PyTorch Forums (original) (raw)

nlp

Topics related to Natural Language Processing

2713

torch.compile

A category for torch.compile and PyTorch 2.0 related compiler issues.
This includes: issues around TorchDynamo ( torch._dynamo ), TorchInductor (torch._inductor) and AOTAutograd

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38493

C++

Topics related to the C++ Frontend, C++ API or C++ Extensions

2498

data

Topics related to DataLoader, Dataset, torch.utils.data, pytorch/data, and TorchArrow.

1034

ExecuTorch

A category of posts relating to ExecuTorch.

deployment

A category of posts focused on production usage of PyTorch. Mobile deployment is out of scope for this category (for now… )

629

autograd

A category of posts relating to the autograd engine itself.

5887

quantization

This category is for questions, discussion and issues related to PyTorch’s quantization feature.

845

vision

Topics related to either pytorch/vision or vision research related topics

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2217

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Mobile

This category is dedicated to the now deprecated “PyTorch Mobile” project. Please look into ExecuTorch as the new Mobile runtime for PyTorch.

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windows

This category is focused on PyTorch on Windows related issues.

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xla

This category is to discuss xla/TPU related issues.

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232

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mps

This category is for any question related to MPS support on Apple hardware (both M1 and x86 with AMD machines).

115

projects

Tell the community how you’re using PyTorch!

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115

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PyTorch Live

PyTorch Live is no longer supported. Please look into ExecuTorch as the new Mobile runtime for PyTorch.

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FAQ

The FAQ category contains commonly-asked questions and their answers. Please refer to this section before you post your query.

Site Feedback

Discussion about this site, its organization, how it works, and how we can improve it.

hackathon

Use this category to discuss ideas about the PyTorch Global and local Hackathons.

TorchX is an SDK for quickly building and deploying ML applications from R&D to production. It offers various builtin components that encode MLOps best practices and make advanced features like distributed training and hyperparameter optimization accessible to all. Users can get started with TorchX with no added setup cost since it supports popular ML schedulers and pipeline orchestrators that are already widely adopted and deployed in production.