Source code for pytorch_lightning.accelerators.accelerator

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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from abc import ABC, abstractmethod
from typing import Any, Dict, Union

import torch

import pytorch_lightning as pl

[docs]class Accelerator(ABC): """The Accelerator Base Class. An Accelerator is meant to deal with one type of Hardware. Currently there are accelerators for: - CPU - GPU - TPU - IPU - HPU """
[docs] def setup_environment(self, root_device: torch.device) -> None: """Setup any processes or distributed connections. This is called before the LightningModule/DataModule setup hook which allows the user to access the accelerator environment before setup is complete. """
[docs] def setup(self, trainer: "pl.Trainer") -> None: """Setup plugins for the trainer fit and creates optimizers. Args: trainer: the trainer instance """
[docs] def get_device_stats(self, device: Union[str, torch.device]) -> Dict[str, Any]: """Get stats for a given device. Args: device: device for which to get stats Returns: Dictionary of device stats """ raise NotImplementedError
[docs] def teardown(self) -> None: """Clean up any state created by the accelerator."""
[docs] @staticmethod @abstractmethod def parse_devices(devices: Any) -> Any: """Accelerator device parsing logic."""
[docs] @staticmethod @abstractmethod def get_parallel_devices(devices: Any) -> Any: """Gets parallel devices for the Accelerator."""
[docs] @staticmethod @abstractmethod def auto_device_count() -> int: """Get the device count when set to auto."""
[docs] @staticmethod @abstractmethod def is_available() -> bool: """Detect if the hardware is available."""
@classmethod def register_accelerators(cls, accelerator_registry: Dict) -> None: pass

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