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‎CHANGELOG.md‎

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@@ -8,6 +8,25 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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### Added
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### Changed
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### Removed
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### Fixed
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## [21.07] - 2021-07-29
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### Added
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- added `pre-commit` hook to run codestyle checker on commit ([#1257](https://github.com/catalyst-team/catalyst/pull/1257))
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- `on publish` github action for docker and docs added ([#1260](https://github.com/catalyst-team/catalyst/pull/1260))
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- MixupCallback and `utils.mixup_batch` ([#1241](https://github.com/catalyst-team/catalyst/pull/1241))

‎CONTRIBUTING.md‎

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@@ -109,7 +109,7 @@ We are using the Github CI for our test cases validation:
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- [unit tests](https://github.com/catalyst-team/catalyst/blob/master/.github/workflows/dl_cpu.yml#L113)
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- [integrations tests](https://github.com/catalyst-team/catalyst/blob/master/.github/workflows/dl_cpu.yml#L114#L117)
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We also have a [colab minimal CI/CD](https://colab.research.google.com/drive/1JCGTVvWlrIsLXMPRRRSWiAstSLic4nbA) as an independent step-by-step handmade tests option.
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We also have a [colab minimal CI/CD](https://colab.research.google.com/github/catalyst-team/catalyst/blob/master/examples/notebooks/colab_ci_cd.ipynb) as an independent step-by-step handmade tests option.
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Please use it as a collaborative platform, if you have any issues during the PR.
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### Codestyle

‎README.md‎

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@@ -71,13 +71,11 @@ model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.02)
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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),
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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}
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runner = dl.SupervisedRunner(
@@ -220,13 +218,11 @@ from catalyst.contrib.datasets import MNIST
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model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
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optimizer = optim.Adam(model.parameters(), lr=0.02)
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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),
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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}
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class CustomRunner(dl.Runner):
@@ -626,13 +622,11 @@ model = nn.Sequential(nn.Flatten(), nn.Linear(28 * 28, 10))
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.02)
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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),
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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}
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runner = dl.SupervisedRunner()
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criterion = IoULoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=0.02)
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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),
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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}
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class CustomRunner(dl.SupervisedRunner):
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criterion = {"cls": nn.CrossEntropyLoss(), "kl": nn.KLDivLoss(reduction="batchmean")}
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optimizer = optim.Adam(student.parameters(), lr=0.02)
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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),
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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class DistilRunner(dl.Runner):
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"generator": torch.optim.Adam(generator.parameters(), lr=0.0003, betas=(0.5, 0.999)),
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"discriminator": torch.optim.Adam(discriminator.parameters(), lr=0.0003, betas=(0.5, 0.999)),
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}
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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)
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}
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train_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {"train": DataLoader(train_data, batch_size=32)}
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class CustomRunner(dl.Runner):
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def predict_batch(self, batch):
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def get_loaders(self, stage: str):
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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def get_loaders(self, stage: str):
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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def get_loaders(self, stage: str):
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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),
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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model = nn.Sequential(
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nn.Flatten(), nn.Linear(784, num_hidden), nn.ReLU(), nn.Linear(num_hidden, 10)
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### Documentation
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- [master](https://catalyst-team.github.io/catalyst/)
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- [21.07](https://catalyst-team.github.io/catalyst/v21.07/index.html)
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- [21.06](https://catalyst-team.github.io/catalyst/v21.06/index.html)
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- [21.05](https://catalyst-team.github.io/catalyst/v21.05/index.html) ([Catalyst — A PyTorch Framework for Accelerated Deep Learning R&D](https://medium.com/pytorch/catalyst-a-pytorch-framework-for-accelerated-deep-learning-r-d-ad9621e4ca88?source=friends_link&sk=885b4409aecab505db0a63b06f19dcef))
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- [21.04/21.04.1](https://catalyst-team.github.io/catalyst/v21.04/index.html), [21.04.2](https://catalyst-team.github.io/catalyst/v21.04.2/index.html)

‎catalyst/__version__.py‎

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__version__ = "21.06"
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__version__ = "21.07"

‎docs/index.rst‎

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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.02)
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train_data = MNIST(os.getcwd(), train=True, download=True, transform=ToTensor())
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valid_data = MNIST(os.getcwd(), train=False, download=True, transform=ToTensor())
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loaders = {
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"train": DataLoader(
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MNIST(os.getcwd(), train=True, download=True, transform=ToTensor()), batch_size=32
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),
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"valid": DataLoader(
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MNIST(os.getcwd(), train=False, download=True, transform=ToTensor()), batch_size=32
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"train": DataLoader(train_data, batch_size=32),
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"valid": DataLoader(valid_data, batch_size=32),
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runner = dl.SupervisedRunner(

‎examples/engines/README.md‎

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Let's check different
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DataParallel and DistributedDataParallel multi-GPU setups with Catalyst Engines.
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> *Please use `pip install git+https://github.com/catalyst-team/catalyst@master --upgrade` before the `v21.06` release.*
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## PyTorch
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```bash
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pip install catalyst
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CUDA_VISIBLE_DEVICES="0" python multi_gpu.py --engine=de
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CUDA_VISIBLE_DEVICES="0,1" python multi_gpu.py --engine=dp
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CUDA_VISIBLE_DEVICES="0,1" python multi_gpu.py --engine=ddp
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‎examples/engines/multi_gpu.py‎

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transform = transforms.Compose(
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)
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train_data = CIFAR10(os.getcwd(), train=True, download=True, transform=transform)
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CIFAR10(os.getcwd(), train=False, download=True, transform=transform),
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"valid": DataLoader(valid_data, batch_size=32),
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}
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def get_model(self, stage: str):

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