deepcausalmmm.utils.device
Device management utilities for DeepCausalMMM.
This module handles: - GPU/CPU device selection - Memory management - Mixed precision training - Multi-GPU support
Functions
Clear GPU memory cache. |
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Get Automatic Mixed Precision (AMP) settings. |
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Get the appropriate device for model training/inference. |
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Recursively move data to specified device. |
Classes
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Context manager for device management. |
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Null context manager for CPU fallback. |
- deepcausalmmm.utils.device.get_device(device: str | None = None) device[source]
Get the appropriate device for model training/inference.
- Parameters:
device – Device specification (‘auto’, ‘cpu’, ‘cuda’, ‘cuda:0’, etc.) If None or ‘auto’, will use CUDA if available
- Returns:
Selected device
- Return type:
- deepcausalmmm.utils.device.get_amp_settings(device: device, mixed_precision: bool = True) Tuple[GradScaler, bool][source]
Get Automatic Mixed Precision (AMP) settings.
- Parameters:
device – Current device
mixed_precision – Whether to enable mixed precision training
- Returns:
Tuple of (gradient scaler, use mixed precision flag)
- deepcausalmmm.utils.device.move_to_device(data: Tensor | dict | list | tuple, device: device) Tensor | dict | list | tuple[source]
Recursively move data to specified device.
- Parameters:
data – Data to move (can be tensor, dict, list, or tuple)
device – Target device
- Returns:
Data on target device
- class deepcausalmmm.utils.device.DeviceContext(device: str | None = None, mixed_precision: bool = True)[source]
Context manager for device management.
Example
- with DeviceContext(device=’auto’, mixed_precision=True) as ctx:
model = model.to(ctx.device) for batch in dataloader:
- with ctx.autocast():
output = model(batch)