container-registry/docker-compose.yml up -d
curl -sSf http://localhost:5000/v2/_catalog
docker build -f Dockerfile.ml-base \
-t localhost:5000/shibboleth/ml-base:cuda12.4-2026-03-18 \
.
docker push localhost:5000/shibboleth/ml-base:cuda12.4-2026-03-18
PROMPTBENCH_BASE_IMAGE=localhost:5000/shibboleth/ml-base:cuda12.4-2026-03-18 \
docker compose build