Category | Topics |
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torch.compileA category for
torch.compile and PyTorch 2.0 related compiler issues.This includes: issues around TorchDynamo ( torch._dynamo ), TorchInductor (torch._inductor ) and AOTAutograd |
138
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35715
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visionTopics related to either pytorch/vision or vision research related topics
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11162
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dataTopics related to DataLoader, Dataset, torch.utils.data, pytorch/data, and TorchArrow.
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776
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quantizationThis category is for questions, discussion and issues related to PyTorch’s quantization feature.
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712
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autogradA category of posts relating to the autograd engine itself.
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5422
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1872
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323
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nlpTopics related to Natural Language Processing
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2493
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MobileThis category is dedicated for iOS and Android issues, new features and general discussion of PyTorch Mobile.
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340
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186
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C++Topics related to the C++ Frontend, C++ API or C++ Extensions
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2237
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deploymentA category of posts focused on production usage of PyTorch. Mobile deployment is out of scope for this category (for now… )
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568
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reinforcement-learningA section to discuss RL implementations, research, problems
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563
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158
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46
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70
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jitA category for TorchScript and the PyTorch JIT compiler
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836
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PyTorch LivePyTorch Live - toolkit for building AI-powered mobile apps in minutes
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134
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mpsThis category is for any question related to MPS support on Apple hardware (both M1 and x86 with AMD machines).
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85
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28
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torch.package / torch::deploythis 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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91
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projectsTell the community how you’re using PyTorch!
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112
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OpacusThis category is for topics related to either pytorch/opacus or general differential privacy related topics.
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86
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windowsThis category is focused on PyTorch on Windows related issues.
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169
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49
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54
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torchxTorchX 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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11
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xlaThis category is to discuss xla/TPU related issues.
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28
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Site FeedbackDiscussion about this site, its organization, how it works, and how we can improve it.
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91
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hackathonUse this category to discuss ideas about the PyTorch Global and local Hackathons.
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9
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glowThis category is for the Glow neural network accelerator compiler: https://github.com/pytorch/glow
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131
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51
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FAQThe FAQ category contains commonly-asked questions and their answers. Please refer to this section before you post your query.
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4
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