composer.core#

Modules

`composer.core.algorithm`	Base class for algorithms that improve a model's quality or efficiency.
`composer.core.callback`	Base module for callbacks.
`composer.core.data_spec`	Specifications for operating and training on data.
`composer.core.engine`	Engine is a coordinator for running algorithms and resolving ordering conflicts among them for composition.
`composer.core.evaluator`	A wrapper for a dataloader to include metrics that apply to a specific dataset.
`composer.core.event`	Training Loop Events.
`composer.core.precision`	Enum class for the numerical precision to be used by the model.
`composer.core.serializable`	Serialization interface used by checkpointing.
`composer.core.state`	The state of the trainer.
`composer.core.time`	Utilities to track training progress in terms of epochs, batches, samples, and tokens.
`composer.core.types`	Reference for common types used throughout the composer library.

Central components used by other modules.

Central parts of composer such as Engine, base class for critical components such as Algorithm and Callback and other useful functionality such as Logger and Timestamp are implemented under core.

Functions

`ensure_data_spec`	Ensures that the `dataloader` is a `DataSpec`.
`ensure_evaluator`	Ensure that `evaluator` is an `Evaluator`.
`ensure_time`	Ensure `maybe_time` is an instance of `Time`.

Classes

`Algorithm`	Base class for algorithms.
`Callback`	Base class for callbacks.
`DataSpec`	Specifications for operating and training on data.
`Engine`	Coordinator for running algorithms and resolving ordering conflicts among them for composition.
`Evaluator`	A wrapper for a dataloader to include metrics that apply to a specific dataset.
`Event`	Enum to represent training loop events.
`Precision`	Enum class for the numerical precision to be used by the model.
`State`	The state of the trainer.
`Time`	Time represents static durations of training time in terms of a `TimeUnit` enum.
`TimeUnit`	Enum class to represent units of time for the training process.
`Timestamp`	Timestamp represents a snapshot of the current training progress.
`Trace`	Record of an algorithm's execution.