botorchsrcd42a5c634e859bbef9877eb960469cd1235da7faaacd3a5b24bc2de92186b134Bayesian optimization in PyTorchBoTorch 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/botorchMITopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213405-20240416-06043botorch-debuginfox86_647943052d286a20bc5095db2c7043c9d86663cdc6b9deb7f2758590b9f5acd933Debug information for package botorchThis 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/botorchMITopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213405-20240416-06043botorch-0.10.0-1.src.rpmbotorch-debugsourcex86_64e58c6b3bad3cdb15c4702586d23da7e8f0f1ae378c6e5bba8e71daa02776ce6dDebug sources for package botorchThis package provides debug sources for package botorch.
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package or when debugging this package.https://pytorch.org/botorchMITopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213405-20240416-06043botorch-0.10.0-1.src.rpmdeepspeedsrcc97246cea7d96b642e699f10baf7f812ea25d1ad2f95dfbccd4fa582f45da7d1DeepSpeed is a deep learning optimization libraryDeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.https://www.deepspeed.ai/BSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213583-20240417-03484deepspeed-debuginfox86_64173a618210628524f95ea9ef3eb0e8c2e5fd05e6a312bf9a28c464bf3dc8de69Debug information for package deepspeedThis package provides debug information for package deepspeed.
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package or when debugging this package.https://www.deepspeed.ai/BSDopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213583-20240417-03484deepspeed-0.14.0-1.src.rpmdeepspeed-debugsourcex86_645ce957359d3b85997edc88b4c607ca33da77cf3baf2f65ff5f46e9302b4f6564Debug sources for package deepspeedThis 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/BSDopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213583-20240417-03484deepspeed-0.14.0-1.src.rpmpython3-botorchx86_640265c38416b7cab32a9d911e3c5f60562f6c785fc25ad65fee52b81657c6d2c4Bayesian optimization in PyTorchBoTorch 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/botorchMITopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213405-20240416-06043botorch-0.10.0-1.src.rpmpython3-deepspeedx86_64c0d57f84233a13e922d432cf3bbd840819a0e581d37aa1b7d3b59de76d995592%{pkg_summary}DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.https://www.deepspeed.ai/BSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213583-20240417-03484deepspeed-0.14.0-1.src.rpmpython3-pytorchx86_6431e7be5ada57622d8f51ab5c0ae24ae9f72f2429f4fe5e6d807c01363358932bTensors and Dynamic neural networks in Python with strong GPU accelerationPyTorch 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/BSD-3openEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00210201-20240228-14084pytorch-2.0.1-2.src.rpm/usr/bin/convert-caffe2-to-onnx/usr/bin/convert-onnx-to-caffe2/usr/bin/torchrun/usr/lib64/python3.11/site-packages/torch/bin/FileStoreTest/usr/lib64/python3.11/site-packages/torch/bin/HashStoreTest/usr/lib64/python3.11/site-packages/torch/bin/TCPStoreTest/usr/lib64/python3.11/site-packages/torch/bin/protoc/usr/lib64/python3.11/site-packages/torch/bin/protoc-3.13.0.0/usr/lib64/python3.11/site-packages/torch/bin/test_api/usr/lib64/python3.11/site-packages/torch/bin/test_cpp_rpc/usr/lib64/python3.11/site-packages/torch/bin/test_dist_autograd/usr/lib64/python3.11/site-packages/torch/bin/test_edge_op_registration/usr/lib64/python3.11/site-packages/torch/bin/test_jit/usr/lib64/python3.11/site-packages/torch/bin/test_lazy/usr/lib64/python3.11/site-packages/torch/bin/test_tensorexpr/usr/lib64/python3.11/site-packages/torch/bin/torch_shm_manager/usr/lib64/python3.11/site-packages/torch/bin/tutorial_tensorexprpython3-pytorch3dx86_64f221e8fe268a25c6e0c3c15288d38bb205ec66c8420e1ec7ab16f7c8629d9df4PyTorch3D is FAIR's library of reusable components for deep learning with 3D dataPyTorch3D 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/BSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00212997-20240415-17482pytorch3d-0.7.5-1.src.rpm/usr/bin/pytorch3d_implicitron_runner/usr/bin/pytorch3d_implicitron_visualizerpython3-pytorchvideox86_648b6650c5098c93b98b9676f084bc06c4a1dc07c868ebd465627c348c97a8241bPyTorchVideo 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/visionBSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00212998-20240415-17501pytorchvideo-0.1.3-1.src.rpmpython3-torchlightningx86_64dd63216f058fb511668be125c02d35d73afc7b7820edde5030cf263de6db132cA Python 3 version of the deep learning library for video understanding researchTorchLightning 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/BSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213330-20240416-03563pytorch-lightning-2.2.2-1.src.rpmpython3-torchrlx86_6412ef623babee68e40e6e3eca786c0993647c771dedffe44c659e21b71d9f5be8TorchRL is a library of reusable components for deep learning with reinforcement learningTorchRL 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/MITopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213375-20240416-05174torchrl-0.3.1-1.src.rpmpython3-transformersx86_642c68c4164c37e85b2d869f93e7a40b8ba9ab3ab447aef96fb665b0cf8f3b9e63Tensors and Dynamic neural networks in Python with strong GPU accelerationTransformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.https://pypi.org/BSD-3openEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00210314-20240229-05333transformers-4.38.1-2.src.rpm/usr/bin/transformers-clipytorchsrc96e824420058612637953a127ca90b0d996999a42a662506b5cf7db184b3593eTensors and Dynamic neural networks in Python with strong GPU accelerationPyTorch 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/BSD-3openEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00210201-20240228-14084pytorch-debuginfox86_643026d9ffbaa1996e23ce04cd3b8073825950b63d5bbcc89bc9b3f947b8a34c91Debug information for package pytorchThis package provides debug information for package pytorch.
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package or when debugging this package.https://pytorch.org/BSD-3openEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00210201-20240228-14084pytorch-2.0.1-2.src.rpmpytorch-helpx86_649b3efb1e7f86c78a2d1d7f0ee8d8fbc0d8fd578d0c977863df3d7cd270bb3058Development documents and examples for torchPyTorch 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/BSD-3openEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00210201-20240228-14084pytorch-2.0.1-2.src.rpmpytorch-lightningsrc5689c45b58ff051298cf2ef106c29b3227e00e69b5fbc3d7f758b1a7d87be86bTorchLightning 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/BSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213330-20240416-03563pytorch-lightning-debuginfox86_64bdc27b8c698dac968ad08d9f30ceb8644a2c58a44019cc186f52a2847562ce6aDebug information for package pytorch-lightningThis package provides debug information for package pytorch-lightning.
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package or when debugging this package.https://pypi.org/project/pytorch-lightning/BSDopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213330-20240416-03563pytorch-lightning-2.2.2-1.src.rpmpytorch3dsrc993f962154ec40809cddc2ea23c825db023caa78a0112c3bb5c4722e2d1e60dfPyTorch3D is FAIR's library of reusable components for deep learning with 3D dataPyTorch3D 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/BSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00212997-20240415-17482pytorch3d-debuginfox86_64b3dece8425dbf4dba0f49b2466858ca0aa949bb05491b4966dd5b1be2a94fd35Debug information for package pytorch3dThis package provides debug information for package pytorch3d.
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package or when debugging this package.https://pytorch3d.org/BSDopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00212997-20240415-17482pytorch3d-0.7.5-1.src.rpmpytorchvideosrc3fbdf54278ff1295a27cd90533b1abfb8db8afca0ca2d277dfe606e4dc64cb12A deep learning library for video understanding researchPyTorchVideo 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/visionBSDopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00212998-20240415-17501pytorchvideo-debuginfox86_646170ec39abd3667c69e8bc8b68fe515d361604f622e930d6009bda27dec646b7Debug information for package pytorchvideoThis package provides debug information for package pytorchvideo.
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package or when debugging this package.https://github.com/pytorch/visionBSDopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00212998-20240415-17501pytorchvideo-0.1.3-1.src.rpmpytorchvideo-debugsourcex86_645c42d73b462f08fc36db641c3bd2d5b6fbbb1112949a98a2da24d418a8a2eab0Debug sources for package pytorchvideoThis package provides debug sources for package pytorchvideo.
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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/MITopenEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00213375-20240416-05174torchrl-debuginfox86_640022d3f196d81d7c615e7cafce3aa8cfecad9c9c600951e271cce0e6a23a8663Debug information for package torchrlThis package provides debug information for package torchrl.
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package or when debugging this package.https://torchrl.org/MITopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213375-20240416-05174torchrl-0.3.1-1.src.rpmtorchrl-debugsourcex86_64a7d0f8be2da8282ba583e88cf3719d9487d6bd437566b4a92577dff9084fe077Debug sources for package torchrlThis package provides debug sources for package torchrl.
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package or when debugging this package.https://torchrl.org/MITopenEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00213375-20240416-05174torchrl-0.3.1-1.src.rpmtorchvisionsrcf8efb64e52b739afaef28599d1fb7c531c2e918b5f4d7fceebf4b2646676438bThe 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/visionBSDopenEuler Copr - user xmyuDevelopment/Librarieseur-prod-workerlocal-x86-64-normal-prod-00213000-20240415-17552torchvisionx86_64056b5e7bf76e1c546992051ce9cc5997751745e99762b80bc3be61b2f399ad95The 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/visionBSDopenEuler Copr - user xmyuDevelopment/Librarieseur-prod-workerlocal-x86-64-normal-prod-00213000-20240415-17552torchvision-0.16.2-1.src.rpmtransformerssrc3efd134d90c52df19bbb724e8c3c40b5eb70fa8091f9f999cc6aa84542b79938Tensors and Dynamic neural networks in Python with strong GPU accelerationTransformers 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/BSD-3openEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00210314-20240229-05333transformers-debuginfox86_6435267806aaa778e8fbf867df082134cf350c8742b30209d00f64b7b80abcd583Debug information for package transformersThis package provides debug information for package transformers.
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package or when debugging this package.https://pypi.org/BSD-3openEuler Copr - user xmyuDevelopment/Debugeur-prod-workerlocal-x86-64-normal-prod-00210314-20240229-05333transformers-4.38.1-2.src.rpmtransformers-helpx86_64cdbdd4e10dd7f70171a64a52e7724627c4769a46918b09847163119874eb90adDevelopment documents and examples for transformersTransformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.https://pypi.org/BSD-3openEuler Copr - user xmyuUnspecifiedeur-prod-workerlocal-x86-64-normal-prod-00210314-20240229-05333transformers-4.38.1-2.src.rpm