- light_pfp_autogen.utils.check_md_log(log_file: Path) bool#
-
Check the MD log file.
If finished, return True.
If not finished, return False.
- light_pfp_autogen.utils.check_model_accuracy(model_id: str) Tuple[float, float, float]#
-
Check the accuracy of the model
Return the MAE of the model.
- light_pfp_autogen.utils.check_train_log(log_file: Path) bool#
-
Check the training log file and validate the previous training job finished successfully.
- light_pfp_autogen.utils.check_training_job_status(job_id: str) bool#
-
Check the status of the training job
If finished, return True.
If failed, return False.
- light_pfp_autogen.utils.count_num_atoms(datasets_list: List[Path]) int#
-
Count the number of atoms in the given list of datasets.
- Parameters
-
datasets_list (List[Path]) – A list of paths to the datasets.
- Returns
-
The total number of atoms in all the datasets.
- Return type
-
int
- light_pfp_autogen.utils.estimate_epoch(datasets_list: List[Path], training_time: float = 0.5) int#
-
Estimate the number of epochs based on the number of atoms in the dataset and
expected training time in hours.- Parameters
-
-
datasets_list (List[Path]) – The list of dataset files.
-
training_time (float, optional) – The expected training time in hours. Defaults to 0.5.
-
- light_pfp_autogen.utils.get_model_id_from_log(log_file: Path) str#
-
Get the model ID from the training log file.
- light_pfp_autogen.utils.md_log_statistic(log_file: Path) Tuple[int, int, int, int, int, float, float, float]#
- light_pfp_autogen.utils.submit_training_job(training_args: TrainConfig, datasets_list: List[Path], task_name: str) str#
-
Submit a training job.
