596ee15301
* Create rule S6983 * Address review comments * Review comments 2 * Add tags --------- Co-authored-by: ghislainpiot <ghislainpiot@users.noreply.github.com> Co-authored-by: Ghislain Piot <ghislain.piot@sonarsource.com>
70 lines
2.3 KiB
Plaintext
70 lines
2.3 KiB
Plaintext
This rule raises an issue when a `torch.utils.data.Dataloader` is instantiated without specifying the `num_workers` parameter.
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== Why is this an issue?
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In the PyTorch library, the data loaders are used to provide an interface where common operations such as batching can be implemented.
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It is also possible to parallelize the data loading process by using multiple worker processes.
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This can improve performance by increasing the number of batches being fetched in parallel, at the cost of higher memory usage.
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This performance increase can also be attributed to avoiding the Global Interpreter Lock (GIL) in the Python interpreter.
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== How to fix it
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Specify the `num_workers` parameter when instantiating the `torch.utils.data.Dataloader` object.
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The default value of `0` will use the main process to load the data, and might be faster for small datasets that can fit completely in memory.
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For larger datasets, it is recommended to use a value of `1` or higher to parallelize the data loading process.
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=== Code examples
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==== Noncompliant code example
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[source,python,diff-id=1,diff-type=noncompliant]
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----
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from torch.utils.data import DataLoader
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from torchvision import datasets
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from torchvision.transforms import ToTensor
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train_dataset = datasets.MNIST(root='data', train=True, transform=ToTensor())
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train_data_loader = DataLoader(train_dataset, batch_size=32)# Noncompliant: the num_workers parameter is not specified
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----
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==== Compliant solution
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[source,python,diff-id=1,diff-type=compliant]
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----
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from torch.utils.data import DataLoader
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from torchvision import datasets
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from torchvision.transforms import ToTensor
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train_dataset = datasets.MNIST(root='data', train=True, transform=ToTensor())
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train_data_loader = DataLoader(train_dataset, batch_size=32, num_workers=4)
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----
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== Resources
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=== Documentation
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* PyTorch documentation - https://pytorch.org/docs/stable/data.html#single-and-multi-process-data-loading[Single- and Multi-process Data Loading]
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* PyTorch documentation - https://pytorch.org/tutorials/beginner/basics/data_tutorial.html[Datasets and DataLoaders]
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ifdef::env-github,rspecator-view[]
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(visible only on this page)
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== Implementation specification
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=== Message
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Primary : Specify the `num_workers` parameter.
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=== Issue location
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Primary : Name of the instantiation
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=== Quickfix
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Fill in with the default parameter
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endif::env-github,rspecator-view[] |