diff --git a/docs/examples/data/torchvision/README.rst b/docs/examples/data/torchvision/README.rst deleted file mode 100644 index b0512e7e..00000000 --- a/docs/examples/data/torchvision/README.rst +++ /dev/null @@ -1,364 +0,0 @@ -.. NOTE: This file is auto-generated from examples/data/torchvision/index.rst -.. This is done so this file can be easily viewed from the GitHub UI. -.. **DO NOT EDIT** - -Torchvision -=========== - - -**Prerequisites** - -Make sure to read the following sections of the documentation before using this -example: - -* `examples/frameworks/pytorch_setup `_ -* `examples/distributed/single_gpu `_ - -The full source code for this example is available on `the mila-docs GitHub -repository. -`_ - - -**job.sh** - -.. code:: diff - - # distributed/single_gpu/job.sh -> data/torchvision/job.sh - #!/bin/bash - #SBATCH --gpus-per-task=rtx8000:1 - #SBATCH --cpus-per-task=4 - #SBATCH --ntasks-per-node=1 - #SBATCH --mem=16G - -#SBATCH --time=00:15:00 - +#SBATCH --time=01:30:00 - +set -o errexit - - - # Echo time and hostname into log - echo "Date: $(date)" - echo "Hostname: $(hostname)" - - - # Ensure only anaconda/3 module loaded. - module --quiet purge - # This example uses Conda to manage package dependencies. - # See https://docs.mila.quebec/Userguide.html#conda for more information. - module load anaconda/3 - module load cuda/11.7 - - # Creating the environment for the first time: - # conda create -y -n pytorch python=3.9 pytorch torchvision torchaudio \ - -# pytorch-cuda=11.7 -c pytorch -c nvidia - +# pytorch-cuda=11.7 scipy -c pytorch -c nvidia - # Other conda packages: - # conda install -y -n pytorch -c conda-forge rich tqdm - - # Activate pre-existing environment. - conda activate pytorch - - - -# Stage dataset into $SLURM_TMPDIR - -mkdir -p $SLURM_TMPDIR/data - -cp /network/datasets/cifar10/cifar-10-python.tar.gz $SLURM_TMPDIR/data/ - -# General-purpose alternatives combining copy and unpack: - -# unzip /network/datasets/some/file.zip -d $SLURM_TMPDIR/data/ - -# tar -xf /network/datasets/some/file.tar -C $SLURM_TMPDIR/data/ - +# Prepare data for training - +mkdir -p "$SLURM_TMPDIR/data" - + - +# If SLURM_JOB_CPUS_PER_NODE is defined and not empty, use the value of - +# SLURM_JOB_CPUS_PER_NODE. Else, use 16 workers to prepare data - +: ${_DATA_PREP_WORKERS:=${SLURM_JOB_CPUS_PER_NODE:-16}} - + - +# Copy the dataset to $SLURM_TMPDIR so it is close to the GPUs for - +# faster training - +srun --ntasks=$SLURM_JOB_NUM_NODES --ntasks-per-node=1 \ - + time -p bash data.py "/network/datasets/inat" ${_DATA_PREP_WORKERS} - - - # Fixes issues with MIG-ed GPUs with versions of PyTorch < 2.0 - unset CUDA_VISIBLE_DEVICES - - # Execute Python script - -python main.py - +srun python main.py - - -**main.py** - -.. code:: diff - - # distributed/single_gpu/main.py -> data/torchvision/main.py - -"""Single-GPU training example.""" - +"""Torchvision training example.""" - import logging - import os - -from pathlib import Path - - import rich.logging - import torch - from torch import Tensor, nn - from torch.nn import functional as F - from torch.utils.data import DataLoader, random_split - from torchvision import transforms - -from torchvision.datasets import CIFAR10 - +from torchvision.datasets import INaturalist - from torchvision.models import resnet18 - from tqdm import tqdm - - - def main(): - - training_epochs = 10 - + training_epochs = 1 - learning_rate = 5e-4 - weight_decay = 1e-4 - - batch_size = 128 - + batch_size = 256 - - # Check that the GPU is available - assert torch.cuda.is_available() and torch.cuda.device_count() > 0 - device = torch.device("cuda", 0) - - # Setup logging (optional, but much better than using print statements) - logging.basicConfig( - level=logging.INFO, - handlers=[rich.logging.RichHandler(markup=True)], # Very pretty, uses the `rich` package. - ) - - logger = logging.getLogger(__name__) - - # Create a model and move it to the GPU. - - model = resnet18(num_classes=10) - + model = resnet18(num_classes=10000) - model.to(device=device) - - optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay) - - - # Setup CIFAR10 - + # Setup ImageNet - num_workers = get_num_workers() - - dataset_path = Path(os.environ.get("SLURM_TMPDIR", ".")) / "data" - - train_dataset, valid_dataset, test_dataset = make_datasets(str(dataset_path)) - + try: - + dataset_path = f"{os.environ['SLURM_TMPDIR']}/data" - + except KeyError: - + dataset_path = "../dataset" - + train_dataset, valid_dataset, test_dataset = make_datasets(dataset_path) - train_dataloader = DataLoader( - train_dataset, - batch_size=batch_size, - num_workers=num_workers, - shuffle=True, - ) - valid_dataloader = DataLoader( - valid_dataset, - batch_size=batch_size, - num_workers=num_workers, - shuffle=False, - ) - test_dataloader = DataLoader( # NOTE: Not used in this example. - test_dataset, - batch_size=batch_size, - num_workers=num_workers, - shuffle=False, - ) - - # Checkout the "checkpointing and preemption" example for more info! - logger.debug("Starting training from scratch.") - - for epoch in range(training_epochs): - logger.debug(f"Starting epoch {epoch}/{training_epochs}") - - - # Set the model in training mode (important for e.g. BatchNorm and Dropout layers) - + # Set the model in training mode (this is important for e.g. BatchNorm and Dropout layers) - model.train() - - # NOTE: using a progress bar from tqdm because it's nicer than using `print`. - progress_bar = tqdm( - total=len(train_dataloader), - desc=f"Train epoch {epoch}", - ) - - # Training loop - for batch in train_dataloader: - # Move the batch to the GPU before we pass it to the model - batch = tuple(item.to(device) for item in batch) - x, y = batch - - # Forward pass - logits: Tensor = model(x) - - loss = F.cross_entropy(logits, y) - - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # Calculate some metrics: - n_correct_predictions = logits.detach().argmax(-1).eq(y).sum() - n_samples = y.shape[0] - accuracy = n_correct_predictions / n_samples - - logger.debug(f"Accuracy: {accuracy.item():.2%}") - logger.debug(f"Average Loss: {loss.item()}") - - - # Advance the progress bar one step and update the progress bar text. - + # Advance the progress bar one step, and update the "postfix" () the progress bar. (nicer than just) - progress_bar.update(1) - progress_bar.set_postfix(loss=loss.item(), accuracy=accuracy.item()) - progress_bar.close() - - val_loss, val_accuracy = validation_loop(model, valid_dataloader, device) - logger.info(f"Epoch {epoch}: Val loss: {val_loss:.3f} accuracy: {val_accuracy:.2%}") - - print("Done!") - - - @torch.no_grad() - def validation_loop(model: nn.Module, dataloader: DataLoader, device: torch.device): - model.eval() - - total_loss = 0.0 - n_samples = 0 - correct_predictions = 0 - - for batch in dataloader: - batch = tuple(item.to(device) for item in batch) - x, y = batch - - logits: Tensor = model(x) - loss = F.cross_entropy(logits, y) - - batch_n_samples = x.shape[0] - batch_correct_predictions = logits.argmax(-1).eq(y).sum() - - total_loss += loss.item() - n_samples += batch_n_samples - correct_predictions += batch_correct_predictions - - accuracy = correct_predictions / n_samples - return total_loss, accuracy - - - def make_datasets( - dataset_path: str, - val_split: float = 0.1, - val_split_seed: int = 42, - ): - - """Returns the training, validation, and test splits for CIFAR10. - + """Returns the training, validation, and test splits for iNat. - - - NOTE: We don't use image transforms here for simplicity. - + NOTE: We use the same image transforms here for train/val/test just to keep things simple. - Having different transformations for train and validation would complicate things a bit. - Later examples will show how to do the train/val/test split properly when using transforms. - """ - - train_dataset = CIFAR10( - - root=dataset_path, transform=transforms.ToTensor(), download=True, train=True - + train_dataset = INaturalist( - + root=dataset_path, - + transform=transforms.Compose([ - + transforms.Resize(256), - + transforms.CenterCrop(224), - + transforms.ToTensor(), - + ]), - + version="2021_train" - ) - - test_dataset = CIFAR10( - - root=dataset_path, transform=transforms.ToTensor(), download=True, train=False - + test_dataset = INaturalist( - + root=dataset_path, - + transform=transforms.Compose([ - + transforms.Resize(256), - + transforms.CenterCrop(224), - + transforms.ToTensor(), - + ]), - + version="2021_valid" - ) - # Split the training dataset into a training and validation set. - - n_samples = len(train_dataset) - - n_valid = int(val_split * n_samples) - - n_train = n_samples - n_valid - train_dataset, valid_dataset = random_split( - - train_dataset, (n_train, n_valid), torch.Generator().manual_seed(val_split_seed) - + train_dataset, ((1 - val_split), val_split), torch.Generator().manual_seed(val_split_seed) - ) - return train_dataset, valid_dataset, test_dataset - - - def get_num_workers() -> int: - """Gets the optimal number of DatLoader workers to use in the current job.""" - if "SLURM_CPUS_PER_TASK" in os.environ: - return int(os.environ["SLURM_CPUS_PER_TASK"]) - if hasattr(os, "sched_getaffinity"): - return len(os.sched_getaffinity(0)) - return torch.multiprocessing.cpu_count() - - - if __name__ == "__main__": - main() - - -**data.py** - -.. code:: python - - """Make sure the data is available""" - import os - import shutil - import sys - import time - from multiprocessing import Pool - from pathlib import Path - - from torchvision.datasets import INaturalist - - - def link_file(src: Path, dest: Path) -> None: - src.symlink_to(dest) - - - def link_files(src: Path, dest: Path, workers: int = 4) -> None: - os.makedirs(dest, exist_ok=True) - with Pool(processes=workers) as pool: - for path, dnames, fnames in os.walk(str(src)): - rel_path = Path(path).relative_to(src) - fnames = map(lambda _f: rel_path / _f, fnames) - dnames = map(lambda _d: rel_path / _d, dnames) - for d in dnames: - os.makedirs(str(dest / d), exist_ok=True) - pool.starmap( - link_file, - [(src / _f, dest / _f) for _f in fnames] - ) - - - if __name__ == "__main__": - src = Path(sys.argv[1]) - workers = int(sys.argv[2]) - # Referencing $SLURM_TMPDIR here instead of job.sh makes sure that the - # environment variable will only be resolved on the worker node (i.e. not - # referencing the $SLURM_TMPDIR of the master node) - dest = Path(os.environ["SLURM_TMPDIR"]) / "dest" - - start_time = time.time() - - link_files(src, dest, workers) - - # Torchvision expects these names - shutil.move(dest / "train.tar.gz", dest / "2021_train.tgz") - shutil.move(dest / "val.tar.gz", dest / "2021_valid.tgz") - - INaturalist(root=dest, version="2021_train", download=True) - INaturalist(root=dest, version="2021_valid", download=True) - - seconds_spent = time.time() - start_time - - print(f"Prepared data in {seconds_spent/60:.2f}m") - - -**Running this example** - -.. code-block:: bash - - $ sbatch job.sh diff --git a/docs/examples/generate_diffs.sh b/docs/examples/generate_diffs.sh index ebf1f580..f22175c1 100755 --- a/docs/examples/generate_diffs.sh +++ b/docs/examples/generate_diffs.sh @@ -31,14 +31,13 @@ generate_diff distributed/single_gpu/main.py distributed/multi_gpu/main.py generate_diff distributed/multi_gpu/job.sh distributed/multi_node/job.sh generate_diff distributed/multi_gpu/main.py distributed/multi_node/main.py -# single_gpu -> torchvision -generate_diff distributed/single_gpu/job.sh data/torchvision/job.sh -generate_diff distributed/single_gpu/main.py data/torchvision/main.py - # single_gpu -> checkpointing generate_diff distributed/single_gpu/job.sh good_practices/checkpointing/job.sh generate_diff distributed/single_gpu/main.py good_practices/checkpointing/main.py +# single_gpu -> data +generate_diff distributed/single_gpu/job.sh good_practices/data/job.sh + # single_gpu -> hpo_with_orion generate_diff distributed/single_gpu/job.sh good_practices/hpo_with_orion/job.sh generate_diff distributed/single_gpu/main.py good_practices/hpo_with_orion/main.py diff --git a/docs/examples/good_practices/data/README.rst b/docs/examples/good_practices/data/README.rst new file mode 100644 index 00000000..7d603284 --- /dev/null +++ b/docs/examples/good_practices/data/README.rst @@ -0,0 +1,342 @@ +.. NOTE: This file is auto-generated from examples/good_practices/data/index.rst +.. This is done so this file can be easily viewed from the GitHub UI. +.. **DO NOT EDIT** + +Data +==== + + +**Prerequisites** + +Make sure to read the following sections of the documentation before using this +example: + +* `examples/frameworks/pytorch_setup `_ +* `examples/distributed/single_gpu `_ + +The full source code for this example is available on `the mila-docs GitHub +repository. +`_ + + +**job.sh** + +.. code:: diff + + # distributed/single_gpu/job.sh -> good_practices/data/job.sh + #!/bin/bash + #SBATCH --gpus-per-task=rtx8000:1 + #SBATCH --cpus-per-task=4 + #SBATCH --ntasks-per-node=1 + #SBATCH --mem=16G + -#SBATCH --time=00:15:00 + +#SBATCH --time=01:30:00 + +set -o errexit + + + # Echo time and hostname into log + echo "Date: $(date)" + echo "Hostname: $(hostname)" + + + # Ensure only anaconda/3 module loaded. + module --quiet purge + # This example uses Conda to manage package dependencies. + # See https://docs.mila.quebec/Userguide.html#conda for more information. + module load anaconda/3 + module load cuda/11.7 + + # Creating the environment for the first time: + # conda create -y -n pytorch python=3.9 pytorch torchvision torchaudio \ + -# pytorch-cuda=11.7 -c pytorch -c nvidia + +# pytorch-cuda=11.7 scipy -c pytorch -c nvidia + # Other conda packages: + # conda install -y -n pytorch -c conda-forge rich tqdm + + # Activate pre-existing environment. + conda activate pytorch + + + -# Stage dataset into $SLURM_TMPDIR + -mkdir -p $SLURM_TMPDIR/data + -cp /network/datasets/cifar10/cifar-10-python.tar.gz $SLURM_TMPDIR/data/ + -# General-purpose alternatives combining copy and unpack: + -# unzip /network/datasets/some/file.zip -d $SLURM_TMPDIR/data/ + -# tar -xf /network/datasets/some/file.tar -C $SLURM_TMPDIR/data/ + +# Prepare data for training + +mkdir -p "$SLURM_TMPDIR/data" + + + +# If SLURM_JOB_CPUS_PER_NODE is defined and not empty, use the value of + +# SLURM_JOB_CPUS_PER_NODE. Else, use 16 workers to prepare data + +: ${_DATA_PREP_WORKERS:=${SLURM_JOB_CPUS_PER_NODE:-16}} + + + +# Copy the dataset to $SLURM_TMPDIR so it is close to the GPUs for + +# faster training + +srun --ntasks=$SLURM_JOB_NUM_NODES --ntasks-per-node=1 \ + + time -p python data.py "/network/datasets/inat" ${_DATA_PREP_WORKERS} + + + # Fixes issues with MIG-ed GPUs with versions of PyTorch < 2.0 + unset CUDA_VISIBLE_DEVICES + + # Execute Python script + -python main.py + +srun python main.py + + +**main.py** + +.. code:: python + + """Data example.""" + import logging + import os + + import rich.logging + import torch + from torch import Tensor, nn + from torch.nn import functional as F + from torch.utils.data import DataLoader, random_split + from torchvision import transforms + from torchvision.datasets import INaturalist + from torchvision.models import resnet18 + from tqdm import tqdm + + + def main(): + training_epochs = 1 + learning_rate = 5e-4 + weight_decay = 1e-4 + batch_size = 256 + + # Check that the GPU is available + assert torch.cuda.is_available() and torch.cuda.device_count() > 0 + device = torch.device("cuda", 0) + + # Setup logging (optional, but much better than using print statements) + logging.basicConfig( + level=logging.INFO, + handlers=[rich.logging.RichHandler(markup=True)], # Very pretty, uses the `rich` package. + ) + + logger = logging.getLogger(__name__) + + # Create a model and move it to the GPU. + model = resnet18(num_classes=10000) + model.to(device=device) + + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay) + + # Setup ImageNet + num_workers = get_num_workers() + try: + dataset_path = f"{os.environ['SLURM_TMPDIR']}/data" + except KeyError: + dataset_path = "../dataset" + train_dataset, valid_dataset, test_dataset = make_datasets(dataset_path) + train_dataloader = DataLoader( + train_dataset, + batch_size=batch_size, + num_workers=num_workers, + shuffle=True, + ) + valid_dataloader = DataLoader( + valid_dataset, + batch_size=batch_size, + num_workers=num_workers, + shuffle=False, + ) + test_dataloader = DataLoader( # NOTE: Not used in this example. + test_dataset, + batch_size=batch_size, + num_workers=num_workers, + shuffle=False, + ) + + # Checkout the "checkpointing and preemption" example for more info! + logger.debug("Starting training from scratch.") + + for epoch in range(training_epochs): + logger.debug(f"Starting epoch {epoch}/{training_epochs}") + + # Set the model in training mode (this is important for e.g. BatchNorm and Dropout layers) + model.train() + + # NOTE: using a progress bar from tqdm because it's nicer than using `print`. + progress_bar = tqdm( + total=len(train_dataloader), + desc=f"Train epoch {epoch}", + ) + + # Training loop + for batch in train_dataloader: + # Move the batch to the GPU before we pass it to the model + batch = tuple(item.to(device) for item in batch) + x, y = batch + + # Forward pass + logits: Tensor = model(x) + + loss = F.cross_entropy(logits, y) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # Calculate some metrics: + n_correct_predictions = logits.detach().argmax(-1).eq(y).sum() + n_samples = y.shape[0] + accuracy = n_correct_predictions / n_samples + + logger.debug(f"Accuracy: {accuracy.item():.2%}") + logger.debug(f"Average Loss: {loss.item()}") + + # Advance the progress bar one step, and update the "postfix" () the progress bar. (nicer than just) + progress_bar.update(1) + progress_bar.set_postfix(loss=loss.item(), accuracy=accuracy.item()) + progress_bar.close() + + val_loss, val_accuracy = validation_loop(model, valid_dataloader, device) + logger.info(f"Epoch {epoch}: Val loss: {val_loss:.3f} accuracy: {val_accuracy:.2%}") + + print("Done!") + + + @torch.no_grad() + def validation_loop(model: nn.Module, dataloader: DataLoader, device: torch.device): + model.eval() + + total_loss = 0.0 + n_samples = 0 + correct_predictions = 0 + + for batch in dataloader: + batch = tuple(item.to(device) for item in batch) + x, y = batch + + logits: Tensor = model(x) + loss = F.cross_entropy(logits, y) + + batch_n_samples = x.shape[0] + batch_correct_predictions = logits.argmax(-1).eq(y).sum() + + total_loss += loss.item() + n_samples += batch_n_samples + correct_predictions += batch_correct_predictions + + accuracy = correct_predictions / n_samples + return total_loss, accuracy + + + def make_datasets( + dataset_path: str, + val_split: float = 0.1, + val_split_seed: int = 42, + ): + """Returns the training, validation, and test splits for iNat. + + NOTE: We use the same image transforms here for train/val/test just to keep things simple. + Having different transformations for train and validation would complicate things a bit. + Later examples will show how to do the train/val/test split properly when using transforms. + """ + train_dataset = INaturalist( + root=dataset_path, + transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + ]), + version="2021_train" + ) + test_dataset = INaturalist( + root=dataset_path, + transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + ]), + version="2021_valid" + ) + # Split the training dataset into a training and validation set. + train_dataset, valid_dataset = random_split( + train_dataset, ((1 - val_split), val_split), torch.Generator().manual_seed(val_split_seed) + ) + return train_dataset, valid_dataset, test_dataset + + + def get_num_workers() -> int: + """Gets the optimal number of DatLoader workers to use in the current job.""" + if "SLURM_CPUS_PER_TASK" in os.environ: + return int(os.environ["SLURM_CPUS_PER_TASK"]) + if hasattr(os, "sched_getaffinity"): + return len(os.sched_getaffinity(0)) + return torch.multiprocessing.cpu_count() + + + if __name__ == "__main__": + main() + + +**data.py** + +.. code:: python + + """Make sure the data is available""" + import os + import shutil + import sys + import time + from multiprocessing import Pool + from pathlib import Path + + from torchvision.datasets import INaturalist + + + def link_file(src: Path, dest: Path) -> None: + src.symlink_to(dest) + + + def link_files(src: Path, dest: Path, workers: int = 4) -> None: + os.makedirs(dest, exist_ok=True) + with Pool(processes=workers) as pool: + for path, dnames, fnames in os.walk(str(src)): + rel_path = Path(path).relative_to(src) + fnames = map(lambda _f: rel_path / _f, fnames) + dnames = map(lambda _d: rel_path / _d, dnames) + for d in dnames: + os.makedirs(str(dest / d), exist_ok=True) + pool.starmap( + link_file, + [(src / _f, dest / _f) for _f in fnames] + ) + + + if __name__ == "__main__": + src = Path(sys.argv[1]) + workers = int(sys.argv[2]) + # Referencing $SLURM_TMPDIR here instead of job.sh makes sure that the + # environment variable will only be resolved on the worker node (i.e. not + # referencing the $SLURM_TMPDIR of the master node) + dest = Path(os.environ["SLURM_TMPDIR"]) / "dest" + + start_time = time.time() + + link_files(src, dest, workers) + + # Torchvision expects these names + shutil.move(dest / "train.tar.gz", dest / "2021_train.tgz") + shutil.move(dest / "val.tar.gz", dest / "2021_valid.tgz") + + INaturalist(root=dest, version="2021_train", download=True) + INaturalist(root=dest, version="2021_valid", download=True) + + seconds_spent = time.time() - start_time + + print(f"Prepared data in {seconds_spent/60:.2f}m") + + +**Running this example** + +.. code-block:: bash + + $ sbatch job.sh diff --git a/docs/examples/data/torchvision/data.py b/docs/examples/good_practices/data/data.py similarity index 100% rename from docs/examples/data/torchvision/data.py rename to docs/examples/good_practices/data/data.py diff --git a/docs/examples/data/torchvision/index.rst b/docs/examples/good_practices/data/index.rst similarity index 68% rename from docs/examples/data/torchvision/index.rst rename to docs/examples/good_practices/data/index.rst index f144f6c0..5aa45af4 100644 --- a/docs/examples/data/torchvision/index.rst +++ b/docs/examples/good_practices/data/index.rst @@ -1,5 +1,5 @@ -Torchvision -=========== +Data +==== **Prerequisites** @@ -12,24 +12,24 @@ example: The full source code for this example is available on `the mila-docs GitHub repository. -`_ +`_ **job.sh** -.. literalinclude:: examples/data/torchvision/job.sh.diff +.. literalinclude:: examples/good_practices/data/job.sh.diff :language: diff **main.py** -.. literalinclude:: examples/data/torchvision/main.py.diff - :language: diff +.. literalinclude:: examples/good_practices/data/main.py + :language: python **data.py** -.. literalinclude:: examples/data/torchvision/data.py +.. literalinclude:: examples/good_practices/data/data.py :language: python diff --git a/docs/examples/data/torchvision/job.sh b/docs/examples/good_practices/data/job.sh similarity index 100% rename from docs/examples/data/torchvision/job.sh rename to docs/examples/good_practices/data/job.sh diff --git a/docs/examples/data/torchvision/main.py b/docs/examples/good_practices/data/main.py similarity index 99% rename from docs/examples/data/torchvision/main.py rename to docs/examples/good_practices/data/main.py index 4ed612f0..91fe5c68 100644 --- a/docs/examples/data/torchvision/main.py +++ b/docs/examples/good_practices/data/main.py @@ -1,4 +1,4 @@ -"""Torchvision training example.""" +"""Data example.""" import logging import os