Runners

• Callbacks
  • AutocastCallback
  • BaseCallback
  • CheckpointCallback
  • ComposedCallback
  • GitCallback
  • IntervalMixin
  • LRScaleCallback
  • LRScheduleCallback
  • LogCallback
  • MetricCallback
  • MonitorCallback
  • OptimizeCallback
  • ShadowCallback
  • TensorBoardCallback
• Strategies
  • BaseStrategy
  • CUDAStrategy
  • DDPStrategy
  • FSDPStrategy
• Utils
  • AverageETA
  • BaseETA
  • Datum
  • EMA_ETA
  • ETARegistry
  • PriorityQueue
  • RunnerHolderMixin

class todd.runners.BaseRunner

Bases: BuildPreHookMixin, StateDictMixin, Generic[T]

__init__(name, *args, strategy, callbacks, dataset, dataloader, work_dir, logger, load_from=None, auto_resume=False, **kwargs)

Parameters:

• name (str)

• strategy (BaseStrategy[T])

• callbacks (ComposedCallback[T])

• dataset (Dataset[Any])

• dataloader (DataLoader[Any])

• work_dir (Path)

• logger (Logger)

• load_from (str | None)

• auto_resume (bool)

Return type:

None

property auto_resume: bool

classmethod build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property callbacks: ComposedCallback[T]

classmethod callbacks_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property dataloader: DataLoader[Any]

classmethod dataloader_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property dataset: Dataset[Any]

classmethod dataset_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property iter_: int

abstract property iters: int

property load_from: str | None

load_state_dict(state_dict, *args, **kwargs)

Parameters:

state_dict (Mapping[str, Any])

Return type:

None

property logger: Logger

classmethod logger_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property model: T

classmethod model_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property name: str

run()

Return type:

dict[str, Any]

state_dict(*args, **kwargs)

Return type:

dict[str, Any]

property strategy: BaseStrategy[T]

classmethod strategy_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property work_dir: Path

classmethod work_dir_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

class todd.runners.CallbackRegistry

Bases: RunnerRegistry

data = {'AutocastCallback': <class 'todd.runners.callbacks.autocast.AutocastCallback'>, 'BaseCallback': <class 'todd.runners.callbacks.base.BaseCallback'>, 'CheckpointCallback': <class 'todd.runners.callbacks.checkpoint.CheckpointCallback'>, 'ComposedCallback': <class 'todd.runners.callbacks.composed.ComposedCallback'>, 'GitCallback': <class 'todd.runners.callbacks.git.GitCallback'>, 'LRScaleCallback': <class 'todd.runners.callbacks.lr.LRScaleCallback'>, 'LRScheduleCallback': <class 'todd.runners.callbacks.lr.LRScheduleCallback'>, 'LogCallback': <class 'todd.runners.callbacks.log.LogCallback'>, 'MetricCallback': <class 'todd.runners.callbacks.metric.MetricCallback'>, 'MonitorCallback': <class 'todd.runners.callbacks.monitor.MonitorCallback'>, 'OptimizeCallback': <class 'todd.runners.callbacks.optimize.OptimizeCallback'>, 'ShadowCallback': <class 'todd.runners.callbacks.shadow.ShadowCallback'>, 'TensorBoardCallback': <class 'todd.runners.callbacks.tensorboard.TensorBoardCallback'>}

class todd.runners.EpochBasedTrainer

Bases: Trainer[T]

__init__(*args, epochs, **kwargs)

Parameters:

epochs (int)

Return type:

None

property epochs: int

property iters: int

class todd.runners.IterBasedTrainer

Bases: Trainer[T]

__init__(*args, iters, **kwargs)

Parameters:

iters (int)

Return type:

None

property iters: int

class todd.runners.MetricRegistry

Bases: RunnerRegistry

data = {'AccuracyMetric': <class 'todd.runners.metrics.accuracy.AccuracyMetric'>, 'LossMetric': <class 'todd.runners.metrics.loss.LossMetric'>, 'ReadyMadeMetric': <class 'todd.runners.metrics.ready_made.ReadyMadeMetric'>}

class todd.runners.StrategyRegistry

Bases: RunnerRegistry

data = {'BaseStrategy': <class 'todd.runners.strategies.base.BaseStrategy'>, 'CUDAStrategy': <class 'todd.runners.strategies.cuda.CUDAStrategy'>, 'DDPStrategy': <class 'todd.runners.strategies.ddp.DDPStrategy'>, 'FSDPStrategy': <class 'todd.runners.strategies.fsdp.FSDPStrategy'>}

class todd.runners.Trainer

Bases: BaseRunner[T], ABC

__init__(*args, optimizer, **kwargs)

Parameters:

optimizer (Optimizer)

Return type:

None

classmethod build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

property epoch: int

property inner_iter: int

property iters_per_epoch: int

load_state_dict(state_dict, *args, **kwargs)

Parameters:

state_dict (Mapping[str, Any])

Return type:

None

property optimizer: Optimizer

classmethod optimizer_build_pre_hook(config, registry, item)

Parameters:

• config (Config)

• registry (RegistryMeta)

• item (Item)

Return type:

Config

state_dict(*args, **kwargs)

Return type:

dict[str, Any]

class todd.runners.Validator

Bases: BaseRunner[T]

property iters: int

run()

Return type:

dict[str, Any]

todd.runners.get_memo(memo, key)

Parameters:

• memo (dict[str, Any])

• key (str)

Return type:

dict[str, Any]