botorch src d42a5c634e859bbef9877eb960469cd1235da7faaacd3a5b24bc2de92186b134 Bayesian optimization in PyTorch BoTorch is a library for Bayesian Optimization built on PyTorch. It provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers. https://pytorch.org/botorch botorch-debuginfo x86_64 7943052d286a20bc5095db2c7043c9d86663cdc6b9deb7f2758590b9f5acd933 Debug information for package botorch This package provides debug information for package botorch. Debug information is useful when developing applications that use this package or when debugging this package. https://pytorch.org/botorch botorch-debugsource x86_64 e58c6b3bad3cdb15c4702586d23da7e8f0f1ae378c6e5bba8e71daa02776ce6d Debug sources for package botorch This package provides debug sources for package botorch. Debug sources are useful when developing applications that use this package or when debugging this package. https://pytorch.org/botorch deepspeed src c97246cea7d96b642e699f10baf7f812ea25d1ad2f95dfbccd4fa582f45da7d1 DeepSpeed is a deep learning optimization library DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. https://www.deepspeed.ai/ deepspeed-debuginfo x86_64 173a618210628524f95ea9ef3eb0e8c2e5fd05e6a312bf9a28c464bf3dc8de69 Debug information for package deepspeed This package provides debug information for package deepspeed. Debug information is useful when developing applications that use this package or when debugging this package. https://www.deepspeed.ai/ deepspeed-debugsource x86_64 5ce957359d3b85997edc88b4c607ca33da77cf3baf2f65ff5f46e9302b4f6564 Debug sources for package deepspeed This package provides debug sources for package deepspeed. Debug sources are useful when developing applications that use this package or when debugging this package. https://www.deepspeed.ai/ python3-botorch x86_64 0265c38416b7cab32a9d911e3c5f60562f6c785fc25ad65fee52b81657c6d2c4 Bayesian optimization in PyTorch BoTorch is a library for Bayesian Optimization built on PyTorch. It provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers. https://pytorch.org/botorch python3-deepspeed x86_64 c0d57f84233a13e922d432cf3bbd840819a0e581d37aa1b7d3b59de76d995592 %{pkg_summary} DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. https://www.deepspeed.ai/ python3-pytorch x86_64 31e7be5ada57622d8f51ab5c0ae24ae9f72f2429f4fe5e6d807c01363358932b Tensors and Dynamic neural networks in Python with strong GPU acceleration PyTorch is a Python package that provides two high-level features: - Tensor computation (like NumPy) with strong GPU acceleration - Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed. https://pytorch.org/ python3-pytorch3d x86_64 f221e8fe268a25c6e0c3c15288d38bb205ec66c8420e1ec7ab16f7c8629d9df4 PyTorch3D is FAIR's library of reusable components for deep learning with 3D data PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch. Key features include: - Data structure for storing and manipulating triangle meshes - Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions) - A differentiable mesh renderer - Implicitron, see its README, a framework for new-view synthesis via implicit representations. https://pytorch3d.org/ python3-pytorchvideo x86_64 8b6650c5098c93b98b9676f084bc06c4a1dc07c868ebd465627c348c97a8241b PyTorchVideo is a deep learning library with a focus on video understanding work. PytorchVideo provides reusable, modular and efficient components needed to accelerate the video understanding research. PyTorchVideo is a deep learning library with a focus on video understanding work. PytorchVideo provides reusable, modular and efficient components needed to accelerate the video understanding research. https://github.com/pytorch/vision python3-torchlightning x86_64 dd63216f058fb511668be125c02d35d73afc7b7820edde5030cf263de6db132c A Python 3 version of the deep learning library for video understanding research TorchLightning is a lightweight PyTorch wrapper for high-performance AI research. It organizes your code to decouple the science from the engineering, and enables seamless linear scaling to any hardware setup. https://pypi.org/project/pytorch-lightning/ python3-torchrl x86_64 12ef623babee68e40e6e3eca786c0993647c771dedffe44c659e21b71d9f5be8 TorchRL is a library of reusable components for deep learning with reinforcement learning TorchRL provides efficient, reusable components for Reinforcement Learning research with PyTorch. Key features include: - Data structures for storing and manipulating reinforcement learning environments - Efficient operations on these structures (sampling, loss functions) - A framework for policy gradient methods - A framework for Q-learning methods. https://torchrl.org/ python3-transformers x86_64 2c68c4164c37e85b2d869f93e7a40b8ba9ab3ab447aef96fb665b0cf8f3b9e63 Tensors and Dynamic neural networks in Python with strong GPU acceleration Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. https://pypi.org/ pytorch src 96e824420058612637953a127ca90b0d996999a42a662506b5cf7db184b3593e Tensors and Dynamic neural networks in Python with strong GPU acceleration PyTorch is a Python package that provides two high-level features: - Tensor computation (like NumPy) with strong GPU acceleration - Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed. https://pytorch.org/ pytorch-debuginfo x86_64 3026d9ffbaa1996e23ce04cd3b8073825950b63d5bbcc89bc9b3f947b8a34c91 Debug information for package pytorch This package provides debug information for package pytorch. Debug information is useful when developing applications that use this package or when debugging this package. https://pytorch.org/ pytorch-debugsource x86_64 26d2d4794cf5a646528f6ea0ee01b4f3fac46c854119b4118e2ad609d81d2ab8 Debug sources for package pytorch This package provides debug sources for package pytorch. Debug sources are useful when developing applications that use this package or when debugging this package. https://pytorch.org/ pytorch-help x86_64 9b3efb1e7f86c78a2d1d7f0ee8d8fbc0d8fd578d0c977863df3d7cd270bb3058 Development documents and examples for torch PyTorch is a Python package that provides two high-level features: - Tensor computation (like NumPy) with strong GPU acceleration - Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy and Cython to extend PyTorch when needed. https://pytorch.org/ pytorch-lightning src 5689c45b58ff051298cf2ef106c29b3227e00e69b5fbc3d7f758b1a7d87be86b TorchLightning is a lightweight PyTorch wrapper for high-performance AI research. It organizes your code to decouple the science from the engineering, and enables seamless linear scaling to any hardware setup. TorchLightning is a lightweight PyTorch wrapper for high-performance AI research. It organizes your code to decouple the science from the engineering, and enables seamless linear scaling to any hardware setup. https://pypi.org/project/pytorch-lightning/ pytorch-lightning-debuginfo x86_64 bdc27b8c698dac968ad08d9f30ceb8644a2c58a44019cc186f52a2847562ce6a Debug information for package pytorch-lightning This package provides debug information for package pytorch-lightning. Debug information is useful when developing applications that use this package or when debugging this package. https://pypi.org/project/pytorch-lightning/ pytorch-lightning-debugsource x86_64 b57eae47334fe4c7f093ce2d8810dfdd968b89c80dde930b174f2b4a306bbcf6 Debug sources for package pytorch-lightning This package provides debug sources for package pytorch-lightning. Debug sources are useful when developing applications that use this package or when debugging this package. https://pypi.org/project/pytorch-lightning/ pytorch3d src 993f962154ec40809cddc2ea23c825db023caa78a0112c3bb5c4722e2d1e60df PyTorch3D is FAIR's library of reusable components for deep learning with 3D data PyTorch3D provides efficient, reusable components for 3D Computer Vision research with PyTorch. Key features include: - Data structure for storing and manipulating triangle meshes - Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions) - A differentiable mesh renderer - Implicitron, see its README, a framework for new-view synthesis via implicit representations. https://pytorch3d.org/ pytorch3d-debuginfo x86_64 b3dece8425dbf4dba0f49b2466858ca0aa949bb05491b4966dd5b1be2a94fd35 Debug information for package pytorch3d This package provides debug information for package pytorch3d. Debug information is useful when developing applications that use this package or when debugging this package. https://pytorch3d.org/ pytorch3d-debugsource x86_64 d2be9c33eadaa8b823cd4f0a4ea4e3e5745c21209bec2c94ac447997d44d4988 Debug sources for package pytorch3d This package provides debug sources for package pytorch3d. Debug sources are useful when developing applications that use this package or when debugging this package. https://pytorch3d.org/ pytorchvideo src 3fbdf54278ff1295a27cd90533b1abfb8db8afca0ca2d277dfe606e4dc64cb12 A deep learning library for video understanding research PyTorchVideo is a deep learning library with a focus on video understanding work. PytorchVideo provides reusable, modular and efficient components needed to accelerate the video understanding research. https://github.com/pytorch/vision pytorchvideo-debuginfo x86_64 6170ec39abd3667c69e8bc8b68fe515d361604f622e930d6009bda27dec646b7 Debug information for package pytorchvideo This package provides debug information for package pytorchvideo. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/pytorch/vision pytorchvideo-debugsource x86_64 5c42d73b462f08fc36db641c3bd2d5b6fbbb1112949a98a2da24d418a8a2eab0 Debug sources for package pytorchvideo This package provides debug sources for package pytorchvideo. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/pytorch/vision torchrl src 946b37416cb8d8ea66c5205af4dee45eb7e30aeda7ae7f2f27b9710693a9fcb4 TorchRL is a library of reusable components for deep learning with reinforcement learning TorchRL provides efficient, reusable components for Reinforcement Learning research with PyTorch. Key features include: - Data structures for storing and manipulating reinforcement learning environments - Efficient operations on these structures (sampling, loss functions) - A framework for policy gradient methods - A framework for Q-learning methods. https://torchrl.org/ torchrl-debuginfo x86_64 0022d3f196d81d7c615e7cafce3aa8cfecad9c9c600951e271cce0e6a23a8663 Debug information for package torchrl This package provides debug information for package torchrl. Debug information is useful when developing applications that use this package or when debugging this package. https://torchrl.org/ torchrl-debugsource x86_64 a7d0f8be2da8282ba583e88cf3719d9487d6bd437566b4a92577dff9084fe077 Debug sources for package torchrl This package provides debug sources for package torchrl. Debug sources are useful when developing applications that use this package or when debugging this package. https://torchrl.org/ torchvision src f8efb64e52b739afaef28599d1fb7c531c2e918b5f4d7fceebf4b2646676438b The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. Torchvision is a PyTorch add-on that provides access to: - Datasets (MNIST, CIFAR10, etc.) - Models (ResNet, VGG, etc.) - Transforms (for image preprocessing) https://github.com/pytorch/vision torchvision x86_64 056b5e7bf76e1c546992051ce9cc5997751745e99762b80bc3be61b2f399ad95 The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. Torchvision is a PyTorch add-on that provides access to: - Datasets (MNIST, CIFAR10, etc.) - Models (ResNet, VGG, etc.) - Transforms (for image preprocessing) https://github.com/pytorch/vision transformers src 3efd134d90c52df19bbb724e8c3c40b5eb70fa8091f9f999cc6aa84542b79938 Tensors and Dynamic neural networks in Python with strong GPU acceleration Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. These models can be applied on: - Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages. - Images, for tasks like image classification, object detection, and segmentation. - Audio, for tasks like speech recognition and audio classification. https://pypi.org/ transformers-debuginfo x86_64 35267806aaa778e8fbf867df082134cf350c8742b30209d00f64b7b80abcd583 Debug information for package transformers This package provides debug information for package transformers. Debug information is useful when developing applications that use this package or when debugging this package. https://pypi.org/ transformers-debugsource x86_64 b413857568cd2568a8796183d0a7bd76b90d3d575d939664c9a465bda1f43ed4 Debug sources for package transformers This package provides debug sources for package transformers. Debug sources are useful when developing applications that use this package or when debugging this package. https://pypi.org/ transformers-help x86_64 cdbdd4e10dd7f70171a64a52e7724627c4769a46918b09847163119874eb90ad Development documents and examples for transformers Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. https://pypi.org/