fastvideo.v1.training.training_pipeline

`fastvideo.v1.training.training_pipeline`#

Module Contents#

Classes#

TrainingPipeline

A pipeline for training a model. All training pipelines should inherit from this class. All reusable components and code should be implemented in this class.

Data#

`logger`
`vsa_available`

API#

class fastvideo.v1.training.training_pipeline.TrainingPipeline(model_path: str, fastvideo_args: fastvideo.v1.fastvideo_args.TrainingArgs, required_config_modules: list[str] | None = None, loaded_modules: dict[str, torch.nn.Module] | None = None)[source]#

Bases: fastvideo.v1.pipelines.LoRAPipeline, abc.ABC

A pipeline for training a model. All training pipelines should inherit from this class. All reusable components and code should be implemented in this class.

Initialization

Initialize the pipeline. After init, the pipeline should be ready to use. The pipeline should be stateless and not hold any batch state.

create_pipeline_stages(fastvideo_args: fastvideo.v1.fastvideo_args.FastVideoArgs)[source]#

current_epoch: int[source]#: 0

initialize_training_pipeline(training_args: fastvideo.v1.fastvideo_args.TrainingArgs)[source]#

abstract initialize_validation_pipeline(training_args: fastvideo.v1.fastvideo_args.TrainingArgs)[source]#

set_schemas() → None[source]#

train() → None[source]#

train_dataloader: torchdata.stateful_dataloader.StatefulDataLoader[source]#: None

train_loader_iter: collections.abc.Iterator[dict[str, Any]][source]#: None

train_one_step(training_batch: fastvideo.v1.pipelines.TrainingBatch) → fastvideo.v1.pipelines.TrainingBatch[source]#

validation_pipeline: fastvideo.v1.pipelines.ComposedPipelineBase[source]#: None

fastvideo.v1.training.training_pipeline.logger[source]#: ‘init_logger(…)’

fastvideo.v1.training.training_pipeline.vsa_available[source]#: ‘is_vsa_available(…)’