Command-line reference

LANfactory installs six commands. The tables below describe the complete command-line surface; run COMMAND --help to inspect the version installed in your environment. Training commands consume LANfactory configuration files, export commands convert saved trainer artifacts, and Hub commands move reviewed artifacts to or from a Hugging Face repository.

jaxtrain

Train a JAX network. Either --training-data-folder or --data-generation-experiment-id must identify the training data.

Option	Required/default	Contract
--config-path PATH	bundled configuration	YAML training configuration
--training-data-folder PATH	unset	Training data directory; optional when an MLflow data-generation experiment is supplied
--network-id INTEGER	0	Network entry selected from the configuration
--dl-workers INTEGER	1	DataLoader worker count; non-positive values request automatic sizing
--networks-path-base PATH	required	Base directory for saved network artifacts
--dry-run	off	Validate configuration and data discovery without training
--export-onnx / --no-export-onnx	on	Export the HSSM-consumable ONNX artifact after training
--mlflow-run-name TEXT	unset	Enable tracking under this run name
--mlflow-experiment-name TEXT	MLFLOW_EXPERIMENT_NAME or unset	MLflow experiment name
--mlflow-run-id TEXT	unset	Resume an existing MLflow run
--data-generation-experiment-id TEXT	unset	Derive the data location and lineage from an MLflow experiment
--mlflow-tracking-uri TEXT	MLFLOW_TRACKING_URI or sqlite:///mlflow.db	MLflow tracking backend
--mlflow-artifact-location TEXT	MLFLOW_ARTIFACT_LOCATION or ./mlruns	MLflow artifact root
--log-level LEVEL, -l LEVEL	WARNING	Logging threshold

torchtrain

Train a PyTorch network. Its data-discovery and MLflow options match jaxtrain; PyTorch artifacts can be converted with transform-onnx.

Option	Required/default	Contract
--config-path PATH	bundled configuration	YAML training configuration
--training-data-folder PATH	unset	Training data directory; optional when an MLflow data-generation experiment is supplied
--networks-path-base PATH	required	Base directory for saved network artifacts
--network-id INTEGER	0	Network entry selected from the configuration
--dl-workers INTEGER	1	DataLoader worker count; non-positive values request automatic sizing
--dry-run	off	Validate configuration and data discovery without training
--mlflow-run-name TEXT	unset	Enable tracking under this run name
--mlflow-experiment-name TEXT	MLFLOW_EXPERIMENT_NAME or unset	MLflow experiment name
--mlflow-run-id TEXT	unset	Resume an existing MLflow run
--data-generation-experiment-id TEXT	unset	Derive the data location and lineage from an MLflow experiment
--mlflow-tracking-uri TEXT	MLFLOW_TRACKING_URI or sqlite:///mlflow.db	MLflow tracking backend
--mlflow-artifact-location TEXT	MLFLOW_ARTIFACT_LOCATION or ./mlruns	MLflow artifact root
--log-level LEVEL, -l LEVEL	WARNING	Logging threshold

transform-onnx

Convert a saved PyTorch TorchMLP configuration and state dictionary to ONNX.

Option	Required/default	Contract
--network-config-file TEXT	required	Pickled network configuration
--state-dict-file TEXT	required	Saved PyTorch state dictionary
--input-shape INTEGER	required	Concrete single-trial input width
--output-onnx-file TEXT	required	Destination ONNX file

transform-jax-onnx

Convert a saved jaxtrain network configuration and Flax state to ONNX.

Option	Required/default	Contract
--network-config-file TEXT	required	Pickled network configuration
--state-file TEXT	required	*_train_state.jax Flax parameter bytes
--input-shape INTEGER	required	Concrete single-trial input width
--output-onnx-file TEXT	required	Destination ONNX file
--opset INTEGER	17	Target ONNX opset

Both transform commands produce concrete-shape, single-trial artifacts. Rank is exporter-specific; HSSM owns the consumer contract and trial-wise vectorization. See the sbi and BayesFlow exporter references for the same cross-package boundary.

upload-hf

Publish a trained artifact set, its optional model card, canonical root alias, and manifest entry.

Option	Required/default	Contract
--model-folder PATH	required	Folder containing the trained artifacts; model_card.yaml is optional
--network-type TEXT	required	One of lan, cpn, opn, or gonogo
--model-name TEXT	required	Model identifier used in the folder and root filename
--repo-id TEXT	franklab/HSSM	Target Hub repository
--commit-message TEXT	Upload model	Hub commit message
--private	off	Create a private repository when creating the target
--create-repo	off	Create the target repository if absent
--include-patterns TEXT	unset	Comma-separated filename globs to include
--exclude-patterns TEXT	unset	Comma-separated filename globs to exclude
--revision TEXT	unset	Target branch or tag
--token TEXT	HF_TOKEN or unset	Explicit token or environment fallback
--dry-run	off	Print the publication plan without uploading or mutating files
--publish-root-alias / --no-publish-root-alias	on	Publish the canonical root filename consumed by HSSM
--update-manifest / --no-update-manifest	on	Read-modify-write the root manifest.json
--require-model-card	off	Reject a missing model_card.yaml instead of generating metadata
--canonical-onnx PATH	inferred	Select the ONNX file copied to the repository root
--overwrite-root	off	Permit replacement of an existing HSSM-facing root artifact
--log-level LEVEL, -l LEVEL	WARNING	Logging threshold

download-hf

Retrieve one {network-type}/{model-name}/ folder from a Hub repository.

Option	Required/default	Contract
--network-type TEXT	required	One of lan, cpn, opn, or gonogo
--model-name TEXT	required	Model folder to retrieve
--output-folder PATH	required	Local destination; must be absent unless --force is set
--repo-id TEXT	franklab/HSSM	Source Hub repository
--revision TEXT	unset (Hub default: main)	Branch, tag, or commit to retrieve
--include-patterns TEXT	unset	Comma-separated filename globs to include
--exclude-patterns TEXT	unset	Comma-separated filename globs to exclude
--token TEXT	HF_TOKEN or unset	Explicit token or environment fallback for private repositories
--force	off	Replace an existing destination
--log-level LEVEL, -l LEVEL	WARNING	Logging threshold

For task-oriented workflows, see Track training with MLflow and Share trained networks on Hugging Face Hub. The Python API reference documents the Hub helpers and constants called by the two Hub entry points.