CLI lifecycle¶

The CLI exposes the main NexuML boundaries without requiring a project-specific training script. Use the generated CLI reference for the exact flags; this page explains when each command belongs in the workflow.

Inspect the environment¶

nexuml registry list scenarios
nexuml registry list layers
nexuml backend list

The registry shows discovered semantic components/scenarios. backend list shows runtime/export backends available in the current installation.

Resolve: Python → persisted configuration¶

nexuml resolve my-scenario

A registered Python scenario is evaluated and compiled, and its resolved configuration is written to configs/my-scenario.yaml by default.

Build: configuration → runtime pipeline¶

nexuml build configs/my-scenario.yaml

build restores typed definitions from YAML, materializes the pipeline, propagates shapes, and reports the compiled stages. When diagram output is enabled, the configured Mermaid file is written as part of the build.

Train: run the canonical lifecycle¶

nexuml train my-scenario

Training can also start from resolved YAML or a trusted scenario file; see Run scenarios.

The local NexuSession lifecycle is:

fit → validate → fit post-train pipeline layers → test

If the scenario contains ExportSpec(kind="train_package"), local training exports the trained package after the run. This is the simplest way to package the exact live trained model without locating a checkpoint manually.

Export an existing checkpoint¶

nexuml export my-scenario --checkpoint PATH -o packages/my-scenario

--checkpoint is optional syntactically, but omitting it does not search for the latest checkpoint. Supply the checkpoint explicitly when the command should package previously trained weights. See Model export.

Other workflows¶

nexuml export-dataset my-scenario -o exported-data
nexuml tune my-scenario --n-trials 20
nexuml smoke my-scenario --max-epochs 1

export-dataset persists raw or partially processed dataset views.
tune runs Optuna search from a registered scenario or trusted scenario file.
smoke exercises resolve → build → train → export → reload → inference for a registered scenario.