Command-line Executables

nequip-train

usage: nequip-train [-h] [--equivariance-test] [--model-debug-mode] [--grad-anomaly-mode] [--log LOG] config

Train (or restart training of) a NequIP model.

positional arguments:

config YAML file configuring the model, dataset, and other options

optional arguments:

-h, --help

show this help message and exit

--equivariance-test

test the model’s equivariance before training

--model-debug-mode

enable model debug mode, which can sometimes give much more useful error messages at the cost of some speed. Do not use for production training!

--grad-anomaly-mode

enable PyTorch autograd anomaly mode to debug NaN gradients. Do not use for production training!

--log LOG

log file to store all the screen logging

nequip-evaluate

usage: nequip-evaluate [-h] [--train-dir TRAIN_DIR] [--model MODEL] [--dataset-config DATASET_CONFIG]
                    [--metrics-config METRICS_CONFIG] [--test-indexes TEST_INDEXES] [--batch-size BATCH_SIZE]
                    [--device DEVICE] [--output OUTPUT] [--log LOG]

Compute the error of a model on a test set using various metrics. The model, metrics, dataset, etc. can specified in individual YAML config files, or a training session can be indicated with --train-dir. In order of priority, the global settings (dtype, TensorFloat32, etc.) are taken from: (1) the model config (for a training session), (2) the dataset config (for a deployed model), or (3) the defaults. Prints only the final result in name = num format to stdout; all other information is ``logging.debug``ed to stderr. WARNING: Please note that results of CUDA models are rarely exactly reproducible, and that even CPU models can be nondeterministic.

optional arguments:

-h, --help

show this help message and exit

--train-dir TRAIN_DIR

Path to a working directory from a training session.

--model MODEL

A deployed or pickled NequIP model to load. If omitted, defaults to best_model.pth in train_dir.

--dataset-config DATASET_CONFIG

A YAML config file specifying the dataset to load test data from. If omitted, config.yaml in train_dir will be used

--metrics-config METRICS_CONFIG

A YAML config file specifying the metrics to compute. If omitted, config.yaml in train_dir will be used. If the config does not specify metrics_components, the default is to logging.debug MAEs and RMSEs for all fields given in the loss function. If the literal string None, no metrics will be computed.

--test-indexes TEST_INDEXES

Path to a file containing the indexes in the dataset that make up the test set. If omitted, all data frames not used as training or validation data in the training session train_dir will be used.

--batch-size BATCH_SIZE

Batch size to use. Larger is usually faster on GPU.

--device DEVICE

Device to run the model on. If not provided, defaults to CUDA if available and CPU otherwise.

--output OUTPUT

XYZ file to write out the test set and model predicted forces, energies, etc. to.

--log LOG

log file to store all the metrics and screen logging.debug

nequip-deploy

usage: nequip-deploy [-h] {info,build} ...

Deploy and view information about previously deployed NequIP models.

optional arguments:

-h, --help

show this help message and exit

commands:

{info,build}

info Get information from a deployed model file build Build a deployment model

nequip-deploy info

usage: nequip-deploy info [-h] model_path

positional arguments:

model_path Path to a deployed model file.

optional arguments:

-h, --help

show this help message and exit

nequip-deploy build

usage: nequip-deploy build [-h] train_dir out_file

positional arguments:

train_dir Path to a working directory from a training session. out_file Output file for deployed model.

optional arguments:

-h, --help

show this help message and exit

nequip-benchmark

usage: nequip-benchmark [-h] [--profile PROFILE] [--device DEVICE] [-n N] [--n-data N_DATA] [--timestep TIMESTEP]
                        config

Benchmark the approximate MD performance of a given model configuration / dataset pair.

positional arguments:

config configuration file

optional arguments:

-h, --help

show this help message and exit

--profile PROFILE

Profile instead of timing, creating and outputing a Chrome trace JSON to the given path.

--device DEVICE

Device to run the model on. If not provided, defaults to CUDA if available and CPU otherwise.

-n N

Number of trials.

--n-data N_DATA

Number of frames to use.

--timestep TIMESTEP

MD timestep for ns/day esimation, in fs. Defauts to 1fs.