mridc.core.conf package

Submodules

mridc.core.conf.base_config module

class mridc.core.conf.base_config.Config(name: Optional[str] = None)

Bases: object

Abstract mridc Configuration class.

name: Optional[str] = None

mridc.core.conf.dataloader module

class mridc.core.conf.dataloader.DataLoaderConfig(batch_size: int = '???', shuffle: bool = False, sampler: Optional[Any] = None, batch_sampler: Optional[Any] = None, num_workers: int = 0, collate_fn: Optional[Any] = None, pin_memory: bool = False, drop_last: bool = False, timeout: int = 0, worker_init_fn: Optional[Any] = None, multiprocessing_context: Optional[Any] = None)

Bases: object

Configuration of PyTorch DataLoader.

..note:

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader

batch_sampler: Optional[Any] = None

batch_size: int = '???'

collate_fn: Optional[Any] = None

drop_last: bool = False

multiprocessing_context: Optional[Any] = None

num_workers: int = 0

pin_memory: bool = False

sampler: Optional[Any] = None

shuffle: bool = False

timeout: int = 0

worker_init_fn: Optional[Any] = None

mridc.core.conf.hydra_runner module

mridc.core.conf.hydra_runner.hydra_runner(config_path: Optional[str] = '.', config_name: Optional[str] = None, schema: Optional[Any] = None) → Callable[[Callable[[Any], Any]], Any]

Decorator used for passing the Config paths to main function. Optionally registers a schema used for validation/providing default values.

Parameters

• config_path (Path to the config file.) –

• config_name (Name of the config file.) –

• schema (Schema used for validation/providing default values.) –

Return type

A decorator that passes the config paths to the main function.

mridc.core.conf.modelPT module

class mridc.core.conf.modelPT.HydraConfig(run: ~typing.Dict[str, ~typing.Any] = <factory>, job_logging: ~typing.Dict[str, ~typing.Any] = <factory>)

Bases: object

Configuration for the hydra framework.

job_logging: Dict[str, Any]

run: Dict[str, Any]

class mridc.core.conf.modelPT.MRIDCConfig(name: str = '???', model: ModelConfig = '???', trainer: TrainerConfig = TrainerConfig(logger=False, callbacks=None, default_root_dir=None, gradient_clip_val=0, num_nodes=1, gpus=None, auto_select_gpus=False, tpu_cores=None, enable_progress_bar=True, overfit_batches=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_epochs=1000, min_epochs=1, max_steps=-1, min_steps=None, limit_train_batches=1.0, limit_val_batches=1.0, limit_test_batches=1.0, val_check_interval=1.0, log_every_n_steps=1, accelerator='gpu', sync_batchnorm=False, precision=32, weights_save_path=None, num_sanity_val_steps=2, resume_from_checkpoint=None, profiler=None, benchmark=False, deterministic=False, auto_lr_find=False, replace_sampler_ddp=True, detect_anomaly=False, auto_scale_batch_size=False, amp_backend='native', amp_level=None, plugins=None, move_metrics_to_cpu=False, multiple_trainloader_mode='max_size_cycle', limit_predict_batches=1.0, gradient_clip_algorithm='norm', max_time=None, reload_dataloaders_every_n_epochs=0, ipus=None, devices=None, strategy='ddp', enable_checkpointing=False, enable_model_summary=True), exp_manager: Optional[Any] = ExpManagerConfig(explicit_log_dir=None, exp_dir=None, name=None, version=None, use_datetime_version=True, resume_if_exists=False, resume_past_end=False, resume_ignore_no_checkpoint=False, create_tensorboard_logger=True, summary_writer_kwargs=None, create_wandb_logger=False, wandb_logger_kwargs=None, create_checkpoint_callback=True, checkpoint_callback_params=CallbackParams(filepath=None, dirpath=None, filename=None, monitor='val_loss', verbose=True, save_last=True, save_top_k=3, save_weights_only=False, mode='min', every_n_epochs=1, prefix=None, postfix='.mridc', save_best_model=False, always_save_mridc=False, save_mridc_on_train_end=True, model_parallel_size=None), files_to_copy=None, log_step_timing=True, step_timing_kwargs=StepTimingParams(reduction='mean', sync_cuda=False, buffer_size=1), log_local_rank_0_only=False, log_global_rank_0_only=False, model_parallel_size=None), hydra: HydraConfig = HydraConfig(run={'dir': '.'}, job_logging={'root': {'handlers': None}}))

Bases: object

Configuration for the mridc framework.

exp_manager: Optional[Any] = ExpManagerConfig(explicit_log_dir=None, exp_dir=None, name=None, version=None, use_datetime_version=True, resume_if_exists=False, resume_past_end=False, resume_ignore_no_checkpoint=False, create_tensorboard_logger=True, summary_writer_kwargs=None, create_wandb_logger=False, wandb_logger_kwargs=None, create_checkpoint_callback=True, checkpoint_callback_params=CallbackParams(filepath=None, dirpath=None, filename=None, monitor='val_loss', verbose=True, save_last=True, save_top_k=3, save_weights_only=False, mode='min', every_n_epochs=1, prefix=None, postfix='.mridc', save_best_model=False, always_save_mridc=False, save_mridc_on_train_end=True, model_parallel_size=None), files_to_copy=None, log_step_timing=True, step_timing_kwargs=StepTimingParams(reduction='mean', sync_cuda=False, buffer_size=1), log_local_rank_0_only=False, log_global_rank_0_only=False, model_parallel_size=None)

hydra: HydraConfig = HydraConfig(run={'dir': '.'}, job_logging={'root': {'handlers': None}})

model: ModelConfig = '???'

name: str = '???'

trainer: TrainerConfig = TrainerConfig(logger=False, callbacks=None, default_root_dir=None, gradient_clip_val=0, num_nodes=1, gpus=None, auto_select_gpus=False, tpu_cores=None, enable_progress_bar=True, overfit_batches=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_epochs=1000, min_epochs=1, max_steps=-1, min_steps=None, limit_train_batches=1.0, limit_val_batches=1.0, limit_test_batches=1.0, val_check_interval=1.0, log_every_n_steps=1, accelerator='gpu', sync_batchnorm=False, precision=32, weights_save_path=None, num_sanity_val_steps=2, resume_from_checkpoint=None, profiler=None, benchmark=False, deterministic=False, auto_lr_find=False, replace_sampler_ddp=True, detect_anomaly=False, auto_scale_batch_size=False, amp_backend='native', amp_level=None, plugins=None, move_metrics_to_cpu=False, multiple_trainloader_mode='max_size_cycle', limit_predict_batches=1.0, gradient_clip_algorithm='norm', max_time=None, reload_dataloaders_every_n_epochs=0, ipus=None, devices=None, strategy='ddp', enable_checkpointing=False, enable_model_summary=True)

class mridc.core.conf.modelPT.ModelConfig(train_ds: Optional[DatasetConfig] = None, validation_ds: Optional[DatasetConfig] = None, test_ds: Optional[DatasetConfig] = None, optim: Optional[OptimConfig] = None)

Bases: object

Configuration for the model.

optim: Optional[OptimConfig] = None

test_ds: Optional[DatasetConfig] = None

train_ds: Optional[DatasetConfig] = None

validation_ds: Optional[DatasetConfig] = None

class mridc.core.conf.modelPT.ModelConfigBuilder(model_cfg: ModelConfig)

Bases: object

Builder for the ModelConfig class.

build() → ModelConfig

Validate config

set_optim(cfg: OptimizerParams, sched_cfg: Optional[SchedulerParams] = None)

Set the optimizer configuration.

set_test_ds(cfg: Optional[DatasetConfig] = None)

Set the test dataset configuration.

set_train_ds(cfg: Optional[DatasetConfig] = None)

Set the training dataset configuration.

set_validation_ds(cfg: Optional[DatasetConfig] = None)

Set the validation dataset configuration.

class mridc.core.conf.modelPT.OptimConfig(name: str = '???', sched: Optional[SchedConfig] = None)

Bases: object

Configuration for the optimizer.

name: str = '???'

sched: Optional[SchedConfig] = None

class mridc.core.conf.modelPT.SchedConfig(name: str = '???', min_lr: float = 0.0, last_epoch: int = -1)

Bases: object

Configuration for the scheduler.

last_epoch: int = -1

min_lr: float = 0.0

name: str = '???'

mridc.core.conf.optimizers module

class mridc.core.conf.optimizers.AdadeltaParams(lr: Optional[float] = '???', rho: float = 0.9, eps: float = 1e-06, weight_decay: float = 0)

Bases: OptimizerParams

Default configuration for Adadelta optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html#torch.optim.Adadelta

eps: float = 1e-06

rho: float = 0.9

weight_decay: float = 0

class mridc.core.conf.optimizers.AdagradParams(lr: Optional[float] = '???', lr_decay: float = 0, weight_decay: float = 0, initial_accumulator_value: float = 0, eps: float = 1e-10)

Bases: OptimizerParams

Default configuration for Adagrad optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html#torch.optim.Adagrad

eps: float = 1e-10

initial_accumulator_value: float = 0

lr_decay: float = 0

weight_decay: float = 0

class mridc.core.conf.optimizers.AdamParams(lr: Optional[float] = '???', eps: float = 1e-08, weight_decay: float = 0, amsgrad: bool = False)

Bases: OptimizerParams

Default configuration for Adam optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam

amsgrad: bool = False

eps: float = 1e-08

weight_decay: float = 0

class mridc.core.conf.optimizers.AdamWParams(lr: Optional[float] = '???', betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-08, weight_decay: float = 0, amsgrad: bool = False)

Bases: OptimizerParams

Default configuration for AdamW optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html#torch.optim.AdamW

amsgrad: bool = False

betas: Tuple[float, float] = (0.9, 0.999)

eps: float = 1e-08

weight_decay: float = 0

class mridc.core.conf.optimizers.AdamaxParams(lr: Optional[float] = '???', betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-08, weight_decay: float = 0)

Bases: OptimizerParams

Default configuration for Adamax optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html#torch.optim.Adamax

betas: Tuple[float, float] = (0.9, 0.999)

eps: float = 1e-08

weight_decay: float = 0

class mridc.core.conf.optimizers.NovogradParams(lr: float = 0.001, betas: Tuple[float, float] = (0.95, 0.98), eps: float = 1e-08, weight_decay: float = 0, grad_averaging: bool = False, amsgrad: bool = False, luc: bool = False, luc_trust: float = 0.001, luc_eps: float = 1e-08)

Bases: OptimizerParams

Configuration of the Novograd optimizer. It has been proposed in “Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks” (https://arxiv.org/abs/1905.11286). The OptimizerParams is a Base Optimizer params with no values. User can choose to explicitly override it via command line arguments.

amsgrad: bool = False

betas: Tuple[float, float] = (0.95, 0.98)

eps: float = 1e-08

grad_averaging: bool = False

lr: float = 0.001

luc: bool = False

luc_eps: float = 1e-08

luc_trust: float = 0.001

weight_decay: float = 0

class mridc.core.conf.optimizers.OptimizerParams(lr: Optional[float] = '???')

Bases: object

Base Optimizer params with no values. User can chose it to explicitly override via command line arguments.

lr: Optional[float] = '???'

class mridc.core.conf.optimizers.RMSpropParams(lr: Optional[float] = '???', alpha: float = 0.99, eps: float = 1e-08, weight_decay: float = 0, momentum: float = 0, centered: bool = False)

Bases: OptimizerParams

Default configuration for RMSprop optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html#torch.optim.RMSprop

alpha: float = 0.99

centered: bool = False

eps: float = 1e-08

momentum: float = 0

weight_decay: float = 0

class mridc.core.conf.optimizers.RpropParams(lr: Optional[float] = '???', etas: Tuple[float, float] = (0.5, 1.2), step_sizes: Tuple[float, float] = (1e-06, 50))

Bases: OptimizerParams

Default configuration for RpropParams optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html#torch.optim.Rprop

etas: Tuple[float, float] = (0.5, 1.2)

step_sizes: Tuple[float, float] = (1e-06, 50)

class mridc.core.conf.optimizers.SGDParams(lr: Optional[float] = '???', momentum: float = 0, dampening: float = 0, weight_decay: float = 0, nesterov: bool = False)

Bases: OptimizerParams

Default configuration for Adam optimizer.

Note

For the details on the function/meanings of the arguments, please refer to: https://pytorch.org/docs/stable/optim.html?highlight=sgd#torch.optim.SGD

dampening: float = 0

momentum: float = 0

nesterov: bool = False

weight_decay: float = 0

mridc.core.conf.optimizers.get_optimizer_config(name: str, **kwargs: Optional[Dict[str, Any]]) → Union[Dict[str, Optional[Dict[str, Any]]], partial]

Convenience method to obtain a OptimizerParams class and partially instantiate it with optimizer kwargs.

Parameters

• name (Name of the OptimizerParams in the registry.) –

• kwargs (Optional kwargs of the optimizer used during instantiation.) –

Return type

A partially instantiated OptimizerParams.

mridc.core.conf.optimizers.register_optimizer_params(name: str, optimizer_params: OptimizerParams)

Checks if the optimizer param name exists in the registry, and if it doesn’t, adds it. This allows custom optimizer params to be added and called by name during instantiation.

Parameters

• name (Name of the optimizer. Will be used as key to retrieve the optimizer.) –

• optimizer_params (Optimizer class) –

mridc.core.conf.schedulers module

class mridc.core.conf.schedulers.CosineAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, constant_steps: Optional[float] = None, constant_ratio: Optional[float] = None, min_lr: float = 0.0)

Bases: WarmupAnnealingHoldSchedulerParams

Cosine Annealing parameter config

min_lr: float = 0.0

class mridc.core.conf.schedulers.CyclicLRParams(last_epoch: int = -1, base_lr: float = 0.001, max_lr: float = 0.1, step_size_up: int = 2000, step_size_down: Optional[int] = None, mode: str = 'triangular', gamma: float = 1.0, scale_mode: str = 'cycle', cycle_momentum: bool = True, base_momentum: float = 0.8, max_momentum: float = 0.9)

Bases: SchedulerParams

Config for CyclicLR.

base_lr: float = 0.001

base_momentum: float = 0.8

cycle_momentum: bool = True

gamma: float = 1.0

max_lr: float = 0.1

max_momentum: float = 0.9

mode: str = 'triangular'

scale_mode: str = 'cycle'

step_size_down: Optional[int] = None

step_size_up: int = 2000

class mridc.core.conf.schedulers.ExponentialLRParams(last_epoch: int = -1, gamma: float = 0.9)

Bases: SchedulerParams

Config for ExponentialLR.

gamma: float = 0.9

class mridc.core.conf.schedulers.InverseSquareRootAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None)

Bases: WarmupSchedulerParams

Inverse Square Root Annealing parameter config

class mridc.core.conf.schedulers.NoamAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, min_lr: float = 0.0)

Bases: WarmupSchedulerParams

Cosine Annealing parameter config

min_lr: float = 0.0

class mridc.core.conf.schedulers.NoamHoldAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, hold_steps: Optional[float] = None, hold_ratio: Optional[float] = None, min_lr: float = 0.0, decay_rate: float = 0.5)

Bases: WarmupHoldSchedulerParams

Polynomial Hold Decay Annealing parameter config. It is not derived from Config as it is not a MRIDC object (and in particular it doesn’t need a name).

decay_rate: float = 0.5

class mridc.core.conf.schedulers.PolynomialDecayAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, power: float = 1.0, cycle: bool = False)

Bases: WarmupSchedulerParams

Polynomial Decay Annealing parameter config

cycle: bool = False

power: float = 1.0

class mridc.core.conf.schedulers.PolynomialHoldDecayAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, power: float = 1.0, cycle: bool = False)

Bases: WarmupSchedulerParams

Polynomial Hold Decay Annealing parameter config

cycle: bool = False

power: float = 1.0

class mridc.core.conf.schedulers.ReduceLROnPlateauParams(mode: str = 'min', factor: float = 0.1, patience: int = 10, verbose: bool = False, threshold: float = 0.0001, threshold_mode: str = 'rel', cooldown: int = 0, min_lr: float = 0, eps: float = 1e-08)

Bases: object

Config for ReduceLROnPlateau.

cooldown: int = 0

eps: float = 1e-08

factor: float = 0.1

min_lr: float = 0

mode: str = 'min'

patience: int = 10

threshold: float = 0.0001

threshold_mode: str = 'rel'

verbose: bool = False

class mridc.core.conf.schedulers.SchedulerParams(last_epoch: int = -1)

Bases: object

Base configuration for all schedulers.

last_epoch: int = -1

class mridc.core.conf.schedulers.SquareAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, min_lr: float = 1e-05)

Bases: WarmupSchedulerParams

Square Annealing parameter config

min_lr: float = 1e-05

class mridc.core.conf.schedulers.SquareRootAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, min_lr: float = 0.0)

Bases: WarmupSchedulerParams

Square Root Annealing parameter config

min_lr: float = 0.0

class mridc.core.conf.schedulers.SquareRootConstantSchedulerParams(last_epoch: int = -1, constant_steps: Optional[float] = None, constant_ratio: Optional[float] = None)

Bases: SchedulerParams

Base configuration for all schedulers. It is not derived from Config as it is not a mridc object (and in particular it doesn’t need a name).

constant_ratio: Optional[float] = None

constant_steps: Optional[float] = None

class mridc.core.conf.schedulers.StepLRParams(last_epoch: int = -1, step_size: float = 0.1, gamma: float = 0.1)

Bases: SchedulerParams

Config for StepLR.

gamma: float = 0.1

step_size: float = 0.1

class mridc.core.conf.schedulers.WarmupAnnealingHoldSchedulerParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, constant_steps: Optional[float] = None, constant_ratio: Optional[float] = None, min_lr: float = 0.0)

Bases: WarmupSchedulerParams

Base configuration for all schedulers.

constant_ratio: Optional[float] = None

constant_steps: Optional[float] = None

min_lr: float = 0.0

class mridc.core.conf.schedulers.WarmupAnnealingParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None)

Bases: WarmupSchedulerParams

Warmup Annealing parameter config

warmup_ratio: Optional[float] = None

class mridc.core.conf.schedulers.WarmupHoldSchedulerParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None, hold_steps: Optional[float] = None, hold_ratio: Optional[float] = None, min_lr: float = 0.0)

Bases: WarmupSchedulerParams

Base configuration for all schedulers.

hold_ratio: Optional[float] = None

hold_steps: Optional[float] = None

min_lr: float = 0.0

class mridc.core.conf.schedulers.WarmupSchedulerParams(last_epoch: int = -1, max_steps: int = 0, warmup_steps: Optional[float] = None, warmup_ratio: Optional[float] = None)

Bases: SchedulerParams

Base configuration for all schedulers.

max_steps: int = 0

warmup_ratio: Optional[float] = None

warmup_steps: Optional[float] = None

mridc.core.conf.schedulers.get_scheduler_config(name: str, **kwargs: Optional[Dict[str, Any]]) → partial

Convenience method to obtain a SchedulerParams class and partially instantiate it with optimizer kwargs.

Parameters

• name (Name of the SchedulerParams in the registry.) –

• kwargs (Optional kwargs of the optimizer used during instantiation.) –

Return type

A partially instantiated SchedulerParams.

mridc.core.conf.schedulers.register_scheduler_params(name: str, scheduler_params: SchedulerParams)

Checks if the scheduler config name exists in the registry, and if it doesn’t, adds it. This allows custom schedulers to be added and called by name during instantiation.

Parameters

• name (Name of the optimizer. Will be used as key to retrieve the optimizer.) –

• scheduler_params (SchedulerParams class) –

mridc.core.conf.trainer module

class mridc.core.conf.trainer.TrainerConfig(logger: Any = True, callbacks: Optional[Any] = None, default_root_dir: Optional[str] = None, gradient_clip_val: float = 0, num_nodes: int = 1, gpus: Optional[Any] = None, auto_select_gpus: bool = False, tpu_cores: Optional[Any] = None, enable_progress_bar: bool = True, overfit_batches: Any = 0.0, track_grad_norm: Any = -1, check_val_every_n_epoch: int = 1, fast_dev_run: bool = False, accumulate_grad_batches: Any = 1, max_epochs: int = 1000, min_epochs: int = 1, max_steps: Optional[int] = -1, min_steps: Optional[int] = None, limit_train_batches: Any = 1.0, limit_val_batches: Any = 1.0, limit_test_batches: Any = 1.0, val_check_interval: Any = 1.0, log_every_n_steps: int = 50, accelerator: Optional[str] = None, sync_batchnorm: bool = False, precision: Any = 32, weights_save_path: Optional[str] = None, num_sanity_val_steps: int = 2, resume_from_checkpoint: Optional[str] = None, profiler: Optional[Any] = None, benchmark: bool = False, deterministic: bool = False, auto_lr_find: Any = False, replace_sampler_ddp: bool = True, detect_anomaly: bool = False, auto_scale_batch_size: Any = False, amp_backend: str = 'native', amp_level: Optional[str] = None, plugins: Optional[Any] = None, move_metrics_to_cpu: bool = False, multiple_trainloader_mode: str = 'max_size_cycle', limit_predict_batches: float = 1.0, gradient_clip_algorithm: str = 'norm', max_time: Optional[Any] = None, reload_dataloaders_every_n_epochs: int = 0, ipus: Optional[int] = None, devices: Optional[Any] = None, strategy: Optional[Any] = None, enable_checkpointing: bool = False, enable_model_summary: bool = True)

Bases: object

TrainerConfig is a dataclass that holds all the hyperparameters for the training process.

accelerator: Optional[str] = None

accumulate_grad_batches: Any = 1

amp_backend: str = 'native'

amp_level: Optional[str] = None

auto_lr_find: Any = False

auto_scale_batch_size: Any = False

auto_select_gpus: bool = False

benchmark: bool = False

callbacks: Optional[Any] = None

check_val_every_n_epoch: int = 1

default_root_dir: Optional[str] = None

detect_anomaly: bool = False

deterministic: bool = False

devices: Any = None

enable_checkpointing: bool = False

enable_model_summary: bool = True

enable_progress_bar: bool = True

fast_dev_run: bool = False

gpus: Optional[Any] = None

gradient_clip_algorithm: str = 'norm'

gradient_clip_val: float = 0

ipus: Optional[int] = None

limit_predict_batches: float = 1.0

limit_test_batches: Any = 1.0

limit_train_batches: Any = 1.0

limit_val_batches: Any = 1.0

log_every_n_steps: int = 50

logger: Any = True

max_epochs: int = 1000

max_steps: Optional[int] = -1

max_time: Optional[Any] = None

min_epochs: int = 1

min_steps: Optional[int] = None

move_metrics_to_cpu: bool = False

multiple_trainloader_mode: str = 'max_size_cycle'

num_nodes: int = 1

num_sanity_val_steps: int = 2

overfit_batches: Any = 0.0

plugins: Optional[Any] = None

precision: Any = 32

profiler: Optional[Any] = None

reload_dataloaders_every_n_epochs: int = 0

replace_sampler_ddp: bool = True

resume_from_checkpoint: Optional[str] = None

strategy: Any = None

sync_batchnorm: bool = False

tpu_cores: Optional[Any] = None

track_grad_norm: Any = -1

val_check_interval: Any = 1.0

weights_save_path: Optional[str] = None

Module contents