ertk.pytorch.train.PyTorchTrainConfig
- class ertk.pytorch.train.PyTorchTrainConfig(batch_size: int = 32, logging: ertk.pytorch.train.PyTorchLoggingConfig = PyTorchLoggingConfig(log_dir='', tensorboard=True, csv=True), n_gpus: int = 1, dist_strategy: str = 'dp', epochs: int = 50, wrapper_model_config: ertk.pytorch.models._base.PyTorchModelConfig = PyTorchModelConfig(optimiser='adam', opt_params={}, learning_rate=0.001, n_features=-1, n_classes=-1, loss='cross_entropy', loss_args={}), enable_checkpointing: bool = False, resume_checkpoint: str = '', save_every_n_epochs: int = 1, data_config: ertk.pytorch.train.PyTorchDataConfig = PyTorchDataConfig(train_data_processing={}, valid_data_processing={}, test_data_processing={}))
Bases:
ERTKConfig- __init__(batch_size: int = 32, logging: PyTorchLoggingConfig = PyTorchLoggingConfig(log_dir='', tensorboard=True, csv=True), n_gpus: int = 1, dist_strategy: str = 'dp', epochs: int = 50, wrapper_model_config: PyTorchModelConfig = PyTorchModelConfig(optimiser='adam', opt_params={}, learning_rate=0.001, n_features=-1, n_classes=-1, loss='cross_entropy', loss_args={}), enable_checkpointing: bool = False, resume_checkpoint: str = '', save_every_n_epochs: int = 1, data_config: PyTorchDataConfig = PyTorchDataConfig(train_data_processing={}, valid_data_processing={}, test_data_processing={})) None
Methods
__init__([batch_size, logging, n_gpus, ...])Inherited Methods
default()Create default config.
from_config(config)Create config object from any compatible config.
from_file(path)Create config from YAML file and optionlly override some values.
merge_with_args(args)Merge config with command-line arguments.
merge_with_config(config)Merge other config into this config.
to_dictconfig()Convert config to DictConfig.
to_file(path)Write config to YAML file.
to_string()Generate YAML string representation of config.
Attributes
batch_sizedata_configdist_strategyenable_checkpointingepochsloggingn_gpusresume_checkpointsave_every_n_epochswrapper_model_config