v0.7.13

Native install (Apple Silicon / homelab)

uv tool install mship
mship bootstrap --metal
mship deploy --config models.yaml

Docker images

Thin (control/coordinator — default, no torch/vllm)

docker pull ghcr.io/modelship-ai/modelship:0.7.13

CUDA (GPU node)

docker pull ghcr.io/modelship-ai/modelship:0.7.13-cuda

CPU (CPU node)

docker pull ghcr.io/modelship-ai/modelship:0.7.13-cpu

Floating tags (:latest, :latest-cuda, :latest-cpu) are single-node only — for any
multi-node cluster, pin every node to the same X.Y.Z tag to avoid a Ray version
mismatch between head and workers.

Helm chart (Kubernetes)

helm install modelship \
  oci://ghcr.io/modelship-ai/charts/modelship \
  --version 0.7.13 \
  -f values.yaml

Running an image

The images take the same subcommands as the mship CLI:

docker run --rm --shm-size=8g \
  -v ./models.yaml:/modelship/config/models.yaml \
  -v ./models-cache:/.cache \
  -p 8000:8000 \
  ghcr.io/modelship-ai/modelship:0.7.13-cpu deploy

Configuration

Create a models.yaml file and mount it at /modelship/config/models.yaml. Example configs for various GPU sizes are included in the image under /modelship/config/examples/ — use them as a reference for structure and available options, then tailor the models and GPU fractions to your hardware.

What's Changed

• feat: split provisioning into mship bootstrap by @alez007 in #174
• Feat/docker native parity by @alez007 in #175
• feat: gate bootstrap --cuda on the CUDA toolchain, not the GPU by @alez007 in #176
• Refactor/ray free config schema by @alez007 in #177
• Fix/gateway route prefix by @alez007 in #178
• feat: deploy a single model from CLI flags instead of models.yaml by @alez007 in #179

Full Changelog: v0.7.12...v0.7.13