Category Topics

jit

A category for TorchScript and the PyTorch JIT compiler
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vision

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

This category is focused on PyTorch on Windows related issues.
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Opacus

This category is for topics related to either pytorch/opacus or general differential privacy related topics.
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projects

Tell the community how you’re using PyTorch!
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autograd

A category of posts relating to the autograd engine itself.
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C++

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

Topics related to Natural Language Processing
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quantization

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

This category is dedicated for iOS and Android issues, new features and general discussion of PyTorch Mobile.
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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.
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torch.package / torch::deploy

this category is focused on python deployment of PyTorch models and specifically the torch::deploy and torch.package APIs. More can be found at pytorch.org in the docs…
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glow

This category is for the Glow neural network accelerator compiler: https://github.com/pytorch/glow
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reinforcement-learning

A section to discuss RL implementations, research, problems
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Site Feedback

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

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

FX is a toolkit for developers to use to transform nn.Module instances. FX consists of three main components: a symbolic tracer, an intermediate representation , and Python code generation .
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hackathon

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

This category is to discuss xla/TPU related issues.
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torchx

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.
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