nexuml.cli.main¶
NexuML CLI — entry point for all pipeline operations.
scenario_resolve(
name: str = typer.Argument(help="Scenario name"),
output: Optional[Path] = typer.Option(
None, "--output", "-o", help="Output YAML path"
),
)
Resolve a named scenario to a YAML config file.
build(
config_path: Path = typer.Argument(
help="Path to resolved YAML config"
),
)
Compile and validate a pipeline from a resolved YAML config.
train_cmd(
scenario_name: Optional[str] = typer.Argument(
None, help="Scenario name"
),
config_path: Optional[Path] = typer.Option(
None, "--config", "-c", help="Config YAML path"
),
scenario_file: Optional[Path] = typer.Option(
None,
"--scenario-file",
help="Trusted Python file exposing scenario() -> ScenarioSpec",
),
artifact_dir: Optional[Path] = typer.Option(
None,
"--artifact-dir",
help="Optional directory for scenario-file provenance snapshots",
),
max_epochs: Optional[int] = typer.Option(
None, "--max-epochs", help="Override max epochs"
),
trainer_checkpoint: Optional[Path] = typer.Option(
None,
"--trainer-checkpoint",
help="Optional Lightning Trainer checkpoint to resume from.",
),
override: Optional[list[str]] = typer.Option(
None,
"--override",
"-O",
help="Override a scenario field: key.path=value. Repeatable.",
),
)
Train a pipeline from a scenario name or config file.
Raises:
| Type | Description |
|---|---|
Exit
|
If input validation fails or training encounters an error. |
export_dataset_cmd(
scenario_name: Optional[str] = typer.Argument(
None, help="Scenario name"
),
config_path: Optional[Path] = typer.Option(
None, "--config", "-c", help="Config YAML path"
),
output: str = typer.Option(
"exported_dataset",
"--output",
"-o",
help="Path or s3:// URI",
),
backend: str = typer.Option(
"numpy", "--backend", help="Dataset export backend"
),
split: list[str] | None = typer.Option(
None,
"--split",
help="Split to export. Repeat the option to export multiple splits.",
),
preprocess: bool = typer.Option(
False,
"--preprocess/--no-preprocess",
help="Run the compiled pipeline until the requested preprocessing keys exist.",
),
preprocess_until_key: list[str] | None = typer.Option(
None,
"--preprocess-until-key",
help="TensorDict x key marking the preprocessing boundary. Repeatable.",
),
x_key: list[str] | None = typer.Option(
None,
"--x-key",
help="x TensorDict keys to persist. Repeatable. Default: all.",
),
y_key: list[str] | None = typer.Option(
None,
"--y-key",
help="label TensorDict keys to persist. Repeatable. Default: all.",
),
include_labels: bool = typer.Option(
True,
"--labels/--no-labels",
help="Include labels from the batch y TensorDict.",
),
dtype: str | None = typer.Option(
None,
"--dtype",
help="Optional storage dtype passed to the export backend, e.g. float16.",
),
samples_per_shard: int = typer.Option(
256,
"--samples-per-shard",
help="Samples per WebDataset tar shard",
),
s3_endpoint_url: str | None = typer.Option(
None,
"--s3-endpoint-url",
help="Optional S3-compatible endpoint",
),
s3_region: str | None = typer.Option(
None, "--s3-region", help="Optional S3 region"
),
s3_profile: str | None = typer.Option(
None,
"--s3-profile",
help="Optional AWS credential profile name",
),
)
Export a dataset view to disk or S3 from a scenario or config.
Raises:
| Type | Description |
|---|---|
Exit
|
If preprocessing is enabled without --preprocess-until-key. |
export_cmd(
scenario_name: str = typer.Argument(
help="Scenario name to train and export"
),
output: Path = typer.Option(
Path("exported_model"),
"--output",
"-o",
help="Export directory",
),
checkpoint: Optional[Path] = typer.Option(
None,
"--checkpoint",
help="Optional checkpoint to export",
),
)
Export a trained pipeline to a portable model package.
smoke(
scenario_name: str = typer.Argument(
default="synthetic-linear-ae-reconstruction",
help="Scenario name",
),
max_epochs: int = typer.Option(
3, "--max-epochs", help="Training epochs"
),
download: bool = typer.Option(
False,
"--download",
help="Download datasets before training",
),
)
Run a full smoke-test: resolve → build → train → export → reload → infer.
tune_cmd(
scenario_name: Optional[str] = typer.Argument(
None, help="Scenario name"
),
scenario_file: Optional[Path] = typer.Option(
None,
"--scenario-file",
help="Trusted Python file exposing scenario() -> ScenarioSpec",
),
artifact_dir: Optional[Path] = typer.Option(
None,
"--artifact-dir",
help="Optional directory for scenario-file provenance snapshots",
),
n_trials: Optional[int] = typer.Option(
None, "--n-trials", help="Number of Optuna trials"
),
metric_key: Optional[str] = typer.Option(
None,
"--metric",
help="Metric to optimise (default: tuning_spec.metric_key or val/loss)",
),
direction: Optional[str] = typer.Option(
None,
"--direction",
help="Optuna direction: minimize or maximize (default: from tuning_spec)",
),
storage: Optional[str] = typer.Option(
None, "--storage", help="Optuna storage path"
),
prune: Optional[bool] = typer.Option(
None,
"--prune/--no-prune",
help="Enable Optuna pruning",
),
override: Optional[list[str]] = typer.Option(
None,
"--override",
"-O",
help="Override a scenario field: key.path=value. Repeatable.",
),
)
Tune a scenario's hyperparameters with Optuna using a default search space.
Raises:
| Type | Description |
|---|---|
Exit
|
If scenario input is ambiguous or loading fails. |
registry_list(
kind: str = typer.Argument(
"layers",
help="Kind to list: layers, data, scenarios, eval",
),
verbose: bool = typer.Option(
False,
"--verbose",
"-v",
help="Show full tracebacks for discovery errors",
),
)
List registered items by kind (layers, data, scenarios, eval).
Raises:
| Type | Description |
|---|---|
Exit
|
If kind is not one of the recognised values. |
backend_list(
category: Optional[str] = typer.Argument(
None,
help="Optional category filter, e.g. data-export, data-loader, training",
),
)
List available backend implementations.
Raises:
| Type | Description |
|---|---|
Exit
|
If category does not match any registered backends. |
library_add(
path: Path = typer.Argument(
...,
help="Path to local library root",
exists=True,
file_okay=False,
dir_okay=True,
),
)
Add a local library root path to the NexuML config.
library_delete(
path: Path = typer.Argument(
..., help="Path to local library root"
),
)
Remove a local library root path from the NexuML config.
Raises:
| Type | Description |
|---|---|
Exit
|
If the path is not currently configured as a library root. |