Merge branch 'master' into fix_metric_reset

Author: ananyahjha93

docs/source/multi_gpu.rst

@@ -239,23 +239,6 @@ Note in particular the difference between `gpus=0`, `gpus=[0]` and `gpus="0"`.
     to be in "exclusive mode", such that only one process at a time can access them.
     For more details see the :ref:`Trainer guide <trainer>`.
 
-
-Remove CUDA flags
-^^^^^^^^^^^^^^^^^
-
-CUDA flags make certain GPUs visible to your script.
-Lightning sets these for you automatically, there's NO NEED to do this yourself.
-
-.. testcode::
-
-    # lightning will set according to what you give the trainer
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-    os.environ["CUDA_VISIBLE_DEVICES"] = "0"
-
-However, when using a cluster, Lightning will NOT set these flags (and you should not either).
-SLURM will set these for you.
-For more details see the :ref:`SLURM cluster guide <slurm>`.
-
 ----------
 
 Distributed modes

pl_examples/basic_examples/autoencoder.py

@@ -15,17 +15,16 @@
 from argparse import ArgumentParser
 
 import torch
-import torch.nn.functional as F
 from torch import nn
-from torch.utils.data import DataLoader
-from torch.utils.data import random_split
+import torch.nn.functional as F
+from torch.utils.data import DataLoader, random_split
 
+from pl_examples import cli_lightning_logo, TORCHVISION_AVAILABLE
 import pytorch_lightning as pl
-from pl_examples import TORCHVISION_AVAILABLE, cli_lightning_logo
 
 if TORCHVISION_AVAILABLE:
-    from torchvision.datasets.mnist import MNIST
     from torchvision import transforms
+    from torchvision.datasets.mnist import MNIST
 else:
     from tests.base.datasets import MNIST
 

pl_examples/basic_examples/backbone_image_classifier.py

@@ -18,12 +18,12 @@
 from torch.nn import functional as F
 from torch.utils.data import DataLoader, random_split
 
+from pl_examples import cli_lightning_logo, DATASETS_PATH, TORCHVISION_AVAILABLE
 import pytorch_lightning as pl
-from pl_examples import DATASETS_PATH, TORCHVISION_AVAILABLE, cli_lightning_logo
 
 if TORCHVISION_AVAILABLE:
-    from torchvision.datasets.mnist import MNIST
     from torchvision import transforms
+    from torchvision.datasets.mnist import MNIST
 else:
     from tests.base.datasets import MNIST
 

pl_examples/basic_examples/conv_sequential_example.py

@@ -20,16 +20,16 @@
 To run:
 python conv_model_sequential_example.py --accelerator ddp --gpus 4 --max_epochs 1  --batch_size 256 --use_ddp_sequential
 """
-import math
 from argparse import ArgumentParser
+import math
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 from pytorch_lightning import Trainer
 from pytorch_lightning.metrics.functional import accuracy
 from pytorch_lightning.plugins.ddp_sequential_plugin import DDPSequentialPlugin

pl_examples/basic_examples/dali_image_classifier.py

@@ -13,29 +13,29 @@
 # limitations under the License.
 from abc import ABC
 from argparse import ArgumentParser
+from distutils.version import LooseVersion
 from random import shuffle
 from warnings import warn
-from distutils.version import LooseVersion
 
 import numpy as np
 import torch
 from torch.nn import functional as F
 from torch.utils.data import random_split
 
+from pl_examples import cli_lightning_logo, DALI_AVAILABLE, TORCHVISION_AVAILABLE
 import pytorch_lightning as pl
-from pl_examples import TORCHVISION_AVAILABLE, DALI_AVAILABLE, cli_lightning_logo
 
 if TORCHVISION_AVAILABLE:
-    from torchvision.datasets.mnist import MNIST
     from torchvision import transforms
+    from torchvision.datasets.mnist import MNIST
 else:
     from tests.base.datasets import MNIST
 
 if DALI_AVAILABLE:
+    from nvidia.dali import __version__ as dali_version
     from nvidia.dali import ops
     from nvidia.dali.pipeline import Pipeline
     from nvidia.dali.plugin.pytorch import DALIClassificationIterator
-    from nvidia.dali import __version__ as dali_version
 
     NEW_DALI_API = LooseVersion(dali_version) >= LooseVersion('0.28.0')
     if NEW_DALI_API:

pl_examples/basic_examples/simple_image_classifier.py

@@ -18,9 +18,9 @@
 import torch
 from torch.nn import functional as F
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
 from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
+import pytorch_lightning as pl
 
 
 class LitClassifier(pl.LightningModule):

pl_examples/bug_report_model.py

@@ -20,11 +20,12 @@
 # --------------------------------------------
 # --------------------------------------------
 import os
+
 import torch
 from torch.utils.data import Dataset
 
 from pl_examples import cli_lightning_logo
-from pytorch_lightning import Trainer, LightningModule
+from pytorch_lightning import LightningModule, Trainer
 
 
 class RandomDataset(Dataset):

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -38,22 +38,21 @@
 from collections import OrderedDict
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Optional, Generator, Union
+from typing import Generator, Optional, Union
 
 import torch
-import torch.nn.functional as F
 from torch import optim
 from torch.nn import Module
+import torch.nn.functional as F
 from torch.optim.lr_scheduler import MultiStepLR
 from torch.optim.optimizer import Optimizer
 from torch.utils.data import DataLoader
-from torchvision import models
-from torchvision import transforms
+from torchvision import models, transforms
 from torchvision.datasets import ImageFolder
 from torchvision.datasets.utils import download_and_extract_archive
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 from pytorch_lightning import _logger as log
 
 BN_TYPES = (torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)

pl_examples/domain_templates/generative_adversarial_net.py

@@ -19,20 +19,20 @@
 
 tensorboard --logdir default
 """
-import os
 from argparse import ArgumentParser, Namespace
+import os
 
 import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F  # noqa
-import torchvision
-import torchvision.transforms as transforms
 from torch.utils.data import DataLoader
+import torchvision
 from torchvision.datasets import MNIST
+import torchvision.transforms as transforms
 
 from pl_examples import cli_lightning_logo
-from pytorch_lightning.core import LightningModule, LightningDataModule
+from pytorch_lightning.core import LightningDataModule, LightningModule
 from pytorch_lightning.trainer import Trainer
 
 

pl_examples/domain_templates/imagenet.py

@@ -30,8 +30,8 @@
     python imagenet.py --help
 
 """
-import os
 from argparse import ArgumentParser, Namespace
+import os
 
 import torch
 import torch.nn.functional as F
@@ -44,8 +44,8 @@
 import torchvision.models as models
 import torchvision.transforms as transforms
 
-import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
+import pytorch_lightning as pl
 from pytorch_lightning.core import LightningModule