botorch src 32a15d2234263282d771caea916864dea081b2d688dce4dfed52618b21dfebd1 Bayesian optimization in PyTorch Provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers. Harnesses the power of PyTorch, including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition functions via the reparameterization trick, which makes it straightforward to implement new ideas without having to impose restrictive assumptions about the underlying model. Enables seamless integration with deep and/or convolutional architectures in PyTorch. Has first-class support for state-of-the art probabilistic models in GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference. https://pytorch.org/botorch botorch-debuginfo x86_64 34644157837db33d326d7d11b4ee5f49fc9b209e35206bfcb8cb756c51ee1d37 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 0f9c3b5cbc4ea7ef6714896d4515284d1510b3b46f3075014bef56ba90bbd9e3 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 botorch-help x86_64 58d3a64fb29b814e2fd5c91a265cf7e699fc8ead96a2fb792a29584b96e933fb Development documents and examples for botorch Provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers. Harnesses the power of PyTorch, including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition functions via the reparameterization trick, which makes it straightforward to implement new ideas without having to impose restrictive assumptions about the underlying model. Enables seamless integration with deep and/or convolutional architectures in PyTorch. Has first-class support for state-of-the art probabilistic models in GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference. https://pytorch.org/botorch cpuinfo src b588ecb6502314c6a814e842269e67a1ff3b9b6faeca4e8c9e6ee30a20337cce A library to detect information about host CPU cpuinfo is a library to detect essential for performance optimization information about host CPU. Features * Cross-platform availability: * Linux, Windows, macOS, Android, and iOS operating systems * x86, x86-64, ARM, and ARM64 architectures * Modern C/C++ interface * Thread-safe * No memory allocation after initialization * No exceptions thrown * Detection of supported instruction sets, up to AVX512 (x86) and ARMv8.3 extensions * Detection of SoC and core information: * Processor (SoC) name * Vendor and microarchitecture for each CPU core * ID (MIDR on ARM, CPUID leaf 1 EAX value on x86) for each CPU core * Detection of cache information: * Cache type (instruction/data/unified), size and line size * Cache associativity * Cores and logical processors (hyper-threads) sharing the cache * Detection of topology information (relative between logical processors, cores, and processor packages) * Well-tested production-quality code: * 60+ mock tests based on data from real devices * Includes work-arounds for common bugs in hardware and OS kernels * Supports systems with heterogenous cores, such as big.LITTLE and Max.Med.Min * Permissive open-source license (Simplified BSD) https://github.com/pytorch/cpuinfo cpuinfo x86_64 23c04074fcee70f6e58e96382ffebc38d9c563a614ef9d1d540175b1e7e3d99d A library to detect information about host CPU cpuinfo is a library to detect essential for performance optimization information about host CPU. Features * Cross-platform availability: * Linux, Windows, macOS, Android, and iOS operating systems * x86, x86-64, ARM, and ARM64 architectures * Modern C/C++ interface * Thread-safe * No memory allocation after initialization * No exceptions thrown * Detection of supported instruction sets, up to AVX512 (x86) and ARMv8.3 extensions * Detection of SoC and core information: * Processor (SoC) name * Vendor and microarchitecture for each CPU core * ID (MIDR on ARM, CPUID leaf 1 EAX value on x86) for each CPU core * Detection of cache information: * Cache type (instruction/data/unified), size and line size * Cache associativity * Cores and logical processors (hyper-threads) sharing the cache * Detection of topology information (relative between logical processors, cores, and processor packages) * Well-tested production-quality code: * 60+ mock tests based on data from real devices * Includes work-arounds for common bugs in hardware and OS kernels * Supports systems with heterogenous cores, such as big.LITTLE and Max.Med.Min * Permissive open-source license (Simplified BSD) https://github.com/pytorch/cpuinfo cpuinfo-debuginfo x86_64 7371ee95b4e290447fe3d2d0e3bdf3c00f34aea73ee2cdee90789e5cddbf9422 Debug information for package cpuinfo This package provides debug information for package cpuinfo. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/pytorch/cpuinfo cpuinfo-debugsource x86_64 e3176c7d5997f4710bbb282443de3d8521c3c7f8e0b9ef9055f56cd96b15d9c3 Debug sources for package cpuinfo This package provides debug sources for package cpuinfo. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/pytorch/cpuinfo cpuinfo-devel x86_64 13500c8af0dc1487544f12da24a475819f92adf1f0278b550a58e535793dbcbb Headers and libraries for cpuinfo This package contains the developement libraries and headers for cpuinfo. https://github.com/pytorch/cpuinfo onnxruntime src a414b150e51e822806b0415ab923e50743495e21d1ebb4be9a68b083ebf218c1 ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime src 33b2417e448e2d96d48c126859d2295807a5c9c9aff75bcc8d9b5c0b79eb1383 ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime src 51c9503c1152ee97b33fb7265d01892d846ccb8fdd87ef5e90be7e815c51a60b ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime src 353a67640f9dad9f2e0ae4985cba339e8de929e3b08e11ed22546a7d27981802 ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime src c40b7732bf9529b082ab39375d04ad8903c2de698f2969eba6349206d6f377c9 ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime x86_64 db6a5872b5ed476e976ca6a91943eed4a976e4d9b5d0e7c3ceb33ac30c884ff7 ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime x86_64 009a9f0ab6c677cfcfed2fd86ad39d2e26c35a7e83c240d704d84ecaa9a9304f ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator onnxruntime is a cross-platform inferencing and training accelerator compatible with many popular ML/DNN frameworks, including PyTorch, TensorFlow/Keras, scikit-learn, and more. https://github.com/microsoft/onnxruntime onnxruntime-debuginfo x86_64 7f9ee6f2252fa09b1333f36689b6b327c1ecebf3e3d19b64f1865d3b60917883 Debug information for package onnxruntime This package provides debug information for package onnxruntime. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/microsoft/onnxruntime onnxruntime-debuginfo x86_64 8ba263fe1ad18bb1fb5781bb04710bd1cd1e3716fc85a22b29adf984510f011e Debug information for package onnxruntime This package provides debug information for package onnxruntime. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/microsoft/onnxruntime onnxruntime-debugsource x86_64 d6f7122c2fe60abbfbaf5daf8edff144b0014685af3f74bee2d77fb558df3366 Debug sources for package onnxruntime This package provides debug sources for package onnxruntime. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/microsoft/onnxruntime onnxruntime-debugsource x86_64 dec7df601c2235d73e9b7c23fa0cf12e2375e00242ad664b62a466dc7ac66fb0 Debug sources for package onnxruntime This package provides debug sources for package onnxruntime. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/microsoft/onnxruntime onnxruntime-devel x86_64 92658eef5fa968d070e61d5d52d4af7320215ec52afbe2e310f8e2c7111a4c1c The development part of the onnxruntime package The development part of the onnxruntime package https://github.com/microsoft/onnxruntime onnxruntime-devel x86_64 6b85f603a5145df8772098628d964b9d6a1bad9506b8be7603f02c35dd99e3ee The development part of the onnxruntime package The development part of the onnxruntime package https://github.com/microsoft/onnxruntime onnxruntime-doc x86_64 ad065a82e2f3b4b126c91a806a1a0fbdc9a4aaa4721b88ae08e5459638035cb5 Documentation files for the onnxruntime package Documentation files for the onnxruntime package https://github.com/microsoft/onnxruntime onnxruntime-doc x86_64 6032177a23bf2d48e6af6800b1d505ea8794326396aefb0994927a525243ea49 Documentation files for the onnxruntime package Documentation files for the onnxruntime package https://github.com/microsoft/onnxruntime python-fsspec src d84a4c29f258573a7b59a7aeebcdd5c4ebf13a75f84c50407b941233a5d5dcd3 File-system specification A specification for pythonic filesystems. http://github.com/fsspec/filesystem_spec python-fsspec-help noarch 7b0bfdfd9b6e5d0e10a83b4cfa19b11ab7f9b9aba9161c093ddaa6fa9eba3297 Development documents and examples for fsspec A specification for pythonic filesystems. http://github.com/fsspec/filesystem_spec python-fvcore src ae334b439da6e988d38e3a533f0a33189c981c2f8636a37f680f6fe7b94b237e Collection of common code that's shared among different research projects in FAIR computer vision team. fvcore is a light-weight core library that provides the most common and essential functionality shared in various computer vision frameworks developed in FAIR, such as Detectron2, PySlowFast, and ClassyVision. All components in this library are type-annotated, tested, and benchmarked. https://github.com/facebookresearch/fvcore python-gpytorch src 4649c32dac0c5f9743a1968dfaf789ac2d6e0f46a2be64d7fd56a05361848ff3 A highly efficient implementation of Gaussian Processes in PyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease. GPyTorch provides (1) significant GPU acceleration (through MVM based inference); (2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility (3) easy integration with deep learning frameworks. https://github.com/cornellius-gp/gpytorch python-gpytorch-debuginfo x86_64 f8d8308b8c9b3fbde4428710741285dc3ec8e5b20b32bae670a8edc031273047 Debug information for package python-gpytorch This package provides debug information for package python-gpytorch. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/gpytorch python-gpytorch-debugsource x86_64 9178e34746e8d05765b355876bd4e0fcd129e049a9fed9e122386100c4a230e4 Debug sources for package python-gpytorch This package provides debug sources for package python-gpytorch. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/gpytorch python-gpytorch-help x86_64 6d2c6ce9a906c0c7da7168a98444014c233a89f41500c6c02b616909d9bd5cb9 Development documents and examples for python-gpytorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease. GPyTorch provides (1) significant GPU acceleration (through MVM based inference); (2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility (3) easy integration with deep learning frameworks. https://github.com/cornellius-gp/gpytorch python-huggingface-hub src 0a0ffe4451ca7ee9c5964452e8190efb60ef6057e76215d548f76fb08d63276b The official Python client for the Huggingface Hub. The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. https://github.com/huggingface/huggingface_hub python-huggingface-hub src 63b17512c3fd8472de758c4f7e1029ca88dcc506b34a1da28810f6e679577ded The official Python client for the Huggingface Hub. The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. https://github.com/huggingface/huggingface_hub python-huggingface-hub-help x86_64 66b49d04b917d8a6494274d2a2c1ceb5cb86526ed32900833de62d7a4f46f0c5 Development documents and examples for huggingface-hub The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. https://github.com/huggingface/huggingface_hub python-huggingface-hub-help x86_64 625790b83e7099173d82e8a8b6271fafdb1dc291f3fbb8a4c2a3ff9dc3b531f0 Development documents and examples for huggingface-hub The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. https://github.com/huggingface/huggingface_hub python-iopath src f234c4e7d2e8113b6e1b3fecb891d073ac5f783f2375e92d40bb9ad06f5972fd A python library that provides common I/O interface across different storage backends. iopath is a light-weight core library that provides the most common and essential functionality https://github.com/facebookresearch/iopath python-jaxtyping src 7c4aac1500d4b0696ac125eb89217656a45a1d6a749112d376cb3e358ac7739f Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. Type annotations and runtime type-checking for: shape and dtype of JAX arrays; (Now also supports PyTorch, NumPy, and TensorFlow!) PyTrees. https://github.com/google-admin/jaxtyping python-jaxtyping-debuginfo x86_64 b06be1eb75e24bafb07c571ce6222f3acb9855df91105afce4915b4e0b262ecc Debug information for package python-jaxtyping This package provides debug information for package python-jaxtyping. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/google-admin/jaxtyping python-jaxtyping-debugsource x86_64 9f79aba1a925f92497ce98dc0edb435b1c3c598547e849bc48b0056136bf6eac Debug sources for package python-jaxtyping This package provides debug sources for package python-jaxtyping. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/google-admin/jaxtyping python-jaxtyping-help x86_64 fe4ef1c6ea7a251b10b2e07607597b82c0c91ebdc4552b4edea5458e92cf52ab Development documents and examples for python-jaxtyping Type annotations and runtime type-checking for: shape and dtype of JAX arrays; (Now also supports PyTorch, NumPy, and TensorFlow!) PyTrees. https://github.com/google-admin/jaxtyping python-linear-operator src e831512f6dffc0e63f3088558e4f6ca7003c7e112390aa2d74953654c6c662fd A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python-linear-operator src 56fd77fc007528f032a7c16ead122216710ccc357a29e94af4276ccf1d6543bb A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python-linear-operator src 8d4eedb71c160b88aff7a114f0bf85f865ac7b42335ee5aff8f7fcdd99f2db89 A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python-linear-operator-debuginfo x86_64 a14842ccfa1fdca541b69e8183b88b4048e575fb716854ba657697b02df0d0e4 Debug information for package python-linear-operator This package provides debug information for package python-linear-operator. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/linear_operator python-linear-operator-debuginfo x86_64 18f7b47e3222d61e0a43c654614d5d5df5ba06c338fcb19a462833c524dba57c Debug information for package python-linear-operator This package provides debug information for package python-linear-operator. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/linear_operator python-linear-operator-debuginfo x86_64 810f82e765422a82c43859db973b449839e0df96889b996efd3adbebf645d25f Debug information for package python-linear-operator This package provides debug information for package python-linear-operator. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/linear_operator python-linear-operator-debugsource x86_64 f84012733a37b3b46f01f5cb586580cb3c9e62448554ed3e7391a6e1a2e61a01 Debug sources for package python-linear-operator This package provides debug sources for package python-linear-operator. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/linear_operator python-linear-operator-debugsource x86_64 b12db2cc29a9acd666ab4d0e52dd3be6ee2e3a55ebe21d8f81747bbe93a82047 Debug sources for package python-linear-operator This package provides debug sources for package python-linear-operator. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/linear_operator python-linear-operator-debugsource x86_64 2bef504c348f191ac0183c1d25454a8df5449a6badd575d991174f1918aa1f43 Debug sources for package python-linear-operator This package provides debug sources for package python-linear-operator. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/cornellius-gp/linear_operator python-linear-operator-help x86_64 4c0f95b603487b09cf17ad8318aaf19477ea9c1312924e823989ab510ed8a5b0 Development documents and examples for python-linear-operator LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python-linear-operator-help x86_64 0719e66d578972fe187e87a0b5c5bea74cce1e09cea59df7df74267f09752ba6 Development documents and examples for python-linear-operator LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python-linear-operator-help x86_64 d0c48713164589e942077c366e8108599cc89ffd7562b588a8e7275ec6586d01 Development documents and examples for python-linear-operator LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python-multipledispatch src 51dfcc49aecfbe4f37128e6e82e6eb5a264aae43fe4e1a7da048b27a0c20aae7 A relatively sane approach to multiple dispatch in Python. A relatively sane approach to multiple dispatch in Python. This implementation of multiple dispatch is efficient, mostly complete, performs static analysis to avoid conflicts, and provides optional namespace support. https://github.com/mrocklin/multipledispatch python-multipledispatch-debuginfo x86_64 96969a951ef9e88442b6d6cba913e0a7617258de31b71777883a226f490b817d Debug information for package python-multipledispatch This package provides debug information for package python-multipledispatch. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/mrocklin/multipledispatch python-multipledispatch-debugsource x86_64 149f317c70176ee5de5a151ef41fd02864571bc3bebe2423d909b6c4b8716aa6 Debug sources for package python-multipledispatch This package provides debug sources for package python-multipledispatch. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/mrocklin/multipledispatch python-multipledispatch-help x86_64 c5434b24af7bcfd5dbcb84560e79f08862e12271525151010d92c5dabf0dd772 Development documents and examples for python-multipledispatch A relatively sane approach to multiple dispatch in Python. This implementation of multiple dispatch is efficient, mostly complete, performs static analysis to avoid conflicts, and provides optional namespace support. https://github.com/mrocklin/multipledispatch python-pyav src 9b3a570f350d016161357d3692bfdf01bc52a63bfbb781bba1565b871303ef2a Pythonic bindings for FFmpeg's libraries. PyAV is a Pythonic binding for the FFmpeg libraries. We aim to provide all of the power and control of the underlying library, but manage the gritty details as much as possible. PyAV is for direct and precise access to your media via containers, streams, packets, codecs, and frames. It exposes a few transformations of that data, and helps you get your data to/from other packages. https://github.com/PyAV-Org/pyav python-pyav-help x86_64 9da4f9cbd82ea74107859a23ddd474e14e1593eda7a4a1b63ee5232a89140b6f Development documents and examples for PyAV PyAV is a Pythonic binding for the FFmpeg libraries. We aim to provide all of the power and control of the underlying library, but manage the gritty details as much as possible. PyAV is for direct and precise access to your media via containers, streams, packets, codecs, and frames. It exposes a few transformations of that data, and helps you get your data to/from other packages. https://github.com/PyAV-Org/pyav python-pyro-api src ead5a715d8a264236d89b640716ce9f2976c4a67b11d29a52c7dc3ee1a01cf77 Alert Management API for wildfire prevention, detection & monitoring Alert Management API for wildfire prevention, detection & monitoring. The building blocks of our wildfire detection & monitoring API. https://github.com/pyronear/pyro-api python-pyro-api-debuginfo x86_64 cd4332da5e88f43584956c23c26a66e09390787461d614f66fa3ed772ce2745c Debug information for package python-pyro-api This package provides debug information for package python-pyro-api. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/pyronear/pyro-api python-pyro-api-debugsource x86_64 9e56ef51087049bd7d6f439f03d984a1d3f3d2901cee7656bba1ce78634c7968 Debug sources for package python-pyro-api This package provides debug sources for package python-pyro-api. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/pyronear/pyro-api python-pyro-api-help x86_64 131b6cdc659e633b662f6ce6d7715e726d9444f2afbbcf1215d5c360231a3c5d Development documents and examples for python-pyro-api Alert Management API for wildfire prevention, detection & monitoring. The building blocks of our wildfire detection & monitoring API. https://github.com/pyronear/pyro-api python-pyro-ppl src 9d086e5954053db70ca668758b3f09fc6fffc957542a7edd60e0850e0baafd36 Deep universal probabilistic programming with Python and PyTorch Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind: - Universal: Pyro is a universal PPL - it can represent any computable probability distribution. - Scalable: Pyro scales to large data sets with little overhead compared to hand-written code. - Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions. - Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference. https://github.com/pyro-ppl/pyro python-pyro-ppl-debuginfo x86_64 0c4485ffc080a4a233d37b31ef09c57cefb595872992bb2d8d264b97c12389e6 Debug information for package python-pyro-ppl This package provides debug information for package python-pyro-ppl. Debug information is useful when developing applications that use this package or when debugging this package. https://github.com/pyro-ppl/pyro python-pyro-ppl-debugsource x86_64 d000f1da64f89d8e2a0fb4046bf7eb9b0b064dfa2f2d2dbbada87931f2131b7b Debug sources for package python-pyro-ppl This package provides debug sources for package python-pyro-ppl. Debug sources are useful when developing applications that use this package or when debugging this package. https://github.com/pyro-ppl/pyro python-pyro-ppl-help x86_64 9fc0d520082d85048dd30d5c4e7742be7011f06822417f482fa94155dbd8bd45 Development documents and examples for python-pyro-ppl Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind: - Universal: Pyro is a universal PPL - it can represent any computable probability distribution. - Scalable: Pyro scales to large data sets with little overhead compared to hand-written code. - Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions. - Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference. https://github.com/pyro-ppl/pyro python-safetensors src d0353e17b75b65ed0530c564031e08c72a162afb1fb7b44cac88c494dccb8666 Simple, safe way to store and distribute tensors Safetensors is a new simple format for storing tensors safely (as opposed to pickle) and that is still fast (zero-copy). Safetensors is really fast. https://github.com/huggingface/safetensors python-tokenizers src e63274942c4e5a215ad8004c8f81eeb49976a726b2519d4da4ff78a0a23798db Fast State-of-the-Art Tokenizers optimized for Research and Production A Tokenizer works as a pipeline, it processes some raw text as input and outputs an Encoding. The various steps of the pipeline are: The Normalizer: in charge of normalizing the text. Common examples of normalization are the unicode normalization standards, such as NFD or NFKC. More details about how to use the Normalizers are available on the Hugging Face blog The PreTokenizer: in charge of creating initial words splits in the text. The most common way of splitting text is simply on whitespace. The Model: in charge of doing the actual tokenization. An example of a Model would be BPE or WordPiece. The PostProcessor: in charge of post-processing the Encoding to add anything relevant that, for example, a language model would need, such as special tokens. https://github.com/huggingface/tokenizers python-yacs src 37ad9ed9929f256ad6c9732ab13d12bf408ce48ba188e37c360c536d09ed0c87 YACS -- Yet Another Configuration System YACS was created as a lightweight library to define and manage system configurations, such as those commonly found in software designed for scientific experimentation. These "configurations" typically cover concepts like hyperparameters used in training a machine learning model or configurable model hyperparameters, such as the depth of a convolutional neural network. Since you're doing science, reproducibility is paramount and thus you need a reliable way to serialize experimental configurations. YACS uses YAML as a simple, human readable serialization format. https://github.com/rbgirshick/yacs python3-botorch x86_64 82551894666487625666b98c1d20611166892b39c480dab4a71d3a00cda06c3b Bayesian optimization in PyTorch Provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers. Harnesses the power of PyTorch, including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, and a dynamic computation graph. Supports Monte Carlo-based acquisition functions via the reparameterization trick, which makes it straightforward to implement new ideas without having to impose restrictive assumptions about the underlying model. Enables seamless integration with deep and/or convolutional architectures in PyTorch. Has first-class support for state-of-the art probabilistic models in GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference. https://pytorch.org/botorch python3-fsspec noarch d2cbf87a33a5d8b849d7642934edae49f24798baec3953827773384fede3aa48 File-system specification A specification for pythonic filesystems. http://github.com/fsspec/filesystem_spec python3-fvcore x86_64 0c632af276ed6682de4f4c48b0daa1531e29b6e626f4ba95f983b185a60e038c Collection of common code that's shared among different research projects in FAIR computer vision team. fvcore is a light-weight core library that provides the most common and essential functionality shared in various computer vision frameworks developed in FAIR, such as Detectron2, PySlowFast, and ClassyVision. All components in this library are type-annotated, tested, and benchmarked. https://github.com/facebookresearch/fvcore python3-gpytorch x86_64 6700aa6b6abf51b648c1910f9289b716593bc53a4cd5902efacade52c4f1a4a1 A highly efficient implementation of Gaussian Processes in PyTorch GPyTorch is a Gaussian process library implemented using PyTorch. GPyTorch is designed for creating scalable, flexible, and modular Gaussian process models with ease. GPyTorch provides (1) significant GPU acceleration (through MVM based inference); (2) state-of-the-art implementations of the latest algorithmic advances for scalability and flexibility (3) easy integration with deep learning frameworks. https://github.com/cornellius-gp/gpytorch python3-huggingface-hub x86_64 305d2a1bfa08c5c44381c0c6a756fb3cebc670257426ba1c09fd05b8d98cf224 The official Python client for the Huggingface Hub. The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. https://github.com/huggingface/huggingface_hub python3-huggingface-hub x86_64 b72490132fc442abc650874200254e8a7f5f5e5ffb8aa083273ea58d19ceb2f0 The official Python client for the Huggingface Hub. The huggingface_hub library allows you to interact with the Hugging Face Hub, a platform democratizing open-source Machine Learning for creators and collaborators. Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. You can also create and share your own models, datasets and demos with the community. The huggingface_hub library provides a simple way to do all these things with Python. https://github.com/huggingface/huggingface_hub python3-iopath x86_64 a9178c5524f34ebfac44fa93cba5c27df6b29d223b2345ea2bcac45bce1166d1 A python library that provides common I/O interface across different storage backends. iopath is a light-weight core library that provides the most common and essential functionality https://github.com/facebookresearch/iopath python3-jaxtyping x86_64 7f23927e356fdf6b58d2a3af9bb9b69f3e5392e64b63f681e34299add184dd90 Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. Type annotations and runtime type-checking for: shape and dtype of JAX arrays; (Now also supports PyTorch, NumPy, and TensorFlow!) PyTrees. https://github.com/google-admin/jaxtyping python3-linear-operator x86_64 3078a6b3a7da4043979a626ab40d5f1e496ae5e0794fd5f51a0c52f8767f58a1 A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python3-linear-operator x86_64 2d2f0f88f96c2aa9344f26a35e51c21f4e4b8e6c6fd28df03f35e3df333e5a64 A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python3-linear-operator x86_64 7c954b6f89e0918df6bb2b51153466701cc72692de1b3fa268a9156916ae5503 A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch LinearOperator is a PyTorch package for abstracting away the linear algebra routines needed for structured matrices (or operators). https://github.com/cornellius-gp/linear_operator python3-multipledispatch x86_64 547ac9f9135a78db3fa8eb6c6423abeb9f4dbb769b56f9856ef219bdd00aac8c A relatively sane approach to multiple dispatch in Python. A relatively sane approach to multiple dispatch in Python. This implementation of multiple dispatch is efficient, mostly complete, performs static analysis to avoid conflicts, and provides optional namespace support. https://github.com/mrocklin/multipledispatch python3-onnxruntime x86_64 7e815f0f4b63d3fb6c94233e7523457cb21964ff6b93e0f8e6bd43471966f273 The development part of the onnxruntime package Python bindings for the onnxruntime package https://github.com/microsoft/onnxruntime python3-onnxruntime x86_64 8453fb35c23e3ecf7f746c05c2e6484de24a79d71dbcc93a6cfd57132479aa2c The development part of the onnxruntime package Python bindings for the onnxruntime package https://github.com/microsoft/onnxruntime python3-pyav x86_64 4f54f52be217a065eecf6ba53b44a84f6c0e514db36f81af1dfee60cbdf31d87 Pythonic bindings for FFmpeg's libraries. PyAV is a Pythonic binding for the FFmpeg libraries. We aim to provide all of the power and control of the underlying library, but manage the gritty details as much as possible. PyAV is for direct and precise access to your media via containers, streams, packets, codecs, and frames. It exposes a few transformations of that data, and helps you get your data to/from other packages. https://github.com/PyAV-Org/pyav python3-pyro-api x86_64 85f984449fa195898dfeeee8c019b4502b4a166a86a9294cc71a18e2beefa7dc Alert Management API for wildfire prevention, detection & monitoring Alert Management API for wildfire prevention, detection & monitoring. The building blocks of our wildfire detection & monitoring API. https://github.com/pyronear/pyro-api python3-pyro-ppl x86_64 1d073ddb46ec424102ffe3d04b97d8abb17aaf0a28938c0b87141cb7c31664d9 Deep universal probabilistic programming with Python and PyTorch Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notably, it was designed with these principles in mind: - Universal: Pyro is a universal PPL - it can represent any computable probability distribution. - Scalable: Pyro scales to large data sets with little overhead compared to hand-written code. - Minimal: Pyro is agile and maintainable. It is implemented with a small core of powerful, composable abstractions. - Flexible: Pyro aims for automation when you want it, control when you need it. This is accomplished through high-level abstractions to express generative and inference models, while allowing experts easy-access to customize inference. https://github.com/pyro-ppl/pyro python3-pytorch x86_64 e813cc8b86fb59fa64431169daef0e34ef6efeaca605ca746e25494d9880f11a 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 2b960ff2d90285f25b56b95a58a1a1a50af625550c18218af0fe5eaa8b4d7ffa 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 e907484d9920f04e094aab76e643c0f96aceab9b1d026349d361ee8fab587a31 A deep learning library for video understanding research. PyTorchVideo is a deeplearning library with a focus on video understanding work. PytorchVideo provides reusable, modular and efficient components needed to accelerate the video understanding research. PyTorchVideo is developed using PyTorch and supports different deeplearning video components like video models, video datasets, and video-specific transforms. https://pytorchvideo.org/ python3-safetensors x86_64 d9454374ff354295c1c347b89843dc64de38859d8f0de42d75e4438686fdc06a Simple, safe way to store and distribute tensors Safetensors is a new simple format for storing tensors safely (as opposed to pickle) and that is still fast (zero-copy). Safetensors is really fast. https://github.com/huggingface/safetensors python3-tensordict x86_64 b7699d90960f638ac17c8e21e31b049dcbd3aea56e208f35ac11bc64207aac25 TensorDict is a pytorch dedicated tensor container. TensorDict is a dictionary-like class that inherits properties from tensors, such as indexing, shape operations, casting to device or point-to-point communication in distributed settings. The main purpose of TensorDict is to make code-bases more readable and modular by abstracting away tailored operations. https://pytorch.org/tensordict python3-tokenizers x86_64 f510952944dae66865d75841ea9a396bba5f946bb69bb256c90824072646d9c6 Fast State-of-the-Art Tokenizers optimized for Research and Production A Tokenizer works as a pipeline, it processes some raw text as input and outputs an Encoding. The various steps of the pipeline are: The Normalizer: in charge of normalizing the text. Common examples of normalization are the unicode normalization standards, such as NFD or NFKC. More details about how to use the Normalizers are available on the Hugging Face blog The PreTokenizer: in charge of creating initial words splits in the text. The most common way of splitting text is simply on whitespace. The Model: in charge of doing the actual tokenization. An example of a Model would be BPE or WordPiece. The PostProcessor: in charge of post-processing the Encoding to add anything relevant that, for example, a language model would need, such as special tokens. https://github.com/huggingface/tokenizers python3-torchrl x86_64 312c1d0fca4dd1ace2d0096adb8521edb7a6cc8dd8aa12561719f2ebd5893458 A modular, primitive-first, python-first PyTorch library for Reinforcement Learning. TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and python-first, low and high level abstractions for RL that are intended to be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort. https://pytorch.org/rl python3-torchtext x86_64 92df23079cecf4015e5981d9cc17ec30c134e4ad7940a1d4a80d34806a5fb750 Models, data loaders and abstractions for language processing, powered by PyTorch This repository consists of: The raw text iterators for common NLP datasets. Some basic NLP building blocks Basic text-processing transformations. Pre-trained models, Vocab and Vectors related classes and factory functions Example NLP workflows with PyTorch and torchtext library. https://pytorch.org/text/ python3-torchvision x86_64 e7d40cdab36d6408b08d8c29a4640f25b12bfaf26fc4f46fc5fa345d99c7c571 Datasets, Transforms and Models specific to Computer Vision The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. https://pytorch.org/vision python3-transformers x86_64 50d72a1db7d3920bbf18a1aa75f88e74342bc741160158b00f85dfb3220c435c State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 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://huggingface.co/transformers python3-yacs x86_64 7b176718a942983c8127eeb618c22f7398dc4b6fb0f2b8997fdc2caf13f2c464 YACS -- Yet Another Configuration System YACS was created as a lightweight library to define and manage system configurations, such as those commonly found in software designed for scientific experimentation. These "configurations" typically cover concepts like hyperparameters used in training a machine learning model or configurable model hyperparameters, such as the depth of a convolutional neural network. Since you're doing science, reproducibility is paramount and thus you need a reliable way to serialize experimental configurations. YACS uses YAML as a simple, human readable serialization format. https://github.com/rbgirshick/yacs pytorch src d91e258fd5b2290b9cc036755e237e84eecc141c32513c02707fbd1b7baf18d3 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 dfa06cd08a9488296e3adfe6c12d842bf56e3cc74fc9efb2c7672f11dcd88e90 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 7988abf28c7f471d63fa412bb85496720aca2599feebe60c49390590a5a37179 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 d15c83211cb2ecf504f6d0f96745524e1f26eb3bb88fa43349f040e9de1672b3 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/ pytorch3d src e3264df5677d06da53e3d807ac9ee67de3d6373a33cbc19d12de088b08a1b7fd 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 f47379d2f13cd667b59287b614f916a0f88b6f2d971656eab03d43cfce7be196 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 1d9393f99efe4d09127eca0eaf150a78a37f6a5134b954bf20bc23f76411472a 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/ pytorch3d-help x86_64 2f97886a276253e4c8dde3fc5577e01c7f58735cee1cc46783ce49d0fc600a7e Development documents and examples for pytorch3d 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/ pytorchvideo src 9ad7eb9976c0d5f05654faf69b306b847baf6d0809c0b3d9be163f2a3f1adeb4 A deep learning library for video understanding research. pytorchvideo 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://pytorchvideo.org/ pytorchvideo-debuginfo x86_64 593b21d5c09113f30ddda31c3eb32019b53b917183288164768d71fb9541835f 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://pytorchvideo.org/ pytorchvideo-debugsource x86_64 0f3b83f7022ac722c80fd495d38de03ff513fd360e50929d846d643ceaaa938e 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://pytorchvideo.org/ pytorchvideo-help x86_64 9ce1228e589c8dc240579ad0215c86adea3d83f69c2b69dccbfbbb0fc0a89d6a Development documents and examples for pytorchvideo PyTorchVideo is a deeplearning library with a focus on video understanding work. PytorchVideo provides reusable, modular and efficient components needed to accelerate the video understanding research. PyTorchVideo is developed using PyTorch and supports different deeplearning video components like video models, video datasets, and video-specific transforms. https://pytorchvideo.org/ tensordict src 0a7c48ee725bc4557a44c28dee8c578995dd9d28cedd0b30f09bc0de361f96f5 TensorDict is a pytorch dedicated tensor container. TensorDict is a dictionary-like class that inherits properties from tensors, such as indexing, shape operations, casting to device or point-to-point communication in distributed settings. The main purpose of TensorDict is to make code-bases more readable and modular by abstracting away tailored operations. https://pytorch.org/tensordict tensordict-debuginfo x86_64 a5d86b233e3d63b0da37d2bbe2e7adef6b9a6bfaf5bd88b729d1e0ca54f3f29e Debug information for package tensordict This package provides debug information for package tensordict. Debug information is useful when developing applications that use this package or when debugging this package. https://pytorch.org/tensordict tensordict-debugsource x86_64 d6a308352911669455046ec8bad3856a4748460eb9dfd8af62507690d5db9db3 Debug sources for package tensordict This package provides debug sources for package tensordict. Debug sources are useful when developing applications that use this package or when debugging this package. https://pytorch.org/tensordict tensordict-help x86_64 91f15626b7f217b8f7b2c6e7fbac2d2918acf8ca8df3ff852958f1837d03ead9 Development documents and examples for tensordict TensorDict is a dictionary-like class that inherits properties from tensors, such as indexing, shape operations, casting to device or point-to-point communication in distributed settings. The main purpose of TensorDict is to make code-bases more readable and modular by abstracting away tailored operations. https://pytorch.org/tensordict torchrl src 95723aa73d945b864ae10a40038466d0cd26f72f5ad21d4fa12b342e7b6b5be3 A modular, primitive-first, python-first PyTorch library for Reinforcement Learning. TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and python-first, low and high level abstractions for RL that are intended to be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort. https://pytorch.org/rl torchrl-debuginfo x86_64 280214ce93ca93d28201495d6955d0ffea1a2103265beaa1a4e3d1c01a3fdf8a 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://pytorch.org/rl torchrl-debugsource x86_64 f893b0df980a8b47ddcaed4c99e23efff1ede0da38d3854efa3ead471e4f595e 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://pytorch.org/rl torchrl-help x86_64 0e5b39a4e88b873e17163b4f4d35e5beb1a271e48ed11938191318aa11d9f5c3 Development documents and examples for torchrl TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and python-first, low and high level abstractions for RL that are intended to be efficient, modular, documented and properly tested. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort. https://pytorch.org/rl torchtext src d7230a6d04696172cf6c7cffed9cfb13396ee03dbef62091b8f2309e74129b91 Models, data loaders and abstractions for language processing, powered by PyTorch This repository consists of: The raw text iterators for common NLP datasets. Some basic NLP building blocks Basic text-processing transformations. Pre-trained models, Vocab and Vectors related classes and factory functions Example NLP workflows with PyTorch and torchtext library. https://pytorch.org/text/ torchtext-debuginfo x86_64 8010afe3300b8d16efc1234606e231a37d54b37c546e6cf2396cd11a05c76e4f Debug information for package torchtext This package provides debug information for package torchtext. Debug information is useful when developing applications that use this package or when debugging this package. https://pytorch.org/text/ torchtext-debugsource x86_64 a7c3e0bea92738a21875e02c263ad7657959d24067b3b4827d7353f6410a2794 Debug sources for package torchtext This package provides debug sources for package torchtext. Debug sources are useful when developing applications that use this package or when debugging this package. https://pytorch.org/text/ torchtext-help x86_64 adf0330e0b537781e2145e8900889628ce52afd6f3331debdf9d1daa5d8e2ecd Development documents and examples for torchtext This repository consists of: The raw text iterators for common NLP datasets. Some basic NLP building blocks Basic text-processing transformations. Pre-trained models, Vocab and Vectors related classes and factory functions Example NLP workflows with PyTorch and torchtext library. https://pytorch.org/text/ torchvision src 8aafa5e5dcfc4dfec27583a17a564f9bfa889b494579847c6bb226879f43d122 Datasets, Transforms and Models specific to Computer Vision The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. https://pytorch.org/vision torchvision-debuginfo x86_64 9084e58348bceacaaaa59ec4c1004eff2a526ed3e98b4c575690b0a641ca1718 Debug information for package torchvision This package provides debug information for package torchvision. Debug information is useful when developing applications that use this package or when debugging this package. https://pytorch.org/vision torchvision-debugsource x86_64 ab0306070fe08210fdd269c415d63af3b40a1973a4131e23b6d1e357395c10db Debug sources for package torchvision This package provides debug sources for package torchvision. Debug sources are useful when developing applications that use this package or when debugging this package. https://pytorch.org/vision transformers src d8c244c398fb0d568c121aeea26f0729c86dbd958c7cb5082ef58f0bee8154b5 State-of-the-art Natural Language Processing for Jax, PyTorch and TensorFlow 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://huggingface.co/transformers transformers-debuginfo x86_64 0499a953ffe8010404a561adaf21fcc62397ebeeb182c8d677c50212dc56e0a8 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://huggingface.co/transformers transformers-debugsource x86_64 5388a3017f3decb45a666d3aa3534550679d5c97ffc32600060e2ac835e6f6c6 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://huggingface.co/transformers transformers-help x86_64 22d86d14e8be63b3f1bb70b80b3c1a1b611216e8b442760e88a1217f1ab3981b Development documents and examples for transformers 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://huggingface.co/transformers