[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

.github/CONTRIBUTING.md

@@ -182,7 +182,7 @@ We welcome any useful contribution! For your convenience here's a recommended wo
 1. Use tags in PR name for the following cases:
 
    - **\[blocked by #<number>\]** if your work is dependent on other PRs.
-   - **\[wip\]** when you start to re-edit your work, mark it so no one will accidentally merge it in meantime.
+   - **[wip]** when you start to re-edit your work, mark it so no one will accidentally merge it in meantime.
 
 ### Question & Answer
 

.github/workflows/README.md

@@ -16,7 +16,7 @@ Brief description of all our automation tools used for boosting development perf
 | .azure-pipelines/gpu-benchmarks.yml    | Run speed/memory benchmarks for parity with vanila PyTorch.                               | GPU         |
 | .github/workflows/ci-flagship-apps.yml | Run end-2-end tests with full applications, including deployment to the production cloud. | CPU         |
 | .github/workflows/ci-tests-pytorch.yml | Run all tests except for accelerator-specific, standalone and slow tests.                 | CPU         |
-| .github/workflows/tpu-tests.yml        | Run only TPU-specific tests. Requires that the PR title contains '\[TPU\]'                | TPU         |
+| .github/workflows/tpu-tests.yml        | Run only TPU-specific tests. Requires that the PR title contains '[TPU]'                  | TPU         |
 
 \* Each standalone test needs to be run in separate processes to avoid unwanted interactions between test cases.
 

examples/fabric/build_your_own_trainer/run.py

@@ -1,8 +1,9 @@
-import lightning as L
 import torch
 from torchmetrics.functional.classification.accuracy import accuracy
 from trainer import MyCustomTrainer
 
+import lightning as L
+
 
 class MNISTModule(L.LightningModule):
     def __init__(self) -> None:

examples/fabric/build_your_own_trainer/trainer.py

@@ -3,15 +3,16 @@
 from functools import partial
 from typing import Any, Literal, Optional, Union, cast
 
-import lightning as L
 import torch
+from lightning_utilities import apply_to_collection
+from tqdm import tqdm
+
+import lightning as L
 from lightning.fabric.accelerators import Accelerator
 from lightning.fabric.loggers import Logger
 from lightning.fabric.strategies import Strategy
 from lightning.fabric.wrappers import _unwrap_objects
 from lightning.pytorch.utilities.model_helpers import is_overridden
-from lightning_utilities import apply_to_collection
-from tqdm import tqdm
 
 
 class MyCustomTrainer:

examples/fabric/dcgan/train_fabric.py

@@ -16,9 +16,10 @@
 import torch.utils.data
 import torchvision.transforms as transforms
 import torchvision.utils
-from lightning.fabric import Fabric, seed_everything
 from torchvision.datasets import CelebA
 
+from lightning.fabric import Fabric, seed_everything
+
 # Root directory for dataset
 dataroot = "data/"
 # Number of workers for dataloader

examples/fabric/fp8_distributed_transformer/train.py

@@ -1,15 +1,16 @@
-import lightning as L
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from lightning.fabric.strategies import ModelParallelStrategy
-from lightning.pytorch.demos import Transformer, WikiText2
 from torch.distributed._composable.fsdp.fully_shard import fully_shard
 from torch.distributed.device_mesh import DeviceMesh
 from torch.utils.data import DataLoader
 from torchao.float8 import Float8LinearConfig, convert_to_float8_training
 from tqdm import tqdm
 
+import lightning as L
+from lightning.fabric.strategies import ModelParallelStrategy
+from lightning.pytorch.demos import Transformer, WikiText2
+
 
 def configure_model(model: nn.Module, device_mesh: DeviceMesh) -> nn.Module:
     float8_config = Float8LinearConfig(

examples/fabric/image_classifier/train_fabric.py

@@ -36,11 +36,12 @@
 import torch.nn.functional as F
 import torch.optim as optim
 import torchvision.transforms as T
-from lightning.fabric import Fabric, seed_everything
 from torch.optim.lr_scheduler import StepLR
 from torchmetrics.classification import Accuracy
 from torchvision.datasets import MNIST
 
+from lightning.fabric import Fabric, seed_everything
+
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "..", "Datasets")
 
 

examples/fabric/kfold_cv/train_fabric.py

@@ -20,12 +20,13 @@
 import torch.nn.functional as F
 import torch.optim as optim
 import torchvision.transforms as T
-from lightning.fabric import Fabric, seed_everything
 from sklearn import model_selection
 from torch.utils.data import DataLoader, SubsetRandomSampler
 from torchmetrics.classification import Accuracy
 from torchvision.datasets import MNIST
 
+from lightning.fabric import Fabric, seed_everything
+
 DATASETS_PATH = path.join(path.dirname(__file__), "..", "..", "..", "Datasets")
 
 

examples/fabric/language_model/train.py

@@ -1,9 +1,10 @@
-import lightning as L
 import torch
 import torch.nn.functional as F
-from lightning.pytorch.demos import Transformer, WikiText2
 from torch.utils.data import DataLoader, random_split
 
+import lightning as L
+from lightning.pytorch.demos import Transformer, WikiText2
+
 
 def main():
     L.seed_everything(42)

examples/fabric/meta_learning/train_fabric.py

@@ -18,6 +18,7 @@
 import cherry
 import learn2learn as l2l
 import torch
+
 from lightning.fabric import Fabric, seed_everything