One directory, one serving intent
An inference project keeps the deployment specification beside the code and configuration that produce it. InferCrane discoversinfercrane.yaml from the current directory or a parent, validates
it locally, and uses the same file for planning and deployment.
workload init writes a valid DeploymentSpec with a JSON Schema directive understood by YAML
language servers in VS Code, JetBrains editors, and other schema-aware tools. Fields, enums, and
unknown keys are checked while you type. A curated recipe also pins the model commit. Recipes are
reviewed configuration starting points, not performance claims; use benchmark or replay evidence
for serving decisions.
Local validation before GPU allocation
plan is also non-mutating. This keeps an invalid container command or unsupported
runtime combination from becoming a paid provisioning attempt.
The versioned schema is published from
schemas/deployment-v1.schema.json.
The Go loader remains authoritative; the schema improves editing and never weakens server-side
validation.
Build a custom OCI workload
Custom OCI projects can build through Docker Buildx:Run a custom workload locally
127.0.0.1. The command is available for custom-oci
projects; vLLM and SGLang model projects use their qualified runtime adapters.
InferCrane owns the reproducible serving intent, not a new container build system. Docker/Buildx,
your registry, vLLM, SGLang, and custom OCI runtimes retain their established responsibilities.