trinity.trainer.trainer module

trinity.trainer.trainer module#

Trainer Class

class trinity.trainer.trainer.Trainer(config: Config)[source]#

Bases: object

Consume the experience and train the model.

__init__(config: Config) → None[source]#

prepare() → None[source]#: Prepare the trainer.

async train() → str[source]#: Train the model.

async train_step() → bool[source]#

Train one step.

Returns:: Whether to continue training.
Return type:: bool

need_sync() → bool[source]#: Whether to sync the model weight.

sync_weight() → None[source]#: Sync the model weight.

save_checkpoint(block_until_saved: bool = False) → None[source]#

async shutdown() → None[source]#

property train_step_num: int#: Get the current training step number.

is_alive() → bool[source]#: Check if the trainer is alive.

classmethod get_actor(config: Config)[source]#: Get a Ray actor for the trainer.

class trinity.trainer.trainer.TrainEngineWrapper[source]#

Bases: ABC

A wrapper class to wrap various training engines.

abstract prepare() → None[source]#: Do some preparation before training started.

abstract property train_step_num: int#: Get the current training step number.

abstract train_step(batch: Experiences) → Tuple[bool, Dict][source]#

Training one step.

Parameters:: batch (Experiences) – A batch of experiences to train.
Returns:: Whether to continue training. Dict: Metrics of the training step.
Return type:: bool

abstract save_checkpoint(block_until_saved: bool = False) → None[source]#: Save the checkpoint.

abstract sync_weight() → None[source]#: Sync the model weight.

abstract upload_state_dict() → None[source]#: Upload the state dict to Synchronizer.

abstract save_state_dict() → None[source]#: Only save the model state dict for Synchronizer.

trinity.trainer.trainer.get_trainer_wrapper(config: Config) → TrainEngineWrapper[source]#: Get a trainer wrapper.