altametris.sara.core.base_trainer¶

Base trainer class for model training.

Provides a common interface for training ML models with support for: - Training and validation loops - Callback system integration - Device management - Progress tracking

Example

>>> class MyTrainer(BaseTrainer):
...     def train(self, dataset_config, epochs=100):
...         for epoch in range(epochs):
...             self.callback_manager.on_epoch_start(epoch=epoch)
...             # Training logic
...             self.callback_manager.on_epoch_end(epoch=epoch, metrics=metrics)

Attributes¶

logger

Classes¶

BaseTrainer

Abstract base class for model training.

Module Contents¶

altametris.sara.core.base_trainer.logger¶

class altametris.sara.core.base_trainer.BaseTrainer(model: altametris.sara.core.base_model.BaseModel, device: str = 'auto', callbacks: list[altametris.sara.core.base_callback.BaseCallback] | None = None)¶

Bases: abc.ABC

Abstract base class for model training.

Provides common training infrastructure: - Callback management for training lifecycle events - Device management - Training state tracking - Configuration validation

Parameters:

model – Model to train
device – Device to train on (“cpu”, “cuda”, “mps”, “auto”)
callbacks – List of callbacks for training events

Example

>>> model = MyModel()
>>> trainer = MyTrainer(model=model, device="cuda")
>>> trainer.train(dataset_config="data.yaml", epochs=100)

model¶

device = 'auto'¶

callback_manager¶

_is_training = False¶

_current_epoch = 0¶

abstract train(dataset_config: Any, epochs: int = 100, **kwargs: Any) → dict[str, Any]¶

Train the model.

Parameters:

dataset_config – Dataset configuration (path, dict, etc.)
epochs – Number of training epochs
**kwargs – Additional training arguments

Returns:

Training results and metrics

Raises:

TrainingError – If training fails

Note

Must be implemented by subclasses

abstract validate(dataset_config: Any, **kwargs: Any) → dict[str, Any]¶

Validate the model.

Parameters:

dataset_config – Validation dataset configuration
**kwargs – Additional validation arguments

Returns:

Validation metrics

Raises:

TrainingError – If validation fails

Note

Must be implemented by subclasses

add_callback(callback: altametris.sara.core.base_callback.BaseCallback) → None¶

Add a callback to the trainer.

Parameters:: callback – Callback instance

Example

>>> trainer.add_callback(MyCallback())

remove_callback(callback: altametris.sara.core.base_callback.BaseCallback) → None¶

Remove a callback from the trainer.

Parameters:: callback – Callback instance to remove

property is_training: bool¶: Check if currently training.

property current_epoch: int¶: Get current epoch number.

_validate_dataset_config(config: Any) → None¶

Validate dataset configuration.

Parameters:: config – Dataset configuration to validate
Raises:: TrainingError – If configuration is invalid

_validate_training_args(epochs: int, **kwargs: Any) → None¶

Validate training arguments.

Parameters:

epochs – Number of epochs
**kwargs – Additional arguments

Raises:

TrainingError – If arguments are invalid

save_checkpoint(path: pathlib.Path, epoch: int, metrics: dict[str, Any]) → None¶

Save training checkpoint.

Parameters:

path – Path to save checkpoint
epoch – Current epoch
metrics – Current metrics

Example

>>> trainer.save_checkpoint(
...     path=Path("checkpoints/epoch_10.pt"),
...     epoch=10,
...     metrics={"loss": 0.5}
... )

load_checkpoint(path: pathlib.Path) → dict[str, Any]¶

Load training checkpoint.

Parameters:: path – Path to checkpoint file
Returns:: Checkpoint data
Raises:: TrainingError – If checkpoint cannot be loaded

__repr__() → str¶: String representation of trainer.