ride.optimizers

Modules adding optimizers

Module Contents

Classes

SgdOptimizer	Abstract base-class for Optimizer mixins
AdamWOptimizer	Abstract base-class for Optimizer mixins
SgdReduceLrOnPlateauOptimizer	Abstract base-class for Optimizer mixins
AdamWReduceLrOnPlateauOptimizer	Abstract base-class for Optimizer mixins
SgdCyclicLrOptimizer	Abstract base-class for Optimizer mixins
AdamWCyclicLrOptimizer	Abstract base-class for Optimizer mixins
SgdOneCycleOptimizer	Abstract base-class for Optimizer mixins
AdamWOneCycleOptimizer	Abstract base-class for Optimizer mixins
SgdMultiStepLR	Abstract base-class for Optimizer mixins
AdamWMultiStepLR	Abstract base-class for Optimizer mixins

Functions

discounted_steps_per_epoch(base_steps, num_gpus, ...)
discriminative_lr_and_params(model, lr, ...)

ride.optimizers.discounted_steps_per_epoch(base_steps: int, num_gpus: int, accumulate_grad_batches: int)

class ride.optimizers.SgdOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.AdamWOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.SgdReduceLrOnPlateauOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.AdamWReduceLrOnPlateauOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.SgdCyclicLrOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

train_dataloader: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.AdamWCyclicLrOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

train_dataloader: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.SgdOneCycleOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

train_dataloader: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.AdamWOneCycleOptimizer(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

train_dataloader: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.SgdMultiStepLR(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

train_dataloader: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

class ride.optimizers.AdamWMultiStepLR(hparams: pytorch_lightning.utilities.parsing.AttributeDict, *args, **kwargs)

Bases: ride.core.OptimizerMixin

Abstract base-class for Optimizer mixins

hparams: Ellipsis

parameters: Callable

train_dataloader: Callable

validate_attributes()

static configs() → ride.core.Configs

configure_optimizers()

ride.optimizers.discriminative_lr_and_params(model: torch.nn.Module, lr: float, discriminative_lr_fraction: float)