generate-npa-workflow

You can design physical AI pipelines with conditional loops and state graphs by creating declarative npa.workflow/v0.0.1 specifications. This framework provides a standardized recipe for defining complex multi-stage workloads and decision gates on Nebius infrastructure.

Validate your declarative workflow specification by running the npa workbench workflow validate-spec command. This automated CLI routine verifies spec structure, detects cycles, checks configuration bounds, and ensures graph integrity before execution.

An npa.workflow specification is used to declaratively orchestrate physical AI workloads on Nebius infrastructure. It structures complex state graphs, conditional loops, and tool-based integrations for multi-stage training processes like simulation-to-real loops.

Yes, you can architect a simulation-to-real training loop where a VLM critique stage evaluates whether to promote a model checkpoint or re-augment the dataset. This is achieved by defining conditional loops and decision gates in the workflow specification.

Workflow validation catches graph topology errors including invalid spec structures, undetected cycles, and breached configuration bounds. It ensures compliance with schema requirements to guarantee graph integrity before running the physical AI pipeline.

You need the npa CLI to orchestrate tool-based workflows for physical AI on Nebius infrastructure. The CLI executes the validation routines required to verify your declarative npa.workflow/v0.0.1 specification structure and graph integrity.