pytorch_pfn_extras.runtime.BaseRuntime¶
- class pytorch_pfn_extras.runtime.BaseRuntime(device_spec, options)¶
A base class for collections of device-specific callback functions.
The function attributes of this class will be called from
ppe.toorppe.handler.Handler.ppe.runtime.runtime_registrystores the runtime classes and dispatches them by feeding the corresponding name string as an input.- Parameters
device_spec (torch.device or str) – The device that modules and tensors are transferred to.
options (dict) – A configuration dictionary that can be used from runtime method.
- Return type
None
- __init__(device_spec, options)¶
- Parameters
device_spec (Union[str, torch.device]) –
options (Dict[str, Any]) –
- Return type
None
Methods
__init__(device_spec, options)convert_batch(args)Transfers the given batch to the specific device.
eval_post_step(evaluator, module, batch_idx, …)The method called at the end of each evaluation.
eval_pre_step(evaluator, module, batch_idx, …)The method called at the beginning of each evaluation.
execute(code_block, batch)Method called by the CodeBlocks API to do device dependent execution.
initialize_module(module, loader_or_batch[, …])Initializes the module at the beginning of training or inference.
move_module(module)Transfers the module to the specific device.
move_tensor(tensor)Transfers the tensor to the specific device.
train_epoch_begin(module)Preprocess of each epoch.
train_epoch_end(module)Completion of each epoch.
train_post_step(trainer, module, batch_idx, …)Postprocess of each step.
train_pre_step(trainer, module, batch_idx, batch)Preprocess of each step.
train_validation_begin(module)The method called before each evaluation.
train_validation_end(module)The method called after each evaluation.