Train a model¶

Train a registered scenario¶

nexuml train my-scenario

Train resolved YAML¶

nexuml train -c configs/my-scenario.yaml

Train a trusted local experiment¶

nexuml train --scenario-file experiment.py --artifact-dir artifacts/exp-001

See Run scenarios for when to use each source.

Common overrides¶

nexuml train my-scenario --max-epochs 20
nexuml train my-scenario -O training.lr=0.0001 -O training.precision=bf16-mixed

Overrides change the scenario used for that command. For reusable experiment changes, prefer expressing the value in the Python scenario and resolving a new configuration.

Resume the Lightning trainer¶

nexuml train my-scenario --trainer-checkpoint PATH

This is a full Lightning resume: model, optimizer/scheduler state, epoch/global step, and compatible callback state come from the trainer checkpoint.

Do not use CheckpointLoadSpec for the same purpose. CheckpointLoadSpec is the separate selective/pretrained-weight workflow described in Checkpoints.

Execution placement¶

The command stays the same when the scenario selects Ray execution:

nexuml train my-scenario

ScenarioSpec.execution controls whether the canonical session runs locally or through Ray. See Execution modes.

Outputs¶

NexuML does not require one hard-coded output directory layout for every project. Outputs are driven by the scenario:

checkpoint callbacks decide checkpoint creation and location;
LoggingSpec controls TensorBoard/MLflow/DVCLive/diagram outputs;
ExportSpec(kind="train_package") can package the live trained model after a local run;
--artifact-dir stores provenance for trusted scenario-file runs.

Use Environment roots for default root resolution and the relevant guide for each output type.

Exact options¶

The CLI is generated from the implementation. Use CLI reference instead of copied option tables.