graphium.config

helper functions to load and convert config files

graphium.config._loader

get_accelerator(config_acc)

Get the accelerator from the config file, and ensure that they are consistant.

get_checkpoint_path(config)

Get the checkpoint path from a config file. If the path is a valid name or a valid path, return it. Otherwise, assume it refers to a file in the checkpointing dir.

load_architecture(config, in_dims)

Loading the architecture used for training. Parameters: config: The config file, with key architecture in_dims: Dictionary of the input dimensions for various Returns: architecture: The datamodule used to process and load the data

load_datamodule(config, accelerator_type)

Load the datamodule from the specified configurations at the key datamodule: args. If the accelerator is IPU, load the IPU options as well.

Parameters:

Name	Type	Description	Default
config	Union[DictConfig, Dict[str, Any]]	The config file, with key datamodule: args	required
accelerator_type	str	The accelerator type, e.g. "cpu", "gpu", "ipu"	required

Returns: datamodule: The datamodule used to process and load the data

load_metrics(config)

Loading the metrics to be tracked. Parameters: config: The config file, with key metrics Returns: metrics: A dictionary of all the metrics

load_mup(mup_base_path, predictor)

Load the base shapes for the mup, based either on a .ckpt or .yaml file. If .yaml, it should be generated by mup.save_base_shapes

load_predictor(config, model_class, model_kwargs, metrics, task_levels, accelerator_type, featurization=None, task_norms=None)

Defining the predictor module, which handles the training logic from lightning.LighningModule Parameters: model_class: The torch Module containing the main forward function accelerator_type: The accelerator type, e.g. "cpu", "gpu", "ipu" Returns: predictor: The predictor module

load_trainer(config, accelerator_type, date_time_suffix='')

Defining the pytorch-lightning Trainer module. Parameters: config: The config file, with key trainer accelerator_type: The accelerator type, e.g. "cpu", "gpu", "ipu" date_time_suffix: The date and time of the current run. To be used for logging. Returns: trainer: the trainer module

load_yaml_config(config_path, main_dir=None, unknown_args=None)

Load a YAML config file and return it as a dictionary. Also returns the anchors & and aliases * of the YAML file. Then, update the config with the unknown arguments. Finally, update the config with the config override file specified in constants.config_override.

Parameters:

Name	Type	Description	Default
config_path	Union[str, PathLike]	The path to the YAML config file	required
main_dir	Optional[Union[str, PathLike]]	The main directory of the project. If specified, the config override file will be loaded from this directory	None
unknown_args		The unknown arguments to update the config with, taken from argparse.parse_known_args	None

Returns:

Name	Type	Description
config	Dict[str, Any]	The config dictionary

merge_dicts(dict_a, dict_b, previous_dict_path='', on_exist='raise')

Recursively merges dict_b into dict_a. If a key is missing from dict_a, it is added from dict_b. If a key exists in both, an error is raised. dict_a is modified in-place.

Parameters:

Name	Type	Description	Default
dict_a	Dict[str, Any]	The dictionary to merge into. Modified in-place.	required
dict_b	Dict[str, Any]	The dictionary to merge from.	required
previous_dict_path	str	The key path of the parent dictionary,	''
on_exist	str	What to do if a key already exists in dict_a. Options are "raise", "overwrite", "ignore".	'raise'

Raises:

Type	Description
ValueError	If a key path already exists in dict_a.

save_params_to_wandb(logger, config, predictor, datamodule)

Save a few stuff to weights-and-biases WandB Parameters: logger: The object used to log the training. Usually WandbLogger config: The config file, with key trainer predictor: The predictor used to handle the train/val/test steps logic datamodule: The datamodule used to load the data into training

graphium.config.config_convert

recursive_config_reformating(configs)

For a given configuration file, convert all DictConfig to dict, all ListConfig to list, and all byte to str.

This helps avoid errors when dumping a yaml file.

graphium.config._load

load_config(name)

Load a default config file by its name.

Parameters:

Name	Type	Description	Default
name	str	name of the config to load.	required