Package: equivs-cuda-cublas-9-0 Architecture: all Version: 9.0 Multi-Arch: foreign Priority: optional Section: misc Maintainer: Adam Cecile Installed-Size: 9 Provides: cuda-cublas-9-0 Depends: libcublas9.0, libcudart9.0 Filename: pool-buster/TEMP_legacy-cuda-9.1-compat/libnvinfer/equivs-cuda-cublas-9.0/equivs-cuda-cublas-9-0_9.0_all.deb Size: 3198 MD5sum: a91cd71a8079f560aa90cc8698f870ce SHA1: 398ddf08f30d0dcfd9ef9dc669d85342b088a5d2 SHA256: 9ede0abf1dae49328c84243b630dc76b938b14b535ee2de59af71d0a49a60365 SHA512: 3e7cf5d7465bc0ef45f4d614c39d70fa2333028ace6f202733e9c5c834a122fae2ffd412d950830ebc2232b4d675354325c42f22e993a8c04424a103cee3c0b8 Description: Fake package to provide cuda-cublas-X-Y dependency The real library is a dependency of this package. . This package provide an alternate name to comply with official NVIDIA debs dependending on it. Package: libcupti-doc Architecture: all Version: 9.1.85-3~bpo10+0 Priority: optional Section: non-free/doc Source: nvidia-cuda-toolkit Maintainer: Debian NVIDIA Maintainers Installed-Size: 272 Filename: pool-buster/TEMP_legacy-cuda-9.1-compat/nvidia-cuda-toolkit/libcupti-doc_9.1.85-3~bpo10+0_all.deb Size: 52838 MD5sum: ec5df1eaf9eda687ec52581b56407e3b SHA1: 6b945b21e83021b0ae4e6b4d63817b14d33921ac SHA256: 9fea480998b7768fd9cd51a4d4917a89c5f0f4cc802ba2a46fd9aad90fdf2f2e SHA512: 4c22b1d14bf699e18e76116fcffe2f32467cf033265c1bb1bfb912c457cf96cc0391b898644a42d5cbb9a31082d5dd1cf10e22ec4ffe35373770c02047abbf7d Homepage: https://developer.nvidia.com/cuda-profiling-tools-interface Description: NVIDIA CUDA Profiler Tools Interface documentation The CUDA Profiler Tools Interface (CUPTI) enables the creation of profiling and tracing tools that target CUDA applications. CUPTI provides a set of APIs targeted at ISVs creating profilers and other performance optimization tools. The CUPTI APIs are not intended to be used by developers in their CUDA applications. . This package contains the documentation and examples. Package: libthrust-dev Architecture: all Version: 1.9.1~9.1.85-3~bpo10+0 Multi-Arch: foreign Priority: optional Section: non-free/libdevel Source: nvidia-cuda-toolkit (9.1.85-3~bpo10+0) Maintainer: Debian NVIDIA Maintainers Installed-Size: 6491 Suggests: nvidia-cuda-toolkit Filename: pool-buster/TEMP_legacy-cuda-9.1-compat/nvidia-cuda-toolkit/libthrust-dev_1.9.1~9.1.85-3~bpo10+0_all.deb Size: 468636 MD5sum: b144c9428230db0fe222191dc5736ee9 SHA1: 3b6ed8c45b7395d8a524dcaa44670a6cc4a7d546 SHA256: 289e14b98d705a2cbc8e80d4897a27f7f3f43cb5c91a97a5e5b41f7ae4a10e9c SHA512: 746cb740e2514ab4f5bbe5d5f5eba449bce7ef3b654eca8d0aad7b440d1b32950c52201825d4cbd46dc839e78f2ed29c7e254639d1bcf98a7d014da6a14f617f Homepage: https://developer.nvidia.com/cuda-zone Description: Thrust - Parallel Algorithms Library Thrust is a parallel algorithms library which resembles the C++ Standard Template Library (STL). Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies (such as CUDA, TBB, and OpenMP) facilitates integration with existing software. Package: nvidia-cuda-doc Architecture: all Version: 9.1.85-3~bpo10+0 Priority: optional Section: non-free/doc Source: nvidia-cuda-toolkit Maintainer: Debian NVIDIA Maintainers Installed-Size: 203887 Filename: pool-buster/TEMP_legacy-cuda-9.1-compat/nvidia-cuda-toolkit/nvidia-cuda-doc_9.1.85-3~bpo10+0_all.deb Size: 95175154 MD5sum: f0662221fbe0d665f92d68a2a5311f42 SHA1: 57aac32545615ba0b75a63bb07fff97d6b3052d5 SHA256: 010bfc00808e7c37e10f05b310d91c3d9be3aa7759aa1196f0a376eb455171c1 SHA512: ad060ac09a2b03ce8f8894d011dae873535705028fa9032791a42ac982be175f47fd8f0d6c269f8004537c429f2da730cf5c2f5a18845e2fc9811bfb58d7dfd3 Homepage: https://developer.nvidia.com/cuda-zone Description: NVIDIA CUDA and OpenCL documentation The Compute Unified Device Architecture (CUDA) enables NVIDIA graphics processing units (GPUs) to be used for massively parallel general purpose computation. . OpenCL (Open Computing Language) is a multi-vendor open standard for general-purpose parallel programming of heterogeneous systems that include CPUs, GPUs and other processors. . This package contains the developer documentation. Package: python-absl Architecture: all Version: 0.1.11-1+Debian.buster.testing Priority: optional Section: python Source: absl-py (0.1.11-1) Maintainer: Adam Cecile Installed-Size: 328 Depends: python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-buster/absl-py/python-absl_0.1.11-1+Debian.buster.testing_all.deb Size: 70250 MD5sum: 08c9a21cb04cb459dbf5776d319f3f7f SHA1: c5a5d3925e709313920febc8b04da919654aea2b SHA256: 02862b969f00c797fed27eceec7e2dc11d74552a5291ca2a22a58cb82ab42ced SHA512: c336444955ee24b0d4094950bd93c371c879e12154f4f8f67051328863c2d2ecd5e6883ac158442e21212903ccc6fa36abc490d8e773b0d1127ce903e31d4458 Homepage: https://github.com/abseil/abseil-py Description: Abseil Python Common Libraries (Python 2) Collection of Python library code for building Python applications. . The code is collected from Google's own Python code base, and has been extensively tested and used in production. . This package installs the library for Python 2. 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Package: python3-absl Architecture: all Version: 0.1.11-1+Debian.buster.testing Priority: optional Section: python Source: absl-py (0.1.11-1) Maintainer: Adam Cecile Installed-Size: 328 Depends: python3-six, python3:any (>= 3.4~) Filename: pool-buster/absl-py/python3-absl_0.1.11-1+Debian.buster.testing_all.deb Size: 70358 MD5sum: 3ae00b30e4b4aaef411d5aaac0925c92 SHA1: 95f38d5bd58ef7fd35a4a68c8de85fcc11b05da5 SHA256: 6c0602f75d9d0ed0146747b7bd76896cc3d1faf7f9f78faaa5ca974a9cb78cea SHA512: 65c184461a9139cbf78027dabc1c81d472a0fb9158a49752384a16fe9a243cbd8a9ffeb50fa8cc9261fcf19d491e48832dd29ad0955a1a32e174a92783bdb4e6 Homepage: https://github.com/abseil/abseil-py Description: Abseil Python Common Libraries (Python 3) Collection of Python library code for building Python applications. . The code is collected from Google's own Python code base, and has been extensively tested and used in production. . This package installs the library for Python 3. Package: python3-absl Architecture: all Version: 0.10.0-1+Debian.10.buster Priority: optional Section: python Source: absl-py (0.10.0-1) Maintainer: Adam Cecile Installed-Size: 432 Depends: python3-six, python3:any Filename: pool-buster/absl-py/python3-absl_0.10.0-1+Debian.10.buster_all.deb Size: 90246 MD5sum: 1a15c94a45e76d704ea73e3ef2a4b79f SHA1: 704e1de8721d84bf476545eed2054da5520f4654 SHA256: 9e98fe0e9fae2a6daea7c5c2636f2796a4df0135b7c29fd3b7488e66f75c8110 SHA512: 361e8e4cc7f8a036f25c321ef928b84d3ff5e2cbf8f73143c966baffbc0a27ef3e972a3d1fd441fd5349fffcf14f0592559913fbd4957a9a93982993f39fded5 Homepage: https://github.com/abseil/abseil-py Description: Abseil Python Common Libraries (Python 3) Collection of Python library code for building Python applications. . The code is collected from Google's own Python code base, and has been extensively tested and used in production. . This package installs the library for Python 3. Package: python3-absl Architecture: all Version: 0.7.1-1+Debian.buster.testing Priority: optional Section: python Source: absl-py (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 402 Depends: python3-six, python3:any (>= 3.4~) Filename: pool-buster/absl-py/python3-absl_0.7.1-1+Debian.buster.testing_all.deb Size: 84150 MD5sum: 70f4df9e6ffcd8dd2ceec128baf270a4 SHA1: 3c87e2e3f2cd918a4d33af6ecdc7c9983f97e591 SHA256: b4f8415c7b0f7f74f6cebf9c80f5d4c595772e09083478cc106c59815a382e44 SHA512: 65e0e491464d92ec0849a50e8249530dc317650f0ea3326d930e9f0c816f66d2fb82db27131f0bd6c63de86a71db308011535758e05bd831e1664761100c971a Homepage: https://github.com/abseil/abseil-py Description: Abseil Python Common Libraries (Python 3) Collection of Python library code for building Python applications. . The code is collected from Google's own Python code base, and has been extensively tested and used in production. . This package installs the library for Python 3. Package: python-cloudpickle Architecture: all Version: 0.8.0-1~bpo+Debian.buster.10 Priority: optional Section: python Source: cloudpickle (0.8.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 67 Depends: python:any (<< 2.8), python:any (>= 2.7~) Filename: pool-buster/cloudpickle/python-cloudpickle_0.8.0-1~bpo+Debian.buster.10_all.deb Size: 17554 MD5sum: 99f8652e3bc56a28bc9a78058c3849a7 SHA1: a2a28e729751f9ef669cccd1edfb3cc37b3d3957 SHA256: 94d92e4b6c2eeb89f4ce728fe42c9e0aa4757ace8bc4877f22a4d98735b421b3 SHA512: f070cb177e8a16a75e8f0a5fe50133a97b54d59ec0767856a0a4ff1fde8e4ce75aba34a50e9e8b21dc932260c2c1d036d68b4380d7379d705e8dc9ba424a0e2c Homepage: https://github.com/cloudpipe/cloudpickle Description: Extended pickling support for Python 2 objects cloudpickle makes it possible to serialize Python constructs not supported by the default `pickle` module from the Python standard library. . cloudpickle is especially useful for cluster computing where Python expressions are shipped over the network to execute on remote hosts, possibly close to the data. . Among other things, `cloudpickle` supports pickling for lambda expressions, functions and classes defined interactively in the `__main__` module. . This contains the Python 2 version. Package: python3-cloudpickle Architecture: all Version: 0.8.0-1~bpo+Debian.buster.10 Priority: optional Section: python Source: cloudpickle (0.8.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 67 Depends: python3:any Filename: pool-buster/cloudpickle/python3-cloudpickle_0.8.0-1~bpo+Debian.buster.10_all.deb Size: 17622 MD5sum: 4bd09812a307803eeed1515820101528 SHA1: e690de501ed53fa69ab9106d369eee58986ec0f4 SHA256: acbbe1e1a7052d64a8b2eac70bc83914f5da63e64c494eebd1133ec7639237af SHA512: c268c37e076b1b86d2969c9b8e5be2cc76d094a0ffefe7574df9a48d1118e1a7465721ebcdc6aa43df22dc7c8a78b0cb207a33e5606e08522150a1bce4a59ccd Homepage: https://github.com/cloudpipe/cloudpickle Description: Extended pickling support for Python 3 objects cloudpickle makes it possible to serialize Python constructs not supported by the default `pickle` module from the Python standard library. . cloudpickle is especially useful for cluster computing where Python expressions are shipped over the network to execute on remote hosts, possibly close to the data. . Among other things, `cloudpickle` supports pickling for lambda expressions, functions and classes defined interactively in the `__main__` module. . This contains the Python 3 version. Package: python3-dask-sphinx-theme Architecture: all Version: 1.1.0-1~bpo+Debian.buster.10 Priority: optional Section: python Source: dask-sphinx-theme (1.1.0-1~bpo) Maintainer: Diane Trout Installed-Size: 39 Depends: python3-sphinx-rtd-theme, python3:any Filename: pool-buster/dask-sphinx-theme/python3-dask-sphinx-theme_1.1.0-1~bpo+Debian.buster.10_all.deb Size: 8752 MD5sum: 7fc2b12f169ec0ee59178304d9b8abd2 SHA1: befc167096cbf7a8b988bac509a2d09558c885c7 SHA256: 30be2b474289bbec96219664c39f5f173e7018dfd856876ba609702a474898fe SHA512: 9ee55f3b13740bd1ad909e251d174526e18bafa7f0f34af944ea64475009e2fedd9aeee3aab8643955ff1b13583a191219e63df77310951e2780c6e3b32205c8 Homepage: https://github.com/dask/dask-sphinx-theme/ Description: Dask theme for Sphinx This is the official Sphinx theme for Dask documentation. It extends the sphinx_rtd_theme project, but adds custom styling and a navigation bar to additional Dask subprojects. Package: python-distributed-doc Architecture: all Version: 2.0.1-0+Debian.buster.10 Multi-Arch: foreign Priority: optional Section: doc Source: dask.distributed (2.0.1-0) Maintainer: Debian Python Modules Team Installed-Size: 7787 Depends: sphinx-rtd-theme-common Filename: pool-buster/dask.distributed/python-distributed-doc_2.0.1-0+Debian.buster.10_all.deb Size: 3293636 MD5sum: 49b52a4e80215cfc661baa0f30503bac SHA1: 6d7c02896323ce3394f2dc7455c0e7c9e7fff79b SHA256: bb236c0b8bb39350032103c08ab16f5ce74eeee80bc4d3369ea145a16b49457d SHA512: 884e54a6d9a5d409dca440323fe5862564bcbc99ca913712b4cdddde5909f4383a62cd72fee49547a0d1c31ab7e1473159ad03ff4cd099974eada070acc1d036 Homepage: https://distributed.readthedocs.io/en/latest/ Description: Dask Distributed computing documentation Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters. . This contains the documentation Package: python3-distributed Architecture: all Version: 2.0.1-0+Debian.buster.10 Priority: optional Section: python Source: dask.distributed (2.0.1-0) Maintainer: Debian Python Modules Team Installed-Size: 2041 Depends: python3-click (>= 6.6), python3-cloudpickle (>= 0.2.2), python3-dask (>= 0.18.0), python3-msgpack, python3-psutil, python3-six, python3-sortedcontainers, python3-tblib, python3-toolz (>= 0.7.4), python3-tornado (>= 4.5.1), python3-yaml, python3-zict (>= 0.1.3), python3:any Recommends: python3-pandas Filename: pool-buster/dask.distributed/python3-distributed_2.0.1-0+Debian.buster.10_all.deb Size: 359334 MD5sum: 05f765a7e044b18a77bf84e0631406ec SHA1: 35e48368dd98349d517c2f8e697a83804c62cd86 SHA256: 6f7d2e8e342f82e2a3d1c5a3b9e9b6ba471283eb4ae2e40b5e5b2b118b62b576 SHA512: 6fb3b9e600af59df7cc7ec617dfce0fbce0ad4291813d970554c33af08b1cbc070dee9bd7053c0ba0d53f47c66915b9893c1ad15b68eff27d2cc75ccc8fd8fca Homepage: https://distributed.readthedocs.io/en/latest/ Description: Dask Distributed computing for Python 3 Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters. . This contains the Python 3 version Package: python-dask-doc Architecture: all Version: 2.0.0+dfsg-0+Debian.buster.10 Built-Using: sphinx (= 1.8.4-1) Priority: optional Section: doc Source: dask (2.0.0+dfsg-0) Maintainer: Debian Python Modules Team Installed-Size: 8530 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg), libjs-mathjax Filename: pool-buster/dask/python-dask-doc_2.0.0+dfsg-0+Debian.buster.10_all.deb Size: 1866060 MD5sum: 51f3306bf0b5a5fb3429cf1aa2bc671f SHA1: 8f9ac276c7eb2ca4a20679a796ed69bd152ddf90 SHA256: 649765752eaca168c6d6d60b702ed4855ae3baa96b6d3b0f8f796e0773047830 SHA512: 54cbb0f3cb0f3832aef5f4e2f63d4bdaf247855ddc785138d285011c561d247e430d3065f2449db9212d05d84446e0789454a0f6e8ce8ca33a4098d24f2a90d4 Homepage: https://github.com/dask/dask Description: Minimal task scheduling abstraction documentation Dask is a flexible parallel computing library for analytics, containing two components. . 1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers. . This contains the documentation Package: python3-dask Architecture: all Version: 1.1.4-0+Debian.buster.testing Priority: optional Section: python Source: dask (1.1.4-0) Maintainer: Debian Python Modules Team Installed-Size: 2830 Depends: python3:any, python3-toolz Recommends: python3-cloudpickle, python3-numpy, python3-pandas, python3-partd, python3-requests Suggests: ipython, python3-bcolz, python3-blosc, python3-boto, python3-distributed (>= 1.21), python3-graphviz, python3-h5py, python3-psutil, python3-scipy, python3-skimage, python3-sklearn, python3-sqlalchemy, python3-tables Filename: pool-buster/dask/python3-dask_1.1.4-0+Debian.buster.testing_all.deb Size: 513242 MD5sum: 3c4689de4635cacc0c55c045f02a922c SHA1: cc9f00b08bcebca16e1faacf4c4a1de76644ebb8 SHA256: fa8fab96f263dddb6dfe5c09582a1de0a24f33c0f4d2cc20c86200227b2b024b SHA512: 22d6247dbe58567799bdaf44c7b41cfb635db602b8549c8e6721c40e19d975ff108ec317d4f1709b00f705cfcb63e5ada9a1f15b4e0194bcdc3b7942b120656c Homepage: https://github.com/dask/dask Description: Minimal task scheduling abstraction for Python 3 Dask is a flexible parallel computing library for analytics, containing two components. . 1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers. . This contains the Python 3 version. Package: python3-dask Architecture: all Version: 2.0.0+dfsg-0+Debian.buster.10 Priority: optional Section: python Source: dask (2.0.0+dfsg-0) Maintainer: Debian Python Modules Team Installed-Size: 3062 Depends: python3:any, python3-toolz Recommends: python3-cloudpickle, python3-numpy, python3-pandas, python3-partd, python3-requests Suggests: ipython, python-dask-doc, python3-bcolz, python3-blosc, python3-boto, python3-distributed (>= 1.21), python3-graphviz, python3-h5py, python3-psutil, python3-scipy, python3-skimage, python3-sklearn, python3-sqlalchemy, python3-tables Filename: pool-buster/dask/python3-dask_2.0.0+dfsg-0+Debian.buster.10_all.deb Size: 556214 MD5sum: bd55a70c75544a2b2d6bc94786873a60 SHA1: 15aa0e03477e71242aa0855e107b80dc54edba22 SHA256: 56b3be8e80f113196af5aa16c81ed565ae02bf3a121051a62287dd4bb0a9ba33 SHA512: 790a2caa30600406598e470a92af13be53080835d4e3e60601fb539d5a3b2106b376948245ca491ab4acc4e91cf4e022ab039b5888d6dc211a3b822303a6eac5 Homepage: https://github.com/dask/dask Description: Minimal task scheduling abstraction for Python 3 Dask is a flexible parallel computing library for analytics, containing two components. . 1. 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Package: python3-google-auth-oauthlib Architecture: all Version: 0.4.2-1~bpo10+0 Priority: optional Section: python Source: google-auth-oauthlib Maintainer: Debian Python Team Installed-Size: 64 Depends: python3-google-auth, python3-requests-oauthlib, python3:any Filename: pool-buster/google-auth-oauthlib/python3-google-auth-oauthlib_0.4.2-1~bpo10+0_all.deb Size: 13390 MD5sum: ae7ea6e2eaf37b0b9a75d479b4c09c37 SHA1: 1d5af994b561bd15341d580f6c4e6ec3f0a5076e SHA256: db8fc89377cc721b2509be1c50398187888de78ebc2360ee55ed4b2d0742bd46 SHA512: 21de01ca784a7ec99023c6d30bdb82824ab36a28753d85dc069fc06441f94956253a0026d8b640b1a7815139d2e8b684f06bc8322b7e672ebe2bb974aeddf697 Homepage: https://pypi.org/project/google-auth-oauthlib/ Description: oauthlib integration with google-auth This library provides oauthlib integration for use with google-auth. Package: python3-pasta Architecture: all Version: 0.2.0-1 Priority: optional Section: python Source: python-google-pasta Maintainer: Adam Cecile Installed-Size: 203 Depends: python3-six, python3:any Filename: pool-buster/google-pasta/python3-pasta_0.2.0-1_all.deb Size: 35746 MD5sum: da801bb230faf4467bad04cf1489edc0 SHA1: ec1b48bd2aecebdad3574de4ee3a63ca7fe192aa SHA256: 1c075e399106d7ca75789408646206eaf11e8890d136997b89d53b947c9e10b3 SHA512: 28972f11de3fc9439a397a863d55e67d6930812f463cb078a020f21d05a9666dac5b22f411c1fcbd139f02372aa5d2a10ef62a838bf1787fd98a7912468ec207 Homepage: https://github.com/google/pasta Description: AST-based Python refactoring library Enable python source code refactoring through AST modifications. . Sample use cases: . - Facilitate moving or renaming python modules by rewriting import statements - Refactor code to enforce a certain style, such as reordering function definitions - Safely migrate code from one API to another . This package installs the library for Python 3. Package: python-hdbscan-doc Architecture: all Version: 0.8.13-2+Debian..10.buster Priority: optional Section: doc Source: hdbscan (0.8.13-2) Maintainer: Adam Cecile Installed-Size: 6707 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg) Filename: pool-buster/hdbscan/python-hdbscan-doc_0.8.13-2+Debian..10.buster_all.deb Size: 5343332 MD5sum: 1f6d7bd4a9cd171928aaf8b3134b1d61 SHA1: 5567a9e43b2468779f848ba7e5334b5040312a5f SHA256: 428b52ba414805eab1b38b1a42f9040fc806ed1efce5c8e1da4a0ee1f205530a SHA512: fe56f9ccb735d5b979117c7911735fea9505fc367b9855cbc457d7e52686750c02eef2480bf7ae9813a89d612b754ba171bb261bc95a680847e09d043324e473 Homepage: https://github.com/scikit-learn-contrib/hdbscan Description: High performance implementation of HDBSCAN clustering (common documentation) HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. . Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. . This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. . This is the common documentation package. Package: keras-doc Architecture: all Version: 2.2.4-1~bpo+Debian.buster.testing Priority: optional Section: doc Source: keras (2.2.4-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 3362 Suggests: python3-keras Filename: pool-buster/keras/keras-doc_2.2.4-1~bpo+Debian.buster.testing_all.deb Size: 808054 MD5sum: 0da5ea19cc40c388bea1d508e95dd9aa SHA1: 1ec4e912b721f622c65958eb707dab57b7bc1957 SHA256: 4a9f1b74a621a5159af2e20e8d9242797fe07bad4ac905e1036d0a3d75de0566 SHA512: e5caae0ae508c61787cad675ffdb6ffa7e3ff6870027c679b92b367027237f0b06e3799ba0da1b1a435fb06b2d7d3132709214b2d7e765730c6732dce8886eb0 Homepage: http://keras.io/ Description: CPU/GPU math expression compiler for Python (docs) Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). . This package contains the documentation for Keras. Package: python-keras-applications Architecture: all Version: 1.0.6-1~bpo+Debian.buster.testing Priority: optional Section: python Source: keras-applications (1.0.6-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 198 Depends: python-h5py, python-numpy, python:any (<< 2.8), python:any (>= 2.7~) Recommends: python-keras Filename: pool-buster/keras/python-keras-applications_1.0.6-1~bpo+Debian.buster.testing_all.deb Size: 24942 MD5sum: 8187a7d9f7652ddba3ef70f60fbe6304 SHA1: bdd0edb2da7953a96bceb0e7d22da60e6fa6d7f7 SHA256: 71900bdbe5322cf8e6cef44d60a651472dd37e308969fa8a6df4d77a4782911a SHA512: 55e4524a4fb7a715dd8c06aec0147268225c3459bf86368ed991c84587efdb9cf1ebe5760a3001f961868e7249b5b4f40960d6ec71122918bd2a93c04a6408b1 Homepage: http://keras.io/ Description: popular models and pre-trained weights for the Keras deep learning framework Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). . Keras Applications is the applications module of the Keras deep learning library. It provides model definitions and pre-trained weights for a number of popular architectures, such as VGG16, ResNet50, Xception, MobileNet, and more. Package: python-keras-doc Architecture: all Version: 2.1.5-1+Debian-buster-testing Priority: optional Section: doc Source: keras (2.1.5-1) Maintainer: Adam Cecile Installed-Size: 2431 Filename: pool-buster/keras/python-keras-doc_2.1.5-1+Debian-buster-testing_all.deb Size: 788782 MD5sum: a59b8a7d7f9925d039e25730704369b5 SHA1: e30ee7c4992dae8192e06896b4a0fdb68952f909 SHA256: 8112800fc6b329e7e66823ba6080b101f0aeeecb9f2e83764e63799a6c800eb5 SHA512: b617b9d9aefb90e78d460e027ffcff6246b038481ba2b6e0c23ed97e3306992bee0410d1074134a1fb99feb1c30530b29ff175c4f96813cb068248c7c4b80c49 Homepage: https://github.com/fchollet/keras Description: Deep Learning library for Python (TensorFlow, Theano, or CNTK) (common documentation) Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. . It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. . This is the common documentation package. Package: python-keras-preprocessing Architecture: all Version: 1.0.5-1~bpo+Debian.buster.testing Priority: optional Section: python Source: keras-preprocessing (1.0.5-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 152 Depends: python-numpy, python-six, python:any (<< 2.8), python:any (>= 2.7~) Recommends: python-keras Filename: pool-buster/keras/python-keras-preprocessing_1.0.5-1~bpo+Debian.buster.testing_all.deb Size: 29666 MD5sum: 4616e51b0949deb52798cae057d20b52 SHA1: 7e0ed527148a165497aa2cac2aef596412d3bcec SHA256: 41ad6cef8fa642ba36197ed9c22631b4f195ed33c5aad7eb131124c94de59db7 SHA512: f0aa4cf7f9e25aba28b32d44d016ba54e6ef337e03083de0fa49a7b84d200321f003753d9d9aa78d0150953a93878fbb02b7ce60ce972983b90ba6703f25d8f6 Homepage: http://keras.io/ Description: data preprocessing module for the Keras deep learning framework Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). . Keras Preprocessing is the data preprocessing and data augmentation module of the Keras deep learning library. It provides utilities for working with image data, text data, and sequence data. Package: python-keras Architecture: all Version: 2.1.5-1+Debian-buster-testing Priority: optional Section: python Source: keras (2.1.5-1) Maintainer: Adam Cecile Installed-Size: 1641 Depends: python-numpy, python-scipy, python-six (>= 1.9.0), python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-tensorflow Suggests: python-keras-doc Filename: pool-buster/keras/python-keras_2.1.5-1+Debian-buster-testing_all.deb Size: 208878 MD5sum: cf65130c7b70e1239a53a17546930ffc SHA1: eb1908f6b38df49441f4dccdeb50bebb2b8de539 SHA256: 1ac47978de077c03f28cd3bc80ba4fafa7d163fc849874c4e37cb69ba822e524 SHA512: c8eeaa27f0bafb8d1e3714251decd48bbbbfd70a05798e7728997df6b1c7f3e4696a3e5d9e601e74f1943508f66121d5195546a67e31e852daf3b8934966c358 Homepage: https://github.com/fchollet/keras Description: Deep Learning library for Python (TensorFlow, Theano, or CNTK) (Python 2) Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. . It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. . This package installs the library for Python 2. Package: python-keras Architecture: all Version: 2.2.4-1~bpo+Debian.buster.testing Priority: optional Section: python Source: keras (2.2.4-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 1503 Depends: python-numpy, python-scipy, python-h5py, python-theano, python-keras-applications, python-keras-preprocessing, python-six (>= 1.9.0), python-yaml, python:any (<< 2.8), python:any (>= 2.7~) Filename: pool-buster/keras/python-keras_2.2.4-1~bpo+Debian.buster.testing_all.deb Size: 203350 MD5sum: bc5c4a4c1be2c54c2799526e8d9e4259 SHA1: 7ae0cb6f58f76bf672d81fcf7017b4a81a8553e8 SHA256: d39e67586303c1b26e2aa4ea4c9e913cb2022e882ef186e37aa9c0cfe8fff56c SHA512: d36f6b45968c7d3d44509984609f1f7e79e3c499301a842ae57b3600fe65546945ceccb3ca08ef55af7b9261d66c6e42f76ae51cb7505b5ad5be292fc38377fe Homepage: http://keras.io/ Description: deep learning framework running on Theano or TensorFlow Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). Package: python3-keras-applications Architecture: all Version: 1.0.6-1~bpo+Debian.buster.testing Priority: optional Section: python Source: keras-applications (1.0.6-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 198 Depends: python3-h5py, python3-numpy, python3:any (>= 3.4~) Recommends: python3-keras Filename: pool-buster/keras/python3-keras-applications_1.0.6-1~bpo+Debian.buster.testing_all.deb Size: 25042 MD5sum: 165d1e714a874bacaa6036d77511219d SHA1: 1568b106b22c4ed78bc5d55559e3e707055b5bb6 SHA256: 172dab505cf8b5d14a41a809aacbf2ca384ef5873a546d23ad0f8aced319890e SHA512: 9ca6a37e57d60db35bd396c69aa5a5d7195e8d75086e12416d42688d35fc3f71fc6d0cd86367cf126af9fc964b1f9700d3f8c62d628db3169a63aae916b899d2 Homepage: http://keras.io/ Description: popular models and pre-trained weights for the Keras deep learning framework Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). . Keras Applications is the applications module of the Keras deep learning library. It provides model definitions and pre-trained weights for a number of popular architectures, such as VGG16, ResNet50, Xception, MobileNet, and more. Package: python3-keras-applications Architecture: all Version: 1.0.8+ds-1~bpo+Debian.10.buster Priority: optional Section: python Source: keras-applications (1.0.8+ds-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 220 Depends: python3-h5py, python3-numpy, python3:any (>= 3.5~) Recommends: python3-keras Filename: pool-buster/keras/python3-keras-applications_1.0.8+ds-1~bpo+Debian.10.buster_all.deb Size: 27018 MD5sum: 58d7669dbdd691f77da64787daac924d SHA1: 88279210a6e24e15bdbfd5b4b2e25436e3bfc3e7 SHA256: 53a1d655ab3e561c0523cc116886b373ca071fc6dc173b649d24ddd904901b5b SHA512: 66582de0103c69e4b9b21cdd279abb793b2654816f1ff88d7f8f363618e2b7cec5cdb338cd7862df952a0187e8412d6350c5f660de8afb3bacc20adc4ac8d464 Homepage: https://keras.io/ Description: popular models and pre-trained weights for the Keras deep learning framework Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). . Keras Applications is the applications module of the Keras deep learning library. It provides model definitions and pre-trained weights for a number of popular architectures, such as VGG16, ResNet50, Xception, MobileNet, and more. Package: python3-keras-preprocessing Architecture: all Version: 1.0.5-1~bpo+Debian.buster.testing Priority: optional Section: python Source: keras-preprocessing (1.0.5-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 152 Depends: python3-numpy, python3-six, python3:any (>= 3.4~) Recommends: python3-keras Filename: pool-buster/keras/python3-keras-preprocessing_1.0.5-1~bpo+Debian.buster.testing_all.deb Size: 29758 MD5sum: 1e43f2c964e4aa50902f183d187807f4 SHA1: 867c70e0ec0574fc79c9d4645a833b4936c174bd SHA256: daf7d4b906a3ff32747a233e65cb91899282673b5b4508804e70366b7b40737e SHA512: 97aa8d28d5ff4d3efc1fd4bb634411f5b1ac48b4468c9e0a7b83cc69b34f96b2e775919b6a3f89cd1bfb7bf1ec8418622319664650d0408716f074442f9fe280 Homepage: http://keras.io/ Description: data preprocessing module for the Keras deep learning framework Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). . Keras Preprocessing is the data preprocessing and data augmentation module of the Keras deep learning library. It provides utilities for working with image data, text data, and sequence data. Package: python3-keras Architecture: all Version: 2.1.5-1+Debian-buster-testing Priority: optional Section: python Source: keras (2.1.5-1) Maintainer: Adam Cecile Installed-Size: 1641 Depends: python3-numpy (>= 1.9.1~), python3-scipy (>= 0.14~), python3-six (>= 1.9.0), python3-yaml, python3:any (>= 3.3.2-2~) Recommends: python3-tensorflow Suggests: python-keras-doc Filename: pool-buster/keras/python3-keras_2.1.5-1+Debian-buster-testing_all.deb Size: 208982 MD5sum: 5c73b32e94ab2c131d7fa703f6c5e627 SHA1: 853e8d3f6b1a12e4989d3c120c72b237bc71dc05 SHA256: 6554f9d35607810c6f7cf557bcd60451ae3f5ef61eac36d1ca4b4c6fa413386c SHA512: 7337e9732cc144ff8084a941f067fc3db27e3b67d3a09e2f329793e3b12e9f448505030a521102be92fd1f5ab0705c1f93313a26a4ad28827d4acffda11469d0 Homepage: https://github.com/fchollet/keras Description: Deep Learning library for Python (TensorFlow, Theano, or CNTK) (Python 3) Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. . It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. . This package installs the library for Python 3. Package: python3-keras Architecture: all Version: 2.2.4-1~bpo+Debian.buster.testing Priority: optional Section: python Source: keras (2.2.4-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 1503 Depends: python3-numpy, python3-scipy, python3-h5py, python3-theano, python3-keras-applications, python3-keras-preprocessing, python3-six (>= 1.9.0), python3-yaml, python3:any (>= 3.4~) Filename: pool-buster/keras/python3-keras_2.2.4-1~bpo+Debian.buster.testing_all.deb Size: 203482 MD5sum: eb1875ad9e295383a9b6f835add61c91 SHA1: 555e0c4f750db60d61d77c71bbcf230248699988 SHA256: 2b488eb4f8c2bc6d749310cd90d76a29daa4b07438871172759537bef85519a2 SHA512: 69c6015a53f10b772a361f683e401911f8702238dd82980da47e1e80d0eafa382ccc86f74389db3199a91dd3a259d9e05c9e0309c37425d728dfa6f020868aff Homepage: http://keras.io/ Description: deep learning framework running on Theano or TensorFlow Keras is a Python library for machine learning based on deep (multi- layered) artificial neural networks (DNN), which follows a minimalistic and modular design with a focus on fast experimentation. . Features of DNNs like neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are available in Keras a standalone modules which can be plugged together as wanted to create sequence models or more complex architectures. Keras supports convolutions neural networks (CNN, used for image recognition resp. classification) and recurrent neural networks (RNN, suitable for sequence analysis like in natural language processing). . It runs as an abstraction layer on the top of Theano (math expression compiler) by default, which makes it possible to accelerate the computations by using (GP)GPU devices. Alternatively, Keras could run on Google's TensorFlow (not yet available in Debian). Package: equivs-cuda-cublas-9-2 Architecture: all Version: 9.2 Multi-Arch: foreign Priority: optional Section: misc Maintainer: Adam Cecile Installed-Size: 9 Provides: cuda-cublas-9-2 Depends: libcublas9.2, libcudart9.2 Filename: pool-buster/libnvinfer/equivs-cuda-cublas/equivs-cuda-cublas-9-2_9.2_all.deb Size: 3200 MD5sum: d5c4b1a46947b0b946cf8716ba82b46a SHA1: 75370d42063167c2b3cb5705f1cff0fa8734ebd4 SHA256: 14534e3359c6accfb5a9c209b1a32c385a0ba42d4e35d0a92cb5fcae5c665ee9 SHA512: 2765be0a9fbbb2dde27f02aa58299632df3b167c7aebd80ed8128dbc2790af4535af812b5312ba2368018b304d15ab002873ea625cc0297f30e3432d1bcc102a Description: Fake package to provide cuda-cublas-X-Y dependency The real library is a dependency of this package. . This package provide an alternate name to comply with official NVIDIA debs dependending on it. Package: lightgbm-dev Architecture: all Version: 2.0.7+debian-2+Debian.buster.testing Priority: optional Section: devel Source: lightgbm (2.0.7+debian-2) Maintainer: Adam Cecile Installed-Size: 224 Depends: liblightgbm (>= 2.0.7+debian-2), liblightgbm (<< 2.0.7+debian-2.1~) Filename: pool-buster/lightgbm/lightgbm-dev_2.0.7+debian-2+Debian.buster.testing_all.deb Size: 38578 MD5sum: 1af0c8e1d96885eb75c9859639a13580 SHA1: d89655ce24d033c5cac60cdb14107b2299f6c7e9 SHA256: c525ae8b503007d2491e3e464f58c0fe4042178fc18efac822615c8abcca23ab SHA512: fef5b8af17f6e29f6f5e65004d41f27994687080c5e815dcc14481525b39f4e3b90c8fe1823cdbe817f288afa590f579c6c701f6e09b6ecb119f3de3fa1aa67c Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (development) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . It is designed to be distributed and efficient with the following advantages: . * Faster training speed and higher efficiency * Lower memory usage * Better accuracy * Parallel and GPU learning supported * Capable of handling large-scale data . This package contains the shared library development headers. Package: lightgbm-dev Architecture: all Version: 2.0.8+debian-1+Debian.buster.testing Priority: optional Section: devel Source: lightgbm (2.0.8+debian-1) Maintainer: Adam Cecile Installed-Size: 227 Depends: liblightgbm (>= 2.0.8+debian-1), liblightgbm (<< 2.0.8+debian-1.1~) Filename: pool-buster/lightgbm/lightgbm-dev_2.0.8+debian-1+Debian.buster.testing_all.deb Size: 39054 MD5sum: 190ec923cf7b0bbd79de7bc1e05c7290 SHA1: 4ea27828bee1412b2e748f350fe020b77bde7e3f SHA256: 306a7b879964c6f3b9a529247cb2414489f0d8d3f9e71d2fbbca13f3eb46212a SHA512: 622b27e54a57cb9907bae3daeec3dd70c8f9f0b3d8d2b61e31adc46ec710432570825279f093896fc560cdb1a1ea4885f3ed079a44dd1a3922f0843a4c11e9c0 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (development) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . It is designed to be distributed and efficient with the following advantages: . * Faster training speed and higher efficiency * Lower memory usage * Better accuracy * Parallel and GPU learning supported * Capable of handling large-scale data . This package contains the shared library development headers. Package: lightgbm-dev Architecture: all Version: 2.2.1+debian-1+Debian.buster.testing Priority: optional Section: devel Source: lightgbm (2.2.1+debian-1) Maintainer: Adam Cecile Installed-Size: 277 Depends: liblightgbm (>= 2.2.1+debian-1), liblightgbm (<< 2.2.1+debian-1.1~) Filename: pool-buster/lightgbm/lightgbm-dev_2.2.1+debian-1+Debian.buster.testing_all.deb Size: 49634 MD5sum: feb6ded86b1340e226cfd7b91e82e0d9 SHA1: 67d3252e51182ebd489d5fa439b8ef2dbd6d0cbb SHA256: 4d9fa872d37ab6fc9f3dd00570b9954f382eb33a1f97fdeb0ec0ba0602bc97ff SHA512: e31a2389b0f6314831d81ebd0a84a82a15157dd7f0f00795e978d7e299985ebecf88583060b537f1a6af949460e436662771a1087dfda646e6c7ed0fd8907b19 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (development) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python-lightgbm Architecture: all Version: 2.0.7+debian-2+Debian.buster.testing Priority: optional Section: python Source: lightgbm (2.0.7+debian-2) Maintainer: Adam Cecile Installed-Size: 188 Depends: python-numpy, python-scipy, python-sklearn, python:any (<< 2.8), python:any (>= 2.7.5-5~), liblightgbm (>= 2.0.7+debian-2), liblightgbm (<< 2.0.7+debian-2.1~) Filename: pool-buster/lightgbm/python-lightgbm_2.0.7+debian-2+Debian.buster.testing_all.deb Size: 32574 MD5sum: 2e28378fbb3a7875b203640183065a72 SHA1: fdb9112d7005f66e3dc4a658770ee17646782e17 SHA256: a0da8df8cf6b308bb7dca8aa7b0f16d15bb156fb70c2884e11ae4018e1318411 SHA512: 6773f78f2cad8fcaca6aaeecdcdb3912670cb3f1cbd8e65b6861bb682803ed5790e82e1a3a6cc4d7a5ad39cc6ff411f6dc52239035bef5b54922983cbf8c7714 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python2) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python-lightgbm Architecture: all Version: 2.2.3+debian-1+Debian.buster.testing Priority: optional Section: python Source: lightgbm (2.2.3+debian-1) Maintainer: Adam Cecile Installed-Size: 232 Depends: python-numpy, python-scipy, python-sklearn, python:any (<< 2.8), python:any (>= 2.7~), liblightgbm (>= 2.2.3+debian-1), liblightgbm (<< 2.2.3+debian-1.1~) Filename: pool-buster/lightgbm/python-lightgbm_2.2.3+debian-1+Debian.buster.testing_all.deb Size: 39238 MD5sum: 7bb2ef0ff4d22920cc1126e1a6d6ea87 SHA1: deb03c7a1cb5d38d32f686317a27b45dd71b859c SHA256: 8020a8641ac1733fa28548cefb5ba7ebe343e7ad5f18f7ced79653b27d9f105b SHA512: bd79f4d8505c5e3b793ca3b851310aeddd8a1cf7a469a69f53884c42c758766bda614fcb45e4d5219c20c0c507adc886cd968366ff13bc0a85b2972252c4b5b8 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python2) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python-lightgbm Architecture: all Version: 2.3.0+debian-1+Debian.buster.10 Priority: optional Section: python Source: lightgbm (2.3.0+debian-1) Maintainer: Adam Cecile Installed-Size: 258 Depends: python-numpy, python-scipy, python-sklearn, python:any (<< 2.8), python:any (>= 2.7~), liblightgbm (>= 2.3.0+debian-1), liblightgbm (<< 2.3.0+debian-1.1~) Filename: pool-buster/lightgbm/python-lightgbm_2.3.0+debian-1+Debian.buster.10_all.deb Size: 42994 MD5sum: 763f238c4be9c967f7a417022ea569da SHA1: fc7c1adb17c587bdc4e8ee8576c1277bdc855a8c SHA256: dd6c4fed39080df92d6bd64e5cdfd54bccd06cfc393f4fde659836652e665a6b SHA512: 20355f7b4d078bd1d9e67f803fed8b3ddd09f1b44531cb0a4c25622e79d989aff1d6953f33a1ce753e4689bdf3a5ec425f4e636cb403b8090e784308bbb5d822 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python2) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python3-lightgbm Architecture: all Version: 2.0.7+debian-2+Debian.buster.testing Priority: optional Section: python Source: lightgbm (2.0.7+debian-2) Maintainer: Adam Cecile Installed-Size: 188 Depends: python3-numpy, python3-scipy, python3-sklearn, python3:any (>= 3.4~), liblightgbm (>= 2.0.7+debian-2), liblightgbm (<< 2.0.7+debian-2.1~) Filename: pool-buster/lightgbm/python3-lightgbm_2.0.7+debian-2+Debian.buster.testing_all.deb Size: 32660 MD5sum: bf4fdfa02df43fc8086bb47b6b44c2c7 SHA1: 474053b81e46d9fa5ae126481d5127d8dbb72480 SHA256: 9ae33b402a01c5bf4998411938e138d4408ab7559795c547a4e18eb2d77bde4d SHA512: 3c0abb883102cfb31e834fcd00a348128525767e31ed7f918ba53eba9e354114345274f0d1d921c80a51fd26cf6ee8c5a880491355e6162b9a26b754998874fc Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python3) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python3-lightgbm Architecture: all Version: 2.0.8+debian-1+Debian.buster.testing Priority: optional Section: python Source: lightgbm (2.0.8+debian-1) Maintainer: Adam Cecile Installed-Size: 188 Depends: python3-numpy, python3-scipy, python3-sklearn, python3:any (>= 3.4~), liblightgbm (>= 2.0.8+debian-1), liblightgbm (<< 2.0.8+debian-1.1~) Filename: pool-buster/lightgbm/python3-lightgbm_2.0.8+debian-1+Debian.buster.testing_all.deb Size: 32642 MD5sum: f18bd4ec6db1c3bfa6e03dbe2083b741 SHA1: 15a8879f11538a7eff6768a8cb072b73374c7868 SHA256: 54e3f51f589cd315c5ee66f043a0f61b2ebecfb8a5f8fcc2d2d1ee3b65e0f2b6 SHA512: 1dabc386ba21f86b7b7db5da003871d38e37f8e671d6bdfde94c3ba8958ab93929ec50fa5e96e8c6a6e0c15fa37eca72a50eeaec2f37dcd9ce5cd9d4e270fe0b Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python3) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python3-lightgbm Architecture: all Version: 2.2.1+debian-1+Debian.buster.testing Priority: optional Section: python Source: lightgbm (2.2.1+debian-1) Maintainer: Adam Cecile Installed-Size: 222 Depends: python3-numpy, python3-scipy, python3-sklearn, python3:any (>= 3.4~), liblightgbm (>= 2.2.1+debian-1), liblightgbm (<< 2.2.1+debian-1.1~) Filename: pool-buster/lightgbm/python3-lightgbm_2.2.1+debian-1+Debian.buster.testing_all.deb Size: 38230 MD5sum: 422cfab092effb3dfd624cb3e738c734 SHA1: b99b4fbaaa23893a26573e24bdd6832aefc12fca SHA256: 3b063bd8064234ff4aa7f44178ccfe0aa824e9ff35939b63ddca91638abc8d80 SHA512: 263ad57bdddec7924ab64ab9f01f026631c5983e718a19290f08bdeeb232f00903585d0790e0a900bd7daef230c0eee14c287e44789aa8b6e32a8af79da418b4 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python3) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python3-lightgbm Architecture: all Version: 2.2.3+debian-1+Debian.buster.testing Priority: optional Section: python Source: lightgbm (2.2.3+debian-1) Maintainer: Adam Cecile Installed-Size: 232 Depends: python3-numpy, python3-scipy, python3-sklearn, python3:any (>= 3.4~), liblightgbm (>= 2.2.3+debian-1), liblightgbm (<< 2.2.3+debian-1.1~) Filename: pool-buster/lightgbm/python3-lightgbm_2.2.3+debian-1+Debian.buster.testing_all.deb Size: 39310 MD5sum: b93edd39308acc581f019e7b81f235c3 SHA1: f2414727fbb3c393b72cb273ca30de4a34ec42cd SHA256: ac0ce5ee61a58432b15934b7563ce514c04a3762330807ae341273daf66a6f15 SHA512: b879e7abc0092fece64a8bf70200eb6b3c61bc36e8b752c5e09ec1d9160666c303bd0f66d6ab41ba618e6cd3c288831eb37750cbbda4aa5d0ae749afd6d0f571 Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python3) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: python3-lightgbm Architecture: all Version: 2.3.0+debian-1+Debian.buster.10 Priority: optional Section: python Source: lightgbm (2.3.0+debian-1) Maintainer: Adam Cecile Installed-Size: 258 Depends: python3-numpy, python3-scipy, python3-sklearn, python3:any (>= 3.4~), liblightgbm (>= 2.3.0+debian-1), liblightgbm (<< 2.3.0+debian-1.1~) Filename: pool-buster/lightgbm/python3-lightgbm_2.3.0+debian-1+Debian.buster.10_all.deb Size: 43098 MD5sum: 308719aa83fa94c933bcbf7e94184660 SHA1: ae0c6be8c3cda249149f0c607fb82589b6f4b6fe SHA256: 9313b30992786416b5440a52f78e193e3484c559340a541616e99de7db3e93e3 SHA512: 78a6a8b3b9d8a92f73b56ff4a88e6627c48a58fd24f07e7ab8ae16ad882912cb1a3ec3d204c736ffeaa1f85991c97189bda123c732a9d2ea9fa26dd516089dea Homepage: https://github.com/Microsoft/LightGBM Description: High performance gradient boosting framework (Python3) LightGBM is a gradient boosting framework that uses tree based learning algorithms. . 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Package: libmkldnn-docs Architecture: all Version: 0.10+20170913.b01e3a5-2+Debian-buster-testing Priority: optional Section: doc Source: mkl-dnn (0.10+20170913.b01e3a5-2) Maintainer: Adam Cecile Installed-Size: 6880 Suggests: libmkldnn-dev (= 0.10+20170913.b01e3a5-2+Debian-buster-testing) Filename: pool-buster/mkl-dnn/libmkldnn-docs_0.10+20170913.b01e3a5-2+Debian-buster-testing_all.deb Size: 1185732 MD5sum: 211132d32a2658c8b53b16bc9ef41ee1 SHA1: 3a45d24c9731a020ae3a3f0992cd9e3b7ca5933a SHA256: 9c104d16ff466c7ea7a717d2d989ccfd581f455d641e945971030c4ab8840131 SHA512: f53f76787ec4deffcc6939e145cf6bf243f35be12e77dcb74c91c970fb6392486e90fea8c1183ed29d196761c31868d0ca38feb5b5e5b3b2e96e909ec42a9f9e Homepage: https://github.com/01org/mkl-dnn Description: Intel MKL for Deep Neural Networks (documentation) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel(R) architecture. . Intel(R) MKL-DNN includes highly vectorized and threaded building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. . This package contains library documentation. Package: libmkldnn-docs Architecture: all Version: 0.13-1+Debian-buster-testing Priority: optional Section: doc Source: mkl-dnn (0.13-1) Maintainer: Adam Cecile Installed-Size: 7863 Suggests: libmkldnn-dev (= 0.13-1+Debian-buster-testing) Filename: pool-buster/mkl-dnn/libmkldnn-docs_0.13-1+Debian-buster-testing_all.deb Size: 1436700 MD5sum: 0d5e9638e76308ab84e0143b78b04ddc SHA1: e73a1862157e83a7f79d6d1fec64c8cbb2ee0b7e SHA256: 9d351fcd4d8babc2f52c539f89905d2e345e1a4983857cbd238c0ca285bcc96d SHA512: 1df17ecb9e085d68a60f3957bb4a398f78a7f374330cace06d0384fd12ee421c146c9474e4388f4cf660cd549f509df2b9e89f55b63f40b8d576ea847cf64242 Homepage: https://github.com/01org/mkl-dnn Description: Intel MKL for Deep Neural Networks (documentation) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel(R) architecture. . Intel(R) MKL-DNN includes highly vectorized and threaded building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. . This package contains library documentation. 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Intel(R) MKL-DNN includes highly vectorized and threaded building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. . This package contains library documentation. Package: libmkldnn-docs Architecture: all Version: 0.17.2-1+Debian.buster.testing Priority: optional Section: doc Source: mkl-dnn (0.17.2-1) Maintainer: Adam Cecile Installed-Size: 11347 Suggests: libmkldnn-dev (= 0.17.2-1+Debian.buster.testing) Filename: pool-buster/mkl-dnn/libmkldnn-docs_0.17.2-1+Debian.buster.testing_all.deb Size: 2305108 MD5sum: 8897453572b82a6e85b077bc40ab6053 SHA1: 5976762de4435b93bb4a687d1fccf45f385876b9 SHA256: a29c6b46b3699d4ee9ca77b188e9c499d3c9dc0746f7b03a586c0a2c51cd55c0 SHA512: a33afc2ef5ee30cde8268d3da62995de050bbcfb68f390cc07d6b58f5d81f9e686153d567eb540a1517b58f8fb6baa8000cc4688ae0552db2e55f62a3e4e2acd Homepage: https://github.com/01org/mkl-dnn Description: Intel MKL for Deep Neural Networks (documentation) Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel(R) architecture. . Intel(R) MKL-DNN includes highly vectorized and threaded building blocks for implementation of convolutional neural networks (CNN) with C and C++ interfaces. . This package contains library documentation. Package: python-mxnet Architecture: all Version: 0.12.0+debian-1+Debian.buster.testing Priority: optional Section: python Source: mxnet (0.12.0+debian-1) Maintainer: Adam Cecile Installed-Size: 1575 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~), python-requests, python-numpy, libmxnet-generic (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet-mkl-dnn (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet-cuda (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet (= 0.12.0+debian-1+Debian.buster.testing) Recommends: python-opencv, python-graphviz Filename: pool-buster/mxnet/python-mxnet_0.12.0+debian-1+Debian.buster.testing_all.deb Size: 212192 MD5sum: 5e30df150aebb2ffaa63858cd1487879 SHA1: e6e1c4a45ea3bdc0d7794da8e019472ddc1c7265 SHA256: 8814d5e12f81bacdc8c21efff80d15297d22e7964665354f269c8c89322a5e03 SHA512: c44d25722378674c2526fa58326e24b35d2b5bb1123cf6e51e7c46687891c6c276e7e32acc5b4a8d62614bd73f436849ce91f3ff6bd774288b7fd36bb32fe6cd Homepage: https://mxnet.incubator.apache.org/ Description: Apache deep learning framework (Python2 bindings) Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. . This package contains Python2 module. Enabled optimizations depends on the libmxnet package being installed. Package: python-mxnet Architecture: all Version: 0.12.0+debian-2+Debian.buster.testing Priority: optional Section: python Source: mxnet (0.12.0+debian-2) Maintainer: Adam Cecile Installed-Size: 1575 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~), python-requests, python-numpy, libmxnet-generic (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet-mkl-dnn (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet-cuda (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet (= 0.12.0+debian-2+Debian.buster.testing) Recommends: python-opencv, python-graphviz Filename: pool-buster/mxnet/python-mxnet_0.12.0+debian-2+Debian.buster.testing_all.deb Size: 212352 MD5sum: 4fe5ba61c931a945c51f02b2e227526e SHA1: 124b7c9d4ac5e351fbbbb57527ebcba8441a39e5 SHA256: f0e5093b1db665ab5f37a37259fdcd0a5a2cc4442f0f09a6db49b63205ade90d SHA512: d30fb77d3e44e705f6a8fa3f27923c870f6532177c6429bf99018fb173e17d6a630b4cd74603c57495d9bbce50298e7a16d7ed963abdca970e3ee609695158c5 Homepage: https://mxnet.incubator.apache.org/ Description: Apache deep learning framework (Python2 bindings) Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. . This package contains Python2 module. Enabled optimizations depends on the libmxnet package being installed. Package: python-mxnet Architecture: all Version: 1.0.0+debian-1+Debian.buster.testing Priority: optional Section: python Source: mxnet (1.0.0+debian-1) Maintainer: Adam Cecile Installed-Size: 1625 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~), python-requests, python-numpy, libmxnet-generic (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet-mkl-dnn (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet-cuda (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet-cuda-mkl-dnn (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet (= 1.0.0+debian-1+Debian.buster.testing) Recommends: python-opencv, python-graphviz Filename: pool-buster/mxnet/python-mxnet_1.0.0+debian-1+Debian.buster.testing_all.deb Size: 220898 MD5sum: 6b63f8b4e80e13fdbc329645be4c85fc SHA1: 6a6468600e779019687dba84b0495c64cb47135f SHA256: eab8124beaa419994ce236723a27df840dbc287ffbeb6b463f28fac9535c908c SHA512: 4c37b045031287a409ab1c5d03cb954cf93c970ec485fb2fe2793c5beac84f616cc7a2e7909183682b32be29048b6a4c98b3697775fd09140493166718114f4c Homepage: https://mxnet.incubator.apache.org/ Description: Apache deep learning framework (Python2 bindings) Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. . This package contains Python2 module. Enabled optimizations depends on the libmxnet package being installed. Package: python3-mxnet Architecture: all Version: 0.12.0+debian-1+Debian.buster.testing Priority: optional Section: python Source: mxnet (0.12.0+debian-1) Maintainer: Adam Cecile Installed-Size: 1575 Depends: python3:any (>= 3.4~), python3-requests, python3-numpy, libmxnet-generic (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet-mkl-dnn (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet-cuda (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-1+Debian.buster.testing) | libmxnet (= 0.12.0+debian-1+Debian.buster.testing) Recommends: python3-opencv, python3-graphviz Filename: pool-buster/mxnet/python3-mxnet_0.12.0+debian-1+Debian.buster.testing_all.deb Size: 212292 MD5sum: bacf258102adaf59a5193dca763768f5 SHA1: e1bf41b3873e51328f2e9992e67661c01d1875ce SHA256: 9a2dbca3872b758d7b27ce4a5a2459a8a51f08d123b21cefae9a98281f321fc8 SHA512: 0f859ffaf7824109b335cd65765ad07201a09319ee4b00323eb66e5dc27d2ab700ac150cf005a2f2bbbf6169177bd7e450c4df7b83477b391094b25f1808255a Homepage: https://mxnet.incubator.apache.org/ Description: Apache deep learning framework (Python3 bindings) Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. . This package contains Python3 module. Enabled optimizations depends on the libmxnet package being installed. Package: python3-mxnet Architecture: all Version: 0.12.0+debian-2+Debian.buster.testing Priority: optional Section: python Source: mxnet (0.12.0+debian-2) Maintainer: Adam Cecile Installed-Size: 1575 Depends: python3:any (>= 3.4~), python3-requests, python3-numpy, libmxnet-generic (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet-mkl-dnn (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet-cuda (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-2+Debian.buster.testing) | libmxnet (= 0.12.0+debian-2+Debian.buster.testing) Recommends: python3-opencv, python3-graphviz Filename: pool-buster/mxnet/python3-mxnet_0.12.0+debian-2+Debian.buster.testing_all.deb Size: 212464 MD5sum: 2c33861b3527fa718a8da1fda372bcb7 SHA1: bf1f105d4962816bf63a93c6bce72c5ab20df7e3 SHA256: 04ae0a8eff9ed71fb342c373fff0360582bde4360fb2230898db5611977bf646 SHA512: 8a8978ce75fa1abfff57b272a78f6825239a0ac7c7b4d8a7189eedc381cf96af2735dc7568decacd82f8a6d0384ceed36d91adab873e1183f4c67de010de0287 Homepage: https://mxnet.incubator.apache.org/ Description: Apache deep learning framework (Python3 bindings) Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. . This package contains Python3 module. Enabled optimizations depends on the libmxnet package being installed. Package: python3-mxnet Architecture: all Version: 1.0.0+debian-1+Debian.buster.testing Priority: optional Section: python Source: mxnet (1.0.0+debian-1) Maintainer: Adam Cecile Installed-Size: 1625 Depends: python3:any (>= 3.4~), python3-requests, python3-numpy, libmxnet-generic (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet-mkl-dnn (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet-cuda (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet-cuda-mkl-dnn (= 1.0.0+debian-1+Debian.buster.testing) | libmxnet (= 1.0.0+debian-1+Debian.buster.testing) Recommends: python3-opencv, python3-graphviz Filename: pool-buster/mxnet/python3-mxnet_1.0.0+debian-1+Debian.buster.testing_all.deb Size: 221058 MD5sum: c1659ad8ed199cad34dbcbd01a92be49 SHA1: e6393b0c63d3f2a9515101c74d0e25fa37ce90a6 SHA256: 1960756b2c33b4713059109d6104303cac3e1006b313bbed2e345565a7aa204a SHA512: b034bc66884bea42274704360ce869cabf2df30bd5c276bc1d16740b1ed6235032d93cf01d432b57a4bfb3e1ab7dc5f47498159461abaec0671d15b3427d89a1 Homepage: https://mxnet.incubator.apache.org/ Description: Apache deep learning framework (Python3 bindings) Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. . It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. . This package contains Python3 module. Enabled optimizations depends on the libmxnet package being installed. Package: python-confu-doc Architecture: all Version: 0.0~git20171211.cc90068-1 Priority: optional Section: doc Source: confu Maintainer: Adam Cecile Installed-Size: 673 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-buster/nnpack/confu/python-confu-doc_0.0~git20171211.cc90068-1_all.deb Size: 296272 MD5sum: 45b9e7accf77fb7f901ad27caa2f8355 SHA1: 0afeb7b8fa278dac631767bf7036207622778b2c SHA256: 5331064955e038862ac768062b44ea8c3e9c28d88d6b5766b0a3ead245e6a96e SHA512: bcf6a7366b44eb5840200886101d1f07e9cf3638a4a42cb87532c903f1b4f7d0e4aa4dbbbe2552e520b496e2876f9e0072751323bdef6e295a03581bd182adb8 Homepage: https://github.com/Maratyszcza/confu Description: Configuration generator for Ninja build system (common documentation) Configuration generator for Ninja build system. . This is the common documentation package. Package: python-confu Architecture: all Version: 0.0~git20171211.cc90068-1 Priority: optional Section: python Source: confu Maintainer: Adam Cecile Installed-Size: 165 Depends: python-ninja-syntax, python-six, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-confu-doc Filename: pool-buster/nnpack/confu/python-confu_0.0~git20171211.cc90068-1_all.deb Size: 22340 MD5sum: 5da45fa4b69b48e3d02568ce7ab36654 SHA1: 50497dcc09d480da35feadd2ee09da6ef5840efd SHA256: a2537304c4b5f436455227ac57b8638b5c07a42c3c4778b06add6791db86129e SHA512: 6a6582c70bf2794fd8aa98dcf42474caea440325dfabf2f6d3e6c614d8f52a5ba1764bc8a5dd70ce813152a3870f45a7cff3913d964daa954a39042c3b60ce19 Homepage: https://github.com/Maratyszcza/confu Description: Configuration generator for Ninja build system (Python 2) Configuration generator for Ninja build system. . This package installs the library for Python 2. Package: python3-confu Architecture: all Version: 0.0~git20171211.cc90068-1 Priority: optional Section: python Source: confu Maintainer: Adam Cecile Installed-Size: 169 Depends: python3, python3-ninja-syntax, python3-six, python3-yaml, python3:any (>= 3.4~) Suggests: python-confu-doc Filename: pool-buster/nnpack/confu/python3-confu_0.0~git20171211.cc90068-1_all.deb Size: 22510 MD5sum: 7258f90a50c4be1eb0484c0e48ddbf22 SHA1: f665188fabfeefd614821411b94b18792ca50700 SHA256: c59f828e3f81c9755afe3acb3578c3bde57934496e925447804a4f7bfff2b3a7 SHA512: 43fb05c7ec6c9254a260725749afc5c81bdd19a1e1cca9ef8da898c171f0a556bb19c88238b4769a4687c377048c5c7c5f5a18743ad9759028deedc3938c2696 Homepage: https://github.com/Maratyszcza/confu Description: Configuration generator for Ninja build system (Python 3) Configuration generator for Ninja build system. . This package installs the library for Python 3 and command line tool. Package: python-ninja-syntax Architecture: all Version: 1.7.2-1 Priority: optional Section: python Source: ninja-syntax Maintainer: Adam Cecile Installed-Size: 45 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-buster/nnpack/ninja-syntax/python-ninja-syntax_1.7.2-1_all.deb Size: 5334 MD5sum: 09315394f9462e704efcf87b4030239e SHA1: a9c286b1a216f6483b69d9f295a84edb2c78e8cf SHA256: 57b35557dc6233a32adb21173f35910e4203ed02c63c4698cc8174ff5f51f1dd SHA512: 735612a3afbc2b36879b472358f8b629f7d9d3cf6693256327489860856906d0a57612adeb8905d061c195bfc3647fc535024f5fcf6d82ee8a448757709eba85 Homepage: https://pypi.python.org/simple/ninja-syntax/ Description: Python module for generating .ninja files (Python 2) Python module for generating .ninja files. . This package installs the library for Python 2. Package: python3-ninja-syntax Architecture: all Version: 1.7.2-1 Priority: optional Section: python Source: ninja-syntax Maintainer: Adam Cecile Installed-Size: 45 Depends: python3:any (>= 3.4~) Suggests: python-ninja-syntax-doc Filename: pool-buster/nnpack/ninja-syntax/python3-ninja-syntax_1.7.2-1_all.deb Size: 5416 MD5sum: b005174b3c8225253e0556ee3c8937f6 SHA1: 64e9ac9e47c112a64170d4084f72adc81f945355 SHA256: 3f1538057d5ea8b386e1f47d53efeeeec27cc9963dcd6b211622978465ec7c8e SHA512: f5fdaac5620771ed2e3231990b84a28ee1631f45e939afcf1e4af2c26d46875e4e1c8313f5357dc6c7f5d07c6b89a5ae69b63a6a992c63f10db3f92044a8c6bb Homepage: https://pypi.python.org/simple/ninja-syntax/ Description: Python module for generating .ninja files (Python 3) Python module for generating .ninja files. . This package installs the library for Python 3. Package: python-opcodes-doc Architecture: all Version: 0.3.14-1 Priority: optional Section: doc Source: opcodes Maintainer: Adam Cecile Installed-Size: 6027 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-buster/nnpack/opcodes/python-opcodes-doc_0.3.14-1_all.deb Size: 428096 MD5sum: dd6344bde982ce34ad965d77834b12c6 SHA1: 13f208a32300b35fcee10d29303b529dc939d79e SHA256: f2a88821eae899c2ad47ae96575b0e89e98d88c3f4675c2fb71cedd753937dca SHA512: 1cd236741203149ff2c55367e2d13caf842a760c54a4282737e6c507fd61169efe4dcb46067a7ee2ac273db3efa7a04ca45bdf026b45e3e85cfc07d14e070e49 Homepage: https://github.com/Maratyszcza/Opcodes Description: Document CPU instruction sets in a format for development (common documentation) The goal of this project is to document instruction sets in a format convenient for tools development. . An instruction set is represented by three files: . * An XML file that describes instructions * An XSD file that describes the structure of the XML file * A Python module that reads the XML file and represents it as a set of Python objects . This project is a spin-off from PeachPy assembler. . The project provides descriptions for most user-mode x86, x86-64, and k1om instructions up to AVX-512 and SHA (including 3dnow!+, XOP, FMA3, FMA4, TBM and BMI2). . This is the common documentation package. Package: python-opcodes Architecture: all Version: 0.3.14-1 Priority: optional Section: python Source: opcodes Maintainer: Adam Cecile Installed-Size: 6275 Depends: python-pkg-resources, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-opcodes-doc Filename: pool-buster/nnpack/opcodes/python-opcodes_0.3.14-1_all.deb Size: 148458 MD5sum: 2af89db240f21d9a79ec753859c7acf7 SHA1: 2e310ae63c106c078641c3240a3730366caddf7c SHA256: bb9854f23aa4a3364cdf0319d1d1e972ff4377d77325db3e54c7f306721a71b8 SHA512: b8bceda8d774d9610f04eaed430426a91474a25cdc8c5977f19d47068f3d8abac105f4f6232aa14d1912feaf9b0454ade3a4ba99e186566ab9179e9585d49fff Homepage: https://github.com/Maratyszcza/Opcodes Description: Document CPU instruction sets in a format for development (Python 2) The goal of this project is to document instruction sets in a format convenient for tools development. . An instruction set is represented by three files: . * An XML file that describes instructions * An XSD file that describes the structure of the XML file * A Python module that reads the XML file and represents it as a set of Python objects . This project is a spin-off from PeachPy assembler. . The project provides descriptions for most user-mode x86, x86-64, and k1om instructions up to AVX-512 and SHA (including 3dnow!+, XOP, FMA3, FMA4, TBM and BMI2). . This package installs the library for Python 2. Package: python3-opcodes Architecture: all Version: 0.3.14-1 Priority: optional Section: python Source: opcodes Maintainer: Adam Cecile Installed-Size: 6275 Depends: python3-pkg-resources, python3:any (>= 3.4~) Suggests: python-opcodes-doc Filename: pool-buster/nnpack/opcodes/python3-opcodes_0.3.14-1_all.deb Size: 148554 MD5sum: e7af3bf7db0b0a01a42560e6939a3152 SHA1: e02b7a2b6278cac8827557991b4b46e9a419413c SHA256: 1afc021def1971c78121b17670ff0481d0155211e263c103c8a469f8d26a00d4 SHA512: 84d5ecb6c4788cbcbe1ea648615bbc4c57ff0becde7fa35a8525ca1a20c719f672ac32968236324138d21d86dcc5fef2fd7b7a14eb5e0a240b7032eaf80f0049 Homepage: https://github.com/Maratyszcza/Opcodes Description: Document CPU instruction sets in a format for development (Python 3) The goal of this project is to document instruction sets in a format convenient for tools development. . An instruction set is represented by three files: . * An XML file that describes instructions * An XSD file that describes the structure of the XML file * A Python module that reads the XML file and represents it as a set of Python objects . This project is a spin-off from PeachPy assembler. . The project provides descriptions for most user-mode x86, x86-64, and k1om instructions up to AVX-512 and SHA (including 3dnow!+, XOP, FMA3, FMA4, TBM and BMI2). . This package installs the library for Python 3. Package: python-peachpy-doc Architecture: all Version: 0.2.0~git20171207.311c1f6-1 Priority: optional Section: doc Source: peachpy Maintainer: Adam Cecile Installed-Size: 6343 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-buster/nnpack/peachpy/python-peachpy-doc_0.2.0~git20171207.311c1f6-1_all.deb Size: 481568 MD5sum: 7f4d496c7ec44f8754407c4030553fb7 SHA1: 537c6c32db461070a6e807f8dced396c73b4e5a8 SHA256: a5d79f83354ae0166a5509606a88b0b5bd3b7b9753bc393c8f560a78da8b0fc0 SHA512: 9b5dcb54515a532210666322affd6d058622b9c12fdcab7a35b68db850f90604b07a3a04f04d7351058fed2ff00e31ff37ddd90eac7959ae65cc4656d8fecc85 Homepage: https://github.com/Maratyszcza/PeachPy Description: Python framework for writing high-performance assembly kernels (common documentation) PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly. Some PeachPy features: . * Universal assembly syntax for Windows, Unix, and Golang assembly * Automatic adaption of function to different calling conventions and ABIs * Automatic register allocation * Automation of routine tasks in assembly programming * Supports x86-64 instructions up to AVX-512 and SHA * Auto-generation of metadata files * Python-based metaprogramming and code-generation * Multiplexing of multiple instruction streams * Compatible with Python 2 and Python 3, CPython and PyPy . This is the common documentation package. Package: python-peachpy-doc Architecture: all Version: 0.2.0~git20171207.311c1f6-2 Priority: optional Section: doc Source: peachpy Maintainer: Adam Cecile Installed-Size: 6343 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-buster/nnpack/peachpy/python-peachpy-doc_0.2.0~git20171207.311c1f6-2_all.deb Size: 481662 MD5sum: c4c8b91bc2ce75bab101263efe771794 SHA1: c2743fa3a775182ad14438d3e6c4c00ff7a28a2f SHA256: 80aa31076c76e0307e160764ab9b4459728692b5830c9e4623fca50241283bf1 SHA512: 3ba1ff49a3a3b613af75c0659b3764e5f3b8f0103f0b9ffa8524116a417dd357688595495851556d3fdda55b6f201bfcf33dcdf62c9946bd840b50b4c4b9239e Homepage: https://github.com/Maratyszcza/PeachPy Description: Python framework for writing high-performance assembly kernels (common documentation) PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly. Some PeachPy features: . * Universal assembly syntax for Windows, Unix, and Golang assembly * Automatic adaption of function to different calling conventions and ABIs * Automatic register allocation * Automation of routine tasks in assembly programming * Supports x86-64 instructions up to AVX-512 and SHA * Auto-generation of metadata files * Python-based metaprogramming and code-generation * Multiplexing of multiple instruction streams * Compatible with Python 2 and Python 3, CPython and PyPy . This is the common documentation package. Package: python-peachpy Architecture: all Version: 0.2.0~git20171207.311c1f6-1 Priority: optional Section: python Source: peachpy Maintainer: Adam Cecile Installed-Size: 5255 Depends: python-enum34, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-peachpy-doc Filename: pool-buster/nnpack/peachpy/python-peachpy_0.2.0~git20171207.311c1f6-1_all.deb Size: 193652 MD5sum: fe096bdee40863024bb7952bee2affb2 SHA1: 8b4f5490cc3e4c422139967e3602e144f5aade6c SHA256: dea6f641df2c81e69a304589c81b6919204f2a6cefca7e8818c16aa026419dab SHA512: 9bac1a7ecfde4e03713a4e385b727b83937f373a6ce166a8f2f7dcb28998d683712b42e9d3c7ab53e0820aa826011d9db26e1b9845cc898e8cde9d59456022f9 Homepage: https://github.com/Maratyszcza/PeachPy Description: Python framework for writing high-performance assembly kernels (Python 2) PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly. Some PeachPy features: . * Universal assembly syntax for Windows, Unix, and Golang assembly * Automatic adaption of function to different calling conventions and ABIs * Automatic register allocation * Automation of routine tasks in assembly programming * Supports x86-64 instructions up to AVX-512 and SHA * Auto-generation of metadata files * Python-based metaprogramming and code-generation * Multiplexing of multiple instruction streams * Compatible with Python 2 and Python 3, CPython and PyPy . This package installs the library for Python 2. Package: python-peachpy Architecture: all Version: 0.2.0~git20171207.311c1f6-2 Priority: optional Section: python Source: peachpy Maintainer: Adam Cecile Installed-Size: 5255 Depends: python-enum34, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-peachpy-doc Filename: pool-buster/nnpack/peachpy/python-peachpy_0.2.0~git20171207.311c1f6-2_all.deb Size: 193736 MD5sum: f0cf74d34e7230e10f578f0d36b5f94c SHA1: 3c10b4d6942ac8fc14ccff6a3b9f7146d9260240 SHA256: 237e089c0f1b2c5b4165166fea1f394cbc0fbe7ee7edfdce882cac1d75604f1f SHA512: a691adbe0fb432223473d37dec50087c548a1a12c9b88ba52f60b667e20ad4fbe624fbc251d5244a1fe9a81c04cde78f032b6f11a7733f88e393d4a577dc47d2 Homepage: https://github.com/Maratyszcza/PeachPy Description: Python framework for writing high-performance assembly kernels (Python 2) PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly. Some PeachPy features: . * Universal assembly syntax for Windows, Unix, and Golang assembly * Automatic adaption of function to different calling conventions and ABIs * Automatic register allocation * Automation of routine tasks in assembly programming * Supports x86-64 instructions up to AVX-512 and SHA * Auto-generation of metadata files * Python-based metaprogramming and code-generation * Multiplexing of multiple instruction streams * Compatible with Python 2 and Python 3, CPython and PyPy . This package installs the library for Python 2. Package: python3-peachpy Architecture: all Version: 0.2.0~git20171207.311c1f6-1 Priority: optional Section: python Source: peachpy Maintainer: Adam Cecile Installed-Size: 5255 Depends: python3-enum34, python3-six, python3:any (>= 3.4~) Suggests: python-peachpy-doc Filename: pool-buster/nnpack/peachpy/python3-peachpy_0.2.0~git20171207.311c1f6-1_all.deb Size: 193800 MD5sum: 75e288efe7588061cd876f0774d650ea SHA1: aea80440aa15431474c16f3207464c1889184322 SHA256: 88fc110cc740509475180dba6bfd1f5cce584dc114cf02cf4990d9bd0ceb59f4 SHA512: 3a7703b7af36f24e2ba069c56f48c377423a39c5f59ac8f3b95fb034e900c75430f61ba41123f09b071f37a07c49f7bfb6c285e736aa45a34ab39720aad16af4 Homepage: https://github.com/Maratyszcza/PeachPy Description: Python framework for writing high-performance assembly kernels (Python 3) PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly. Some PeachPy features: . * Universal assembly syntax for Windows, Unix, and Golang assembly * Automatic adaption of function to different calling conventions and ABIs * Automatic register allocation * Automation of routine tasks in assembly programming * Supports x86-64 instructions up to AVX-512 and SHA * Auto-generation of metadata files * Python-based metaprogramming and code-generation * Multiplexing of multiple instruction streams * Compatible with Python 2 and Python 3, CPython and PyPy . This package installs the library for Python 3. Package: python3-peachpy Architecture: all Version: 0.2.0~git20171207.311c1f6-2 Priority: optional Section: python Source: peachpy Maintainer: Adam Cecile Installed-Size: 5255 Depends: python3-six, python3:any (>= 3.4~) Suggests: python-peachpy-doc Filename: pool-buster/nnpack/peachpy/python3-peachpy_0.2.0~git20171207.311c1f6-2_all.deb Size: 193880 MD5sum: e5d2b80dce9b44bb343d04bf3429066e SHA1: eeb14be9651429f745957e413eefe80f5325981f SHA256: 9eb61817606b0fc03f451f59739f6311e985ec8a5ade6108f1561af092db8200 SHA512: 36051f8ad820d662e9caf10a7ee0583bfef490dd2696b311af66bf3813b36fc20999c15dc7e60ba6d3f8fb90aba3d5cb93e1324d4a83c39b3c83582cb0ad1cd3 Homepage: https://github.com/Maratyszcza/PeachPy Description: Python framework for writing high-performance assembly kernels (Python 3) PeachPy aims to simplify writing optimized assembly kernels while preserving all optimization opportunities of traditional assembly. Some PeachPy features: . * Universal assembly syntax for Windows, Unix, and Golang assembly * Automatic adaption of function to different calling conventions and ABIs * Automatic register allocation * Automation of routine tasks in assembly programming * Supports x86-64 instructions up to AVX-512 and SHA * Auto-generation of metadata files * Python-based metaprogramming and code-generation * Multiplexing of multiple instruction streams * Compatible with Python 2 and Python 3, CPython and PyPy . This package installs the library for Python 3. Package: onnx-tf-test-models Architecture: all Version: 1.2.0-1 Priority: optional Section: science Source: onnx-tensorflow (0.0) Maintainer: Adam Cecile Installed-Size: 1723334 Filename: pool-buster/onnx-tf-test-models_all_distribs/onnx-tf-test-models_1.2.0-1_all.deb Size: 1611987830 MD5sum: 8766fefe20748a95cd0bd0237bff10a7 SHA1: a306efb11e993695d8206d5b2f36d24749cc63ab SHA256: a52e12ca016a37cd39fe0a84a2c649784a83bd6a1f9f66e6a4294448bd68acd1 SHA512: d9b03273dcd7fedb7c85c54cdc593ca6eda49db551c83f4b62bceb32323c4f5d535ef1a396c1dd18a0a9bc8a1144c0007dffc77c85ef6bc06fa54e6026386861 Homepage: https://github.com/onnx/onnx-tensorflow Description: Tensorflow models used in ONNX unit tests Set of TensorFlow models used during onnx-tensorflow unit tests Package: python-opt-einsum-doc Architecture: all Version: 3.3.0-1+Debian.10.buster Priority: optional Section: doc Source: opt-einsum (3.3.0-1) Maintainer: Adam Cecile Installed-Size: 2338 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg) Filename: pool-buster/opt-einsum/python-opt-einsum-doc_3.3.0-1+Debian.10.buster_all.deb Size: 1212092 MD5sum: 0335b79621fce02c1fbeb30cc2547727 SHA1: 23ee5eead126ae7c018889cea5da51ad5207830f SHA256: fc62781700d738484d3b3328bd629c892d580aa18b7da00c0c92155328f67e36 SHA512: ac2d0b6a525a0e351d5b065e7993879458e4c37cb36733ecf2d25b64cf5077f800051c0ca20de2d1d3a687ebf81bc0697dba386db5f53f34f2cce701e085a233 Homepage: https://github.com/dgasmith/opt_einsum Description: Tensor contraction order optimize (common documentation) Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., np.einsum, dask.array.einsum, pytorch.einsum, tensorflow.einsum) by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. . Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API . This is the common documentation package. Package: python3-opt-einsum Architecture: all Version: 3.3.0-1+Debian.10.buster Priority: optional Section: python Source: opt-einsum (3.3.0-1) Maintainer: Adam Cecile Installed-Size: 247 Depends: python3-numpy (>= 1.7~), python3:any Suggests: python-opt-einsum-doc Filename: pool-buster/opt-einsum/python3-opt-einsum_3.3.0-1+Debian.10.buster_all.deb Size: 50318 MD5sum: 0894eeb31b741cb4bcafa1b4868c6318 SHA1: 83ce357603dc405c115178dbaf941d013fe82a40 SHA256: 77a16d372df3a0a82d66a82591315b38c6ef1dce67886b395bad0d723559a8da SHA512: a2fcace88d5e6e5791695d25c10e10f498e643d2f97d3a3854c4cb10428b1b25ffb528bf65c384e6bc8e28a9ebff6967e56636fb3f48427711b1c754b749ea3c Homepage: https://github.com/dgasmith/opt_einsum Description: Tensor contraction order optimizer (Python 3) Optimized einsum can significantly reduce the overall execution time of einsum-like expressions (e.g., np.einsum, dask.array.einsum, pytorch.einsum, tensorflow.einsum) by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. . Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API . This package installs the library for Python 3. Package: libprotobuf-java Architecture: all Version: 3.4.0-0+Debian-buster-testing Priority: optional Section: java Source: protobuf (3.4.0-0) Maintainer: Debian protobuf maintainers Installed-Size: 759 Filename: pool-buster/protobuf/libprotobuf-java_3.4.0-0+Debian-buster-testing_all.deb Size: 688524 MD5sum: f7470ba26cd23cc9248bcf96b8976257 SHA1: 53955c27fb595f7de2d0662d701b1f4685c6c8f2 SHA256: ac1df138e0d571c60d424c760a5b9ddba81459310742972f337e5af6a1539d92 SHA512: 815a95cce212f198d2bfb8b915e9630acc48646b51699513768b93a975dade040d3ec1ca69510790bd37d66a545034c5d664d0215b3fb9be4411e78f6849a0d0 Homepage: https://github.com/google/protobuf/ Description: Java bindings for protocol buffers Protocol buffers are a flexible, efficient, automated mechanism for serializing structured data - similar to XML, but smaller, faster, and simpler. You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the Java bindings for the protocol buffers. You will need the protoc tool (in the protobuf-compiler package) to compile your definition to Java classes, and then the modules in this package will allow you to use those classes in your programs. 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You define how you want your data to be structured once, then you can use special generated source code to easily write and read your structured data to and from a variety of data streams and using a variety of languages. You can even update your data structure without breaking deployed programs that are compiled against the "old" format. . Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. . This package contains the Java bindings for the protocol buffers. You will need the protoc tool (in the protobuf-compiler package) to compile your definition to Java classes, and then the modules in this package will allow you to use those classes in your programs. Package: python-backports.weakref Architecture: all Version: 1.0~rc1-1+Debian-buster-testing Priority: optional Section: python Source: backports.weakref (1.0~rc1-1) Maintainer: Adam Cecile Installed-Size: 27 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-backports.weakref-doc Filename: pool-buster/protobuf/python-backports.weakref_1.0~rc1-1+Debian-buster-testing_all.deb Size: 6172 MD5sum: 8de15705372178b39e9b33cb7686dc1b SHA1: 231b4669c51b1365441a58efd6207f6daaa08319 SHA256: 18ced654b5908ca3a03bfde720d7125ce2228f8fbe7b5385c602427dd46edd79 SHA512: 70560bd3be9acc3ca00412f429c8bc3ba4081dbe08dee6c660d97df6e0665450290a05e6cf57f2b72f877741ab06ff695c6b0658b02a0c813b9158937258d5be Homepage: https://pypi.python.org/pypi/backports.weakref/ Description: Backport of new features in Python's weakref module (Python 2) This package provides backports of new features in Python’s weakref module under the backports namespace. . This package installs the library for Python 2. Package: python3-backports.weakref Architecture: all Version: 1.0~rc1-1+Debian-buster-testing Priority: optional Section: python Source: backports.weakref (1.0~rc1-1) Maintainer: Adam Cecile Installed-Size: 27 Depends: python3:any (>= 3.3.2-2~) Suggests: python-backports.weakref-doc Filename: pool-buster/protobuf/python3-backports.weakref_1.0~rc1-1+Debian-buster-testing_all.deb Size: 6248 MD5sum: aae6140384839d464369929cc94aeda9 SHA1: 843a59d7e7017cd312b4b0292cbfde5004c70840 SHA256: 81eff5db6fe82557ff16d2267c1e070286bb599a7d27c9bdd04627046f9a425e SHA512: bcc6514d929d3013096ef0418f9bced7c6435998b91ab26f50d1804db7993e788e252cf335a1b0b38d23c0dc865763df1d0a190c718052a7f6332ef28c3205d8 Homepage: https://pypi.python.org/pypi/backports.weakref/ Description: Backport of new features in Python's weakref module (Python 3) This package provides backports of new features in Python’s weakref module under the backports namespace. . This package installs the library for Python 3. Package: python-pyglet Architecture: all Version: 1.3.0-1.1~0+Debian.buster.testing Priority: optional Section: python Source: pyglet (1.3.0-1.1~0) Maintainer: Debian Python Modules Team Installed-Size: 6941 Provides: python2.7-pyglet Depends: libgl1 | libgl1-mesa-swx11, libglu1 | libglu1-mesa, libgtk2.0-0, python-ctypes | python (>= 2.5), python-future, python:any (<< 2.8), python:any (>= 2.7~) Recommends: libasound2 | libopenal1 Filename: pool-buster/pyglet/python-pyglet_1.3.0-1.1~0+Debian.buster.testing_all.deb Size: 1435236 MD5sum: b1446988e97f305710c8df5f0204f311 SHA1: 3cbd310c6a3ce1750d537269b7b3f77578710350 SHA256: 27e7ebcd88804d5ddabb78db1f0d055f6c46d1662aeecb5359e51190dd30df30 SHA512: 52e3e339835b10b3137fcf2fe15e78780d012f0d793f0f83a9d4b28964b8caa46189bda34fa0f4d9474c17c6cf033d83d2ba546354b6311c91ece2520c19b481 Homepage: http://www.pyglet.org Description: cross-platform windowing and multimedia library (Python 2) This library provides an object-oriented programming interface for developing games and other visually-rich applications with Python. pyglet has virtually no external dependencies. For most applications and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. It also handles multiple windows and fully aware of multi-monitor setups. . pyglet might be seen as an alternative to PyGame. Package: python3-pyglet Architecture: all Version: 1.3.0-1.1~0+Debian.buster.testing Priority: optional Section: python Source: pyglet (1.3.0-1.1~0) Maintainer: Debian Python Modules Team Installed-Size: 6936 Depends: libgl1 | libgl1-mesa-swx11, libglu1 | libglu1-mesa, libgtk2.0-0, python3-future, python3:any (>= 3.4~) Recommends: libasound2 | libopenal1 Filename: pool-buster/pyglet/python3-pyglet_1.3.0-1.1~0+Debian.buster.testing_all.deb Size: 1434312 MD5sum: dffbb1623aa44e41076e95f60fb0d980 SHA1: 36c1fc527ade3d7471ccdc93aa8911db49363a4e SHA256: b3736c5f92b7d0f43d211bcfc772dcf3d6e6c49de140a46586fcd29a9534abc2 SHA512: 5518bcd229223ae598e83b43d199334220ed7f24fa6ed15a9d056fef952509c5f447f49aa214b3c890e68d58b9d38dada1765974a4b66629deeda24a7faff1d0 Homepage: http://www.pyglet.org Description: cross-platform windowing and multimedia library (Python 3) This library provides an object-oriented programming interface for developing games and other visually-rich applications with Python. pyglet has virtually no external dependencies. For most applications and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. It also handles multiple windows and fully aware of multi-monitor setups. . pyglet might be seen as an alternative to PyGame. Package: python-astor-doc Architecture: all Version: 0.7.1-1+Debian.buster.testing Priority: optional Section: doc Source: astor (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 149 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.0+dfsg) Filename: pool-buster/python-astor/python-astor-doc_0.7.1-1+Debian.buster.testing_all.deb Size: 36950 MD5sum: 97bb408c1edd8bc2617c37d1bc86f320 SHA1: 3ed4e26b94ac9efb937ca2a7113897d9af96f11d SHA256: 4547ac3085a557edf8bfadb0abb4dbbc1c3280cc959ba9f5e835642549b3c6d4 SHA512: 102df35cfdb56eae2760cc5327753abb234dcb56570566e1679ad99255a3b42ef3bede1fde2741decd5f977e2a1c77d1173ecadb38a12144530875e265dd5c9f Homepage: https://github.com/berkerpeksag/astor Description: Easy manipulation of Python source via the AST (common documentation) Astor is designed to allow easy manipulation of Python source via the AST. . There are some other similar libraries, but astor focuses on the following areas: * Round-trip back to Python via Armin Ronacher's codegen.py module: - Modified AST doesn’t need linenumbers, ctx, etc. or otherwise be directly compileable - Easy to read generated code as, well, code * Dump pretty-printing of AST - Harder to read than round-tripped code, but more accurate to figure out what is going on - Easier to read than dump from built-in AST module * Non-recursive treewalk - Sometimes you want a recursive treewalk (and astor supports that, starting at any node on the tree), but sometimes you don't need to do that. astor doesn’t require you to explicitly visit sub-nodes unless you want to - You can add code that executes before a node's children are visited - You can add code that executes after a node's children are visited - You can add code that executes and keeps the node's children from being visited (and optionally visit them yourself via a recursive call) - Write functions to access the tree based on object names and/or attribute names - Enjoy easy access to parent node(s) for tree rewriting . This is the common documentation package. Package: python-astor-doc Architecture: all Version: 0.7.1-2+Debian.buster.testing Priority: optional Section: doc Source: astor (0.7.1-2) Maintainer: Adam Cecile Installed-Size: 161 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg) Filename: pool-buster/python-astor/python-astor-doc_0.7.1-2+Debian.buster.testing_all.deb Size: 40214 MD5sum: f3dcf75bb50c7d4c58ed75c31112e9fc SHA1: fdf35977e7bd4521e68c269b6770d52ccf0d8ac5 SHA256: ff28e282f54b3d51c8b5b214ca308b609e17f6aeddc8bca81e7f3116e903740f SHA512: 40d6c8c40eb882fff72d6ef2e6e7e9fb5d503ea84adab29c927352ebda8063b2a9ad1a9dea2d0b60df45193f8b593e2a394429549cee57a6c5abdd1faff9c616 Homepage: https://github.com/berkerpeksag/astor Description: Easy manipulation of Python source via the AST (common documentation) Astor is designed to allow easy manipulation of Python source via the AST. . There are some other similar libraries, but astor focuses on the following areas: * Round-trip back to Python via Armin Ronacher's codegen.py module: - Modified AST doesn’t need linenumbers, ctx, etc. or otherwise be directly compileable - Easy to read generated code as, well, code * Dump pretty-printing of AST - Harder to read than round-tripped code, but more accurate to figure out what is going on - Easier to read than dump from built-in AST module * Non-recursive treewalk - Sometimes you want a recursive treewalk (and astor supports that, starting at any node on the tree), but sometimes you don't need to do that. astor doesn’t require you to explicitly visit sub-nodes unless you want to - You can add code that executes before a node's children are visited - You can add code that executes after a node's children are visited - You can add code that executes and keeps the node's children from being visited (and optionally visit them yourself via a recursive call) - Write functions to access the tree based on object names and/or attribute names - Enjoy easy access to parent node(s) for tree rewriting . This is the common documentation package. Package: python-astor Architecture: all Version: 0.7.1-1+Debian.buster.testing Priority: optional Section: python Source: astor (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 9558 Depends: python:any (<< 2.8), python:any (>= 2.7~) Suggests: python-astor-doc Filename: pool-buster/python-astor/python-astor_0.7.1-1+Debian.buster.testing_all.deb Size: 1585400 MD5sum: b79acd7bb21b9d529c453212b3532e4f SHA1: 4a4f25d1771eea6a141e2bf5b8c8500d555501eb SHA256: 36517e4fa47ff57a53dda85ce2fa446d8d10c4bb2ba8ce1c4dfb3a312bdf3a4e SHA512: 4b8f8dce12da39b37b010ff10a66c06e51a9cd0d77ed01586fdccd36203e3fe57655e238bdf3b2f83e1c26f267c6230bb143a105fd0d9f11df567e8f3d33063e Homepage: https://github.com/berkerpeksag/astor Description: Easy manipulation of Python source via the AST (Python 2) Astor is designed to allow easy manipulation of Python source via the AST. . There are some other similar libraries, but astor focuses on the following areas: * Round-trip back to Python via Armin Ronacher's codegen.py module: - Modified AST doesn’t need linenumbers, ctx, etc. or otherwise be directly compileable - Easy to read generated code as, well, code * Dump pretty-printing of AST - Harder to read than round-tripped code, but more accurate to figure out what is going on - Easier to read than dump from built-in AST module * Non-recursive treewalk - Sometimes you want a recursive treewalk (and astor supports that, starting at any node on the tree), but sometimes you don't need to do that. astor doesn’t require you to explicitly visit sub-nodes unless you want to - You can add code that executes before a node's children are visited - You can add code that executes after a node's children are visited - You can add code that executes and keeps the node's children from being visited (and optionally visit them yourself via a recursive call) - Write functions to access the tree based on object names and/or attribute names - Enjoy easy access to parent node(s) for tree rewriting . This package installs the library for Python 2. Package: python-astor Architecture: all Version: 0.7.1-2+Debian.buster.testing Priority: optional Section: python Source: astor (0.7.1-2) Maintainer: Adam Cecile Installed-Size: 98 Depends: python:any (<< 2.8), python:any (>= 2.7~) Suggests: python-astor-doc Filename: pool-buster/python-astor/python-astor_0.7.1-2+Debian.buster.testing_all.deb Size: 25274 MD5sum: a126cd74a4da485a57650c889ff62a9b SHA1: 644d4e6c881eb87fe34b80e0fdaa146bd5016460 SHA256: f200c5181bdc8d5e41a81cddc7334b034820600ca847627f1266c4abdc9adffe SHA512: 9269b6180aca19481a8d322052c53626ae029fef5bf7633a0e5b5fc4f5d186c6ef4b8a968de39f9a53901e4a6f9ddc22d4d7e93345d86ce581a009a3b15ae03f Homepage: https://github.com/berkerpeksag/astor Description: Easy manipulation of Python source via the AST (Python 2) Astor is designed to allow easy manipulation of Python source via the AST. . There are some other similar libraries, but astor focuses on the following areas: * Round-trip back to Python via Armin Ronacher's codegen.py module: - Modified AST doesn’t need linenumbers, ctx, etc. or otherwise be directly compileable - Easy to read generated code as, well, code * Dump pretty-printing of AST - Harder to read than round-tripped code, but more accurate to figure out what is going on - Easier to read than dump from built-in AST module * Non-recursive treewalk - Sometimes you want a recursive treewalk (and astor supports that, starting at any node on the tree), but sometimes you don't need to do that. astor doesn’t require you to explicitly visit sub-nodes unless you want to - You can add code that executes before a node's children are visited - You can add code that executes after a node's children are visited - You can add code that executes and keeps the node's children from being visited (and optionally visit them yourself via a recursive call) - Write functions to access the tree based on object names and/or attribute names - Enjoy easy access to parent node(s) for tree rewriting . This package installs the library for Python 2. Package: python3-astor Architecture: all Version: 0.7.1-1+Debian.buster.testing Priority: optional Section: python Source: astor (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 98 Depends: python3:any (>= 3.3~) Suggests: python-astor-doc Filename: pool-buster/python-astor/python3-astor_0.7.1-1+Debian.buster.testing_all.deb Size: 25042 MD5sum: da0efcfc94a6c9b82602631531f94c13 SHA1: 19e844ef11c40b01637245c64de398e1143f435d SHA256: 0528951bfe87863bfe330acfe5675eda3fbf29bd5e36e746c084805025cf1414 SHA512: 08d78f4d1a27013712a00996884fa66fb66fbc587828bdc09f72970f861c6fe6bb39e84207871c3054580a3f594477e6218f3e3b537cd6491aaacaeaa0f8337d Homepage: https://github.com/berkerpeksag/astor Description: Easy manipulation of Python source via the AST (Python 3) Astor is designed to allow easy manipulation of Python source via the AST. . There are some other similar libraries, but astor focuses on the following areas: * Round-trip back to Python via Armin Ronacher's codegen.py module: - Modified AST doesn’t need linenumbers, ctx, etc. or otherwise be directly compileable - Easy to read generated code as, well, code * Dump pretty-printing of AST - Harder to read than round-tripped code, but more accurate to figure out what is going on - Easier to read than dump from built-in AST module * Non-recursive treewalk - Sometimes you want a recursive treewalk (and astor supports that, starting at any node on the tree), but sometimes you don't need to do that. astor doesn’t require you to explicitly visit sub-nodes unless you want to - You can add code that executes before a node's children are visited - You can add code that executes after a node's children are visited - You can add code that executes and keeps the node's children from being visited (and optionally visit them yourself via a recursive call) - Write functions to access the tree based on object names and/or attribute names - Enjoy easy access to parent node(s) for tree rewriting . This package installs the library for Python 3. Package: python3-astor Architecture: all Version: 0.7.1-2+Debian.buster.testing Priority: optional Section: python Source: astor (0.7.1-2) Maintainer: Adam Cecile Installed-Size: 98 Depends: python3:any (>= 3.3~) Suggests: python-astor-doc Filename: pool-buster/python-astor/python3-astor_0.7.1-2+Debian.buster.testing_all.deb Size: 25362 MD5sum: efbf88799c2b88663a040d8c1c3b800a SHA1: 487b6d5e19ad72de44251803a11a4f7b2e8bc76e SHA256: 02ec2608f252f3e066c2f4498163c67d4115713033fa559f503fddb67384eeb9 SHA512: a5bce4a5910d731105ca82784dfb7aab27c31e0b72d052368101a6151ab3c4b994b0d41164a8bfd30f337ae07c4a1c0fc24318a62033c5ffe85cd5dde123dbde Homepage: https://github.com/berkerpeksag/astor Description: Easy manipulation of Python source via the AST (Python 3) Astor is designed to allow easy manipulation of Python source via the AST. . There are some other similar libraries, but astor focuses on the following areas: * Round-trip back to Python via Armin Ronacher's codegen.py module: - Modified AST doesn’t need linenumbers, ctx, etc. or otherwise be directly compileable - Easy to read generated code as, well, code * Dump pretty-printing of AST - Harder to read than round-tripped code, but more accurate to figure out what is going on - Easier to read than dump from built-in AST module * Non-recursive treewalk - Sometimes you want a recursive treewalk (and astor supports that, starting at any node on the tree), but sometimes you don't need to do that. astor doesn’t require you to explicitly visit sub-nodes unless you want to - You can add code that executes before a node's children are visited - You can add code that executes after a node's children are visited - You can add code that executes and keeps the node's children from being visited (and optionally visit them yourself via a recursive call) - Write functions to access the tree based on object names and/or attribute names - Enjoy easy access to parent node(s) for tree rewriting . This package installs the library for Python 3. Package: python-astunparse-doc Architecture: all Version: 1.5.0-1+Debian.buster.testing Priority: optional Section: doc Source: astunparse (1.5.0-1) Maintainer: Adam Cecile Installed-Size: 144 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.0+dfsg) Filename: pool-buster/python-astunparse/python-astunparse-doc_1.5.0-1+Debian.buster.testing_all.deb Size: 22854 MD5sum: 0143d8c8df675aae48c32989ad97d2b8 SHA1: 83cdc8c2895067e7bce9a4acc0ca9cb18456f06b SHA256: 5962cf7d4814efb7a933cc49d8bc60d61a41ea330ca954cde8428f3f3a2ddfde SHA512: 8f94be45e8aa27870243d7292b4eb71d13551da3eab69be6ef29ec2a54c05de8c093164a70d9e813c6788bab27435fe63c814f797615b4f77705f99b2ea2ed94 Homepage: https://github.com/simonpercivall/astunparse Description: AST unparser for Python (common documentation) This is a factored out version of unparse found in the Python source distribution under Demo/parser in Python 2 and under Tools/parser in Python 3. . This is the common documentation package. Package: python-astunparse-doc Architecture: all Version: 1.6.2-1+Debian.buster.testing Priority: optional Section: doc Source: astunparse (1.6.2-1) Maintainer: Adam Cecile Installed-Size: 158 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg) Filename: pool-buster/python-astunparse/python-astunparse-doc_1.6.2-1+Debian.buster.testing_all.deb Size: 26466 MD5sum: 6b4534395cb911fcd9f491d1bae4703f SHA1: 0175a3666f10c40e57cee25c0dde47654f60b8e5 SHA256: 51acb5d84c971df37860c2ca5a968fc122150831464c63745b40f25ad2a64f86 SHA512: c28cfed5e67549d1fc0869d0c0f9015dea6ac45544e652b35246526429ef0958a72aa4501168610c917fabef1cacd9a0a934c824d455425faa692804f08c4495 Homepage: https://github.com/simonpercivall/astunparse Description: AST unparser for Python (common documentation) This is a factored out version of unparse found in the Python source distribution under Demo/parser in Python 2 and under Tools/parser in Python 3. . This is the common documentation package. Package: python-astunparse Architecture: all Version: 1.5.0-1+Debian.buster.testing Priority: optional Section: python Source: astunparse (1.5.0-1) Maintainer: Adam Cecile Installed-Size: 55 Depends: python-six (<< 2.0), python-six (>= 1.6.1), python:any (<< 2.8), python:any (>= 2.7~) Suggests: python-astunparse-doc Filename: pool-buster/python-astunparse/python-astunparse_1.5.0-1+Debian.buster.testing_all.deb Size: 11648 MD5sum: ed2e00c5485ae90e1955999e4e987d0b SHA1: e7e7eb1c7c484f023311ef105f3a836792884fe7 SHA256: cc98c17e240e912c4f4477fbec90f1e370541c1df5e792df86ae017bbd129ba1 SHA512: 4e7bf9948676fe9219a3a19e4f9a2360ba64231b868550b3f3bab906ed9d65cf73911afb4552653ab7455e80cc121ff226eac48725db363da40cf192aa9cd43d Homepage: https://github.com/simonpercivall/astunparse Description: AST unparser for Python (Python 2) This is a factored out version of unparse found in the Python source distribution under Demo/parser in Python 2 and under Tools/parser in Python 3. . This package installs the library for Python 2. Package: python-astunparse Architecture: all Version: 1.6.2-1+Debian.buster.testing Priority: optional Section: python Source: astunparse (1.6.2-1) Maintainer: Adam Cecile Installed-Size: 58 Depends: python-six (<< 2.0), python-six (>= 1.6.1), python:any (<< 2.8), python:any (>= 2.7~) Suggests: python-astunparse-doc Filename: pool-buster/python-astunparse/python-astunparse_1.6.2-1+Debian.buster.testing_all.deb Size: 12352 MD5sum: 771705257aea6c7a1c9c906b5e67e337 SHA1: 2c1a52f8d08005a29b28344789e8b41f18eb6a5f SHA256: cb894e2d29c06cfcc5c867642ee8f2d6c18342b8ee5dd405d1b6f8bccd1a029f SHA512: b2920349b10e86a9f6fd638009348693613c9a8e38a06282c54b2a89c6aa9d56db1fab09cb77ccdc1220ab82fe4325d58150240c843543f087277dfb5837b6a2 Homepage: https://github.com/simonpercivall/astunparse Description: AST unparser for Python (Python 2) This is a factored out version of unparse found in the Python source distribution under Demo/parser in Python 2 and under Tools/parser in Python 3. . This package installs the library for Python 2. Package: python3-astunparse Architecture: all Version: 1.5.0-1+Debian.buster.testing Priority: optional Section: python Source: astunparse (1.5.0-1) Maintainer: Adam Cecile Installed-Size: 55 Depends: python3-six (<< 2.0), python3-six (>= 1.6.1), python3:any (>= 3.3~) Suggests: python-astunparse-doc Filename: pool-buster/python-astunparse/python3-astunparse_1.5.0-1+Debian.buster.testing_all.deb Size: 11728 MD5sum: 759dbb0dac7e3e2388cb0bf4633c56c8 SHA1: 4eccc2ff05cf6984e599bc9e72c2e1206b653671 SHA256: ed348f6d2e82f964e6408aebe437300abbdd66260c724587b87649221cef6a96 SHA512: a20f9a89e6943794f381694584384b915346fd33a414221a3558dc3e617d1d225688ade92783b573cc4a015bd5acaed2bd3795339ee36511456732a841804eb8 Homepage: https://github.com/simonpercivall/astunparse Description: AST unparser for Python (Python 3) This is a factored out version of unparse found in the Python source distribution under Demo/parser in Python 2 and under Tools/parser in Python 3. . This package installs the library for Python 3. Package: python3-astunparse Architecture: all Version: 1.6.2-1+Debian.buster.testing Priority: optional Section: python Source: astunparse (1.6.2-1) Maintainer: Adam Cecile Installed-Size: 58 Depends: python3-six (<< 2.0), python3-six (>= 1.6.1), python3:any (>= 3.3~) Suggests: python-astunparse-doc Filename: pool-buster/python-astunparse/python3-astunparse_1.6.2-1+Debian.buster.testing_all.deb Size: 12432 MD5sum: 0f17c3e347f060971441b4c7ba305654 SHA1: 41c1469535e7ba5337a72abf3135584dbc3afc5b SHA256: 84cba5d30d50b43d5323d80202b179d800ce9df2cb26fa52fd2fe53c1d98f4d7 SHA512: 1ac3ce49fe4d9853e2abce8444dfb3bc5150b74e96086deb65bc6446627837988bae2378c5f8a7179196c3ac28b433c31871810e53374d07cb27c83d68e189f3 Homepage: https://github.com/simonpercivall/astunparse Description: AST unparser for Python (Python 3) This is a factored out version of unparse found in the Python source distribution under Demo/parser in Python 2 and under Tools/parser in Python 3. . This package installs the library for Python 3. Package: python-bayes-opt-doc Architecture: all Version: 1.0.1-0+Debian.buster.10 Priority: optional Section: doc Source: python-bayesian-optimization (1.0.1-0) Maintainer: Adam Cecile Installed-Size: 16975 Filename: pool-buster/python-bayesian-optimization/python-bayes-opt-doc_1.0.1-0+Debian.buster.10_all.deb Size: 17000560 MD5sum: 925c11a89d51fdd70305065a3bd5d946 SHA1: 8c2406caff896000450ad31768ec575d480f85da SHA256: 6c08d56e4f7f5f98039b11b701d7b1a6306c97ea33b1a7f84a2dfbb71fd91d60 SHA512: fa440ec58da2bd34e756ef7b5078ea15ac76904fc0e457ddfce4359deb697b17d913fb320c90604aad8e235c9e27543fcbb61896b185145f6073734442a2012c Homepage: https://github.com/fmfn/BayesianOptimization Description: Python implementation of optimization with gaussian processes (common doc) This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. . This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. . This is the common documentation package. Package: python3-bayes-opt Architecture: all Version: 1.0.1-0+Debian.buster.10 Priority: optional Section: python Source: python-bayesian-optimization (1.0.1-0) Maintainer: Adam Cecile Installed-Size: 47 Depends: python3-numpy, python3-scipy, python3-sklearn, python3:any (>= 3.4~) Suggests: python-bayes-opt-doc Filename: pool-buster/python-bayesian-optimization/python3-bayes-opt_1.0.1-0+Debian.buster.10_all.deb Size: 10940 MD5sum: 34641520c283c18032a937db0930c94c SHA1: c550dfa304f9b1bf09188d600c462d12096b1203 SHA256: a5426a4eca6bcc32d6343be0473ef4be625f87480bf55ac8eb930b071bf556a8 SHA512: 1d80c9a43c579a089a454da2611b65697bd78aeb979506b65db69c4a7d64e414c49908a4cc809a33d307c610d5aaf2aa0b4aeffd72bac62beb7f59eacf990d5a Homepage: https://github.com/fmfn/BayesianOptimization Description: Python implementation of optimization with gaussian processes (Python 3) This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. . This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. . This package installs the library for Python 3. Package: python-box2d-doc Architecture: all Version: 2.3.2~dfsg-2.1~0+Debian.buster.testing Priority: optional Section: doc Source: python-box2d (2.3.2~dfsg-2.1~0) Maintainer: Debian Sugar Team Installed-Size: 963 Depends: ttf-bitstream-vera Suggests: python-box2d, python-pygame Filename: pool-buster/python-box2d/python-box2d-doc_2.3.2~dfsg-2.1~0+Debian.buster.testing_all.deb Size: 688582 MD5sum: 3c6281893a48d855cc7aabe00c229889 SHA1: 38b95d5aed449ac31bd2d15f0f962d7f19363e22 SHA256: 4198a45374054c1b58d845db4bc592a5dd4c2fc4cb5d59c67b91d5d656a4d069 SHA512: e131d690b64bb4d625622e7251b0542f73d5a3e1bacf2813ece3e6d76ceb017919d523b0bbc643a23a913872e64bb4fbfac3db6c37092cd3600b8a90e9b41667 Homepage: https://github.com/pybox2d/pybox2d Description: 2D Game Physics for Python - documentation pybox2d is a 2D physics library for your games and simple simulations. It's based on the Box2D library, written in C++. It supports several shape types (circle, polygon, thin line segments), and quite a few joint types (revolute, prismatic, wheel, etc.). . This package contains documentation. Package: python-gast Architecture: all Version: 0.2.0-1+Debian.buster.testing Priority: optional Section: python Source: gast (0.2.0-1) Maintainer: Adam Cecile Installed-Size: 46 Depends: python:any (<< 2.8), python:any (>= 2.7~) Filename: pool-buster/python-gast/python-gast_0.2.0-1+Debian.buster.testing_all.deb Size: 8176 MD5sum: 9f69056fc6acf10c5709c21110516b17 SHA1: 3c41c6b6a151aecb2dd71bc050df4c4566ac723c SHA256: fb34977b7946a6be520a587b100aa371d281c883a239d9be264641b9ec02f8e6 SHA512: 4117663a73cc8d1d69cb5c76b52f86a8c0178cd4a44f2e81af2d68c6b1e3c1417c1bcd7a36a3704d3de099248a794e9dedc7eaffb78be06f5c66508bc95fe436 Homepage: https://github.com/serge-sans-paille/gast Description: Generic AST to represent Python2 and Python3 AST (Python 2) GAST provides a compatibility layer between the AST of various Python versions, as produced by ast.parse from the standard ast module. . This package installs the library for Python 2. Package: python-gast Architecture: all Version: 0.2.2-1+Debian.buster.testing Priority: optional Section: python Source: gast (0.2.2-1) Maintainer: Adam Cecile Installed-Size: 45 Depends: python:any (<< 2.8), python:any (>= 2.7~) Filename: pool-buster/python-gast/python-gast_0.2.2-1+Debian.buster.testing_all.deb Size: 8116 MD5sum: c5cf1bfb58bf232b52bdba04681bb9f1 SHA1: b48cf444940bbd599efcf06d5e2616e6339cdda4 SHA256: cc5bc4d0a45731e407f2a44d3fb189592dfd57759823bfda650b5e32a69e10b3 SHA512: ee6e7cc630273d2bf7a2cf60503b1fe909777ea6c2b19199afb7d106ba19b8733ef626f25c3d7fcc1ac15116372648012748d56c0deeededefe6376e5314f7f3 Homepage: https://github.com/serge-sans-paille/gast Description: Generic AST to represent Python2 and Python3 AST (Python 2) GAST provides a compatibility layer between the AST of various Python versions, as produced by ast.parse from the standard ast module. . This package installs the library for Python 2. Package: python3-gast Architecture: all Version: 0.2.0-1+Debian.buster.testing Priority: optional Section: python Source: gast (0.2.0-1) Maintainer: Adam Cecile Installed-Size: 46 Depends: python3:any (>= 3.3~) Filename: pool-buster/python-gast/python3-gast_0.2.0-1+Debian.buster.testing_all.deb Size: 8240 MD5sum: b9c1da80d860177bc9ae8e58da64bd46 SHA1: e9881cc200c729345ff4c131fb5c0bebd33b48cf SHA256: 47c69a604d5ae96ed8147fc3bb95d0a9ef597cf9f5105cac213b571bbcb7dbda SHA512: f152ec24d25acbdc5ef50d6825608da23c403bd2067eaa4c8a17c35403842fd89797b147e673fbb47020bea316b027a2728b6d01672873d06e85d5a4ff450622 Homepage: https://github.com/serge-sans-paille/gast Description: Generic AST to represent Python2 and Python3 AST (Python 3) GAST provides a compatibility layer between the AST of various Python versions, as produced by ast.parse from the standard ast module. . This package installs the library for Python 3. Package: python3-gast Architecture: all Version: 0.2.2-1+Debian.buster.testing Priority: optional Section: python Source: gast (0.2.2-1) Maintainer: Adam Cecile Installed-Size: 45 Depends: python3:any (>= 3.3~) Filename: pool-buster/python-gast/python3-gast_0.2.2-1+Debian.buster.testing_all.deb Size: 8212 MD5sum: 21ab446ba593978bd3781f5915aa53be SHA1: d6b7de56b0d6141537be9c2aea3beacd312dfcb3 SHA256: a37e2d639019c93ddc44eb11806853558608239270fbdebabcfc23bda0b77d4a SHA512: 5b1a182057eaff995c0f5d1b268a9d6a23834b6a175b3fcf11d77f2dda52c599cd5556f0602df4e209a4e9376f56beec74fa78581fc645520895ff0758054d0d Homepage: https://github.com/serge-sans-paille/gast Description: Generic AST to represent Python2 and Python3 AST (Python 3) GAST provides a compatibility layer between the AST of various Python versions, as produced by ast.parse from the standard ast module. . This package installs the library for Python 3. Package: python-grpcio-doc Architecture: all Version: 1.14.1-1+Debian.buster.testing Priority: optional Section: doc Source: python-grpcio (1.14.1-1) Maintainer: Adam Cecile Installed-Size: 1175 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.0+dfsg) Filename: pool-buster/python-grpcio/python-grpcio-doc_1.14.1-1+Debian.buster.testing_all.deb Size: 77938 MD5sum: e35f16edcf842a27a03bf65f0482150c SHA1: c97cc5c65e4aae142fad6d9636d840ed752fe76e SHA256: b568c5a5913bbad742fbd72c4db521f9151c304687171bba4447e938b914ceb1 SHA512: a0b7ddbecfbfdaa3ed9de93fe7381b605a74b17063ff401bf16171e55e2f9abb4255bd7b03e5e5a4bf4f3f97d3c7dc96c269bedb75666a44c3b29832b7f8fa40 Homepage: https://pypi.org/project/grpcio/ Description: High performance, open-source universal RPC framework (common documentation) gRPC is a modern, open source, high-performance remote procedure call (RPC) framework that can run anywhere. . It enables client and server applications to communicate transparently, and makes it easier to build connected systems. . This is the common documentation package. 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Users of Python 3.7 and beyond should use the standard library module, since for these versions, importlib_resources just delegates to that module. . This package installs the library for Python 3. Package: python-onnx-tf-doc Architecture: all Version: 1.2.0-1+Debian.buster.testing Priority: optional Section: doc Source: onnx-tensorflow (1.2.0-1) Maintainer: Adam Cecile Installed-Size: 17 Filename: pool-buster/python-onnx-tf/python-onnx-tf-doc_1.2.0-1+Debian.buster.testing_all.deb Size: 5608 MD5sum: 1d17b010326cc4f80b1ea89e7ebb67bc SHA1: ca715db21ad84003c2bfe3aecc032de605ab8a59 SHA256: a0fbad99ac6c4ed0a687a95f97ec4cf757381e624bc19a0623898e8e4f31f744 SHA512: 125f4f7fc34e0177051c78816456e3b95b4b2d569b06a3f240895ac7df586f938d18440deeb6eea19211a8285df8221f7a54cb93f7b101f3294909845cedd180 Homepage: https://github.com/onnx/onnx-tensorflow Description: Tensorflow Backend and Frontend for ONNX (common documentation) Convert models between Tensorflow and ONNX. . 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Package: python3-onnx-tf Architecture: all Version: 1.2.0-1+Debian.buster.testing Priority: optional Section: python Source: onnx-tensorflow (1.2.0-1) Maintainer: Adam Cecile Installed-Size: 344 Depends: python3-onnx, python3-yaml, python3:any (>= 3.4~) Suggests: python-onnx-tf-doc Filename: pool-buster/python-onnx-tf/python3-onnx-tf_1.2.0-1+Debian.buster.testing_all.deb Size: 43470 MD5sum: b4725dc9a0990c5c67f29014661368e0 SHA1: f8e14bbe13a874bc24acbdab176eeaec94e29c97 SHA256: 8b3b4a3d8bd216786f1c4c373bd75d78f081f4613fe3da270fe435b308ade550 SHA512: 36dfad84500eac7a22bb43519600c194ad23c8951f71d2d37667fa047cc104fe6ff5c8c865f97d230dd3d4b00f17c3a0bbca114f97f40e189d99729cc76d4a5d Homepage: https://github.com/onnx/onnx-tensorflow Description: Tensorflow Backend and Frontend for ONNX (Python 3) Convert models between Tensorflow and ONNX. . This package installs the library for Python 3. Package: python-onnx-doc Architecture: all Version: 1.3.0+debian-1+Debian.buster.testing Priority: optional Section: doc Source: onnx (1.3.0+debian-1) Maintainer: Adam Cecile Installed-Size: 238 Filename: pool-buster/python-onnx/python-onnx-doc_1.3.0+debian-1+Debian.buster.testing_all.deb Size: 198978 MD5sum: 9c7e1ae7457cdfb2a957f395ccb45136 SHA1: 099d6d1d99f316341fa4509d32bed75ee5962e7c SHA256: b7281a743189e596dd4d5ce79719e9696ff01a2034ef626a64a25fbb6b6c2013 SHA512: 8937017083f7189e91c12ac644dfb8d1985f95ba602a274c4621560db60d4fdd2387d8cf7b9940b598a41c9982a0f5867a6175c134c778544cf56724854f3159 Homepage: https://onnx.ai/ Description: Open Neural Network Exchange (ONNX) (common documentation) Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem that empowers AI developers to choose the right tools as their project evolves. . ONNX provides an open source format for AI models. . 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. Package: python3-tensorboard Architecture: all Version: 1.5.1-1+Debian.buster.testing Priority: optional Section: python Source: tensorboard (1.5.1-1) Maintainer: Adam Cecile Installed-Size: 3418 Depends: python3-bleach (>= 1.5.0), python3-html5lib (>= 0.9999999), python3-markdown (>= 2.6.8), python3-numpy (>= 1.11.0), python3-protobuf (>= 3.3.0), python3-six (>= 1.10.0), python3-werkzeug (>= 0.11.10), python3:any (>= 3.4~) Breaks: python3-tensorflow (<< 1.5~) Filename: pool-buster/python-tensorboard/1.5.1/python3-tensorboard_1.5.1-1+Debian.buster.testing_all.deb Size: 2926364 MD5sum: 5549ad2181fea3583a913e65b738c0a0 SHA1: b57c8c3531c1744b27c8899027ae87b16b631ba8 SHA256: 0afb4867765c0b52a739bb460e24549147a01e213136977bbd68019790507c0b SHA512: 9ce416ba45e52613b206b40aae5ecfc8c426b5ff9e6c54d9ace0076ece78e0db794f65d135e8db88ff0e139879fbeec5ca07a3dce67d9e024b08a64bdefad0ba Homepage: https://github.com/tensorflow/tensorboard Description: suite of web applications for inspecting TensorFlow runs and graphs (Python 3) The computations you'll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. . To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. Package: python-tensorboard Architecture: all Version: 1.6.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorboard (1.6.0-1) Maintainer: Adam Cecile Installed-Size: 3441 Depends: python-bleach (>= 1.5.0), python-html5lib (>= 0.9999999), python-markdown (>= 2.6.8), python-numpy (>= 1.11.0), python-protobuf (>= 3.3.0), python-six (>= 1.10.0), python-werkzeug (>= 0.11.10), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-concurrent.futures (>= 3) Breaks: python-tensorflow (<< 1.5~) Filename: pool-buster/python-tensorboard/1.6.0/python-tensorboard_1.6.0-1+Debian.buster.testing_all.deb Size: 2943456 MD5sum: 3b76557d318967de92798a271cbd4e29 SHA1: 4dd39fc33ffbaede9ca04e0cfb487848a89c259c SHA256: f25c700019b4f705e1a9159452cdf3be84d6fbd559db0a896d89aa2167a389c4 SHA512: 0766bd18382b7226d3c34f8fc78f6ed7e0fdd9c785c41e2ab73c7195afac9472fa519006da463d96e63bf27420ba5ca11e2d011a0907397f2a61ab5187f33a6c Homepage: https://github.com/tensorflow/tensorboard Description: suite of web applications for inspecting TensorFlow runs and graphs (Python 2) The computations you'll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. . To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. Package: python3-tensorboard Architecture: all Version: 1.6.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorboard (1.6.0-1) Maintainer: Adam Cecile Installed-Size: 3442 Depends: python3-bleach (>= 1.5.0), python3-html5lib (>= 0.9999999), python3-markdown (>= 2.6.8), python3-numpy (>= 1.11.0), python3-protobuf (>= 3.3.0), python3-six (>= 1.10.0), python3-werkzeug (>= 0.11.10), python3:any (>= 3.4~) Breaks: python3-tensorflow (<< 1.5~) Filename: pool-buster/python-tensorboard/1.6.0/python3-tensorboard_1.6.0-1+Debian.buster.testing_all.deb Size: 2943584 MD5sum: a4fb52710d33a0664aaf2de6b9637490 SHA1: f052cee0eadb97d791efd614d87c91ffe95fba15 SHA256: 1fa00f208ec33108cf4c34092830598562e3c2e94df7edfd39b0759a73024b2a SHA512: 303df90d330a14a1a04bb6a6cdfe5dd0fb398be80e10c7a0f596b0f34f7a871ceebd77459af748f5c2348ba65d9b7409159d01ef84b082cfc482f0b4fad6d1d3 Homepage: https://github.com/tensorflow/tensorboard Description: suite of web applications for inspecting TensorFlow runs and graphs (Python 3) The computations you'll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. . To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. Package: python3-tensorboard Architecture: all Version: 1.7.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorboard (1.7.0-1) Maintainer: Adam Cecile Installed-Size: 3549 Depends: python3-bleach (>= 1.5.0), python3-html5lib (>= 0.9999999), python3-markdown (>= 2.6.8), python3-numpy (>= 1.11.0), python3-protobuf (>= 3.3.0), python3-six (>= 1.10.0), python3-werkzeug (>= 0.11.10), python3:any (>= 3.4~) Breaks: python3-tensorflow (>= 1.8.0~), python3-tensorflow (<< 1.7~) Filename: pool-buster/python-tensorboard/1.7.0/python3-tensorboard_1.7.0-1+Debian.buster.testing_all.deb Size: 2993408 MD5sum: cb869c6cd9e28c479dcfc8af07da0853 SHA1: 9f207d918708ceb848a0986fa850329714b556f1 SHA256: cfb4ef320e91c34e8b07a63ff4c2d7b3cecc0f18a71c5a7eb632357de7e88b7d SHA512: 7cfaa867023b4fc965757a67c883aa990b44f9beebd9b464df7b333097be66562c19f8098755de3e87d414bfb9c514d1f42a39d623d69945ae72f6e7c467c416 Homepage: https://github.com/tensorflow/tensorboard Description: suite of web applications for inspecting TensorFlow runs and graphs (Python 3) The computations you'll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. . To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. Package: python3-tensorboard Architecture: all Version: 1.8.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorboard (1.8.0-1) Maintainer: Adam Cecile Installed-Size: 3590 Depends: python3-bleach (>= 1.5.0), python3-html5lib (>= 0.9999999), python3-markdown (>= 2.6.8), python3-numpy (>= 1.11.0), python3-protobuf (>= 3.3.0), python3-six (>= 1.10.0), python3-werkzeug (>= 0.11.10), python3:any (>= 3.4~) Breaks: python3-tensorflow (>= 1.9.0~), python3-tensorflow (<< 1.8~) Filename: pool-buster/python-tensorboard/1.8.0/python3-tensorboard_1.8.0-1+Debian.buster.testing_all.deb Size: 3006060 MD5sum: f3190314461f2a6790bd8b8bc103d10d SHA1: a0c231e4d5fa26fecf51c90cd45d64cfc3863ddd SHA256: 4d22c3b510b3352a2bcf4493b33be9089652a0c40833125071267db9dd4e3936 SHA512: a8289a668d540373c94496784b0c8648e557fb218caaf768b8da3376e953289366c89679034237907d04ab63d4da529fc0ff54ab6f6f41693312c5a95d7e4473 Homepage: https://github.com/tensorflow/tensorboard Description: suite of web applications for inspecting TensorFlow runs and graphs (Python 3) The computations you'll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. . To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. 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To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 2. Package: python3-tensorboard Architecture: all Version: 1.9.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorboard (1.9.0-1) Maintainer: Adam Cecile Installed-Size: 4154 Depends: python3-markdown (>= 2.6.8), python3-numpy (>= 1.11.0), python3-protobuf (>= 3.3.0), python3-six (>= 1.10.0), python3-werkzeug (>= 0.11.10), python3:any (>= 3.4~) Breaks: python3-tensorflow (>= 1.10.0~), python3-tensorflow (<< 1.9.0~) Filename: pool-buster/python-tensorboard/1.9.0/python3-tensorboard_1.9.0-1+Debian.buster.testing_all.deb Size: 3124324 MD5sum: 90134798060836f63a1be25efb12b5dc SHA1: dea4823b094074f0b32301279d5c5b22135bc40f SHA256: 4a0fe7f0c4dff83e600692a218d18ebeb095aba5ffb048765773ce9a44e3ca8c SHA512: c9360b35daf707ce5b44bbe8c01b881e2668cd926e45f03e0d5b48bec8a696192915e3077da28061240b44f6b8999b38d02414437006c59ea423dcafaef5030f Homepage: https://github.com/tensorflow/tensorboard Description: suite of web applications for inspecting TensorFlow runs and graphs (Python 3) The computations you'll use TensorFlow for - like training a massive deep neural network - can be complex and confusing. . To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. . You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. . This package installs the library for Python 3. Package: tensorflow-tools Architecture: all Version: 1.10.1-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.10.1-1) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.10.1-1), python3-tensorflow (<< 1.10.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.10.1/tensorflow-tools_1.10.1-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6272 MD5sum: 2e077bd310894e618407371ef1f4e33b SHA1: 88148b9293a0e456bf469dfae458d2c43f153000 SHA256: 097c7b2c3e57fd51df95517c925c47bcf0c35db88305bc91f0d5516201d7a7b0 SHA512: 5d3ef6e4e8ff2a5de35079fad245a754308029e759c9fccc625dd5e0558b4e5b16e9d591972e089bac5bb28ca41fe5b26d9d110e9c12d572946549b9b54e5bf3 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.11.0-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.11.0-1) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.11.0-1), python3-tensorflow (<< 1.11.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.11.0/tensorflow-tools_1.11.0-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6320 MD5sum: a5f2acd4b76ac5fcad89dfb048418c11 SHA1: 98cb6c0f4d9f0bb4515e4585941d1971652db5c4 SHA256: 7a62b535c31b4d9e07ad45aaadf478ff8bd97f4d0f9be7f763a37c6231e2e2b0 SHA512: 6bc21493c7f58a3978ba4372cc4a1e273218ee752a818a8a69b22379fde9974af001cbd755c8bed61a63695df0948c67e90066827078e0affbd559e192ac9fd3 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. 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Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.12.0-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.12.0-1) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.12.0-1), python3-tensorflow (<< 1.12.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.12.0/tensorflow-tools_1.12.0-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6464 MD5sum: 48f8cb390e84e740fb8da3c5cd206570 SHA1: 6520f819679d4adf4a8673f606068b3a5b843b16 SHA256: 83c31cd2400738a95fe11b71a90b5fa42285c076b412b414468d8554b9312aea SHA512: 6c1beb69fd07edf0c2485d67621125130519c9cc853cd8e725cb74decfc7efd822bb8f45657d09ebcc1e4a628cc9769fa01108ae38ba7ebe52a3de5a374f8b3c Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.12.2-1+Debian.buster.10+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.12.2-1) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.12.2-1), python3-tensorflow (<< 1.12.2.0~) Filename: pool-buster/python-tensorflow-cuda/1.12.2/tensorflow-tools_1.12.2-1+Debian.buster.10+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6588 MD5sum: 3893bfedeafe4bd428676bf8e8636b32 SHA1: f016dbb6b76667912b5a3fd736dc5c940fa2207a SHA256: 1272e167278b62e52ef32dd52c9690ad922bfb0d84244830a54e0bddb23d9c56 SHA512: 0ad796da2446b3f3a12669b5e7764836090fade9eeffab59b26c48be404053512d4799e680158300e38c4b7071de20d5cfec520f0e8fcd4da81b4969047bb6ed Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.13.1-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.13.1-1) Maintainer: Adam Cecile Installed-Size: 19 Depends: python3.7:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.13.1-1), python3-tensorflow (<< 1.13.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.13.1/tensorflow-tools_1.13.1-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 7732 MD5sum: d450d82fcb75aac96773b0d880ab7969 SHA1: fbd68db694de9a1384cce85170061dd2031c7482 SHA256: d460bac5e8d9775b039b7205ced6f172a8e28398d3cbd30d91821834fbae66ec SHA512: 1f2e3943572980bf36e2ece634b84cf08b7cf62b7f26cd81e5378db795e19f3ceb1b81622788cec098d30ffcb25972a2244b769973198b48a52499cd06b3a678 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.13.1-2+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.13.1-2) Maintainer: Adam Cecile Installed-Size: 20 Depends: python3.7:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.13.1-2), python3-tensorflow (<< 1.13.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.13.1/tensorflow-tools_1.13.1-2+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 7904 MD5sum: bbc31eec851a42e9e603b1ed79150a23 SHA1: 6f66a3e81863f2db332e3da55ccb83a4765948a9 SHA256: 1265684ebefb899caeaa97dcae777980f4947383c990ec0be3aa9ccc9042e476 SHA512: 44f1a8c759da417f833cc3ab5e7ac4d107ae0c9e80f1b3406e41c5d886cc2f5167af35955822bf0edda70996ad2540260ae2cf11fd3fc39fb8d0511879a95937 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.13.2-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.13.2-1) Maintainer: Adam Cecile Installed-Size: 20 Depends: python3.7:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.13.2-1), python3-tensorflow (<< 1.13.2.0~) Filename: pool-buster/python-tensorflow-cuda/1.13.2/tensorflow-tools_1.13.2-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 7944 MD5sum: 62f41e99cc5159df3ab2a8f31b74fc44 SHA1: 426de5268843214e577e8051ec9bafd27d66c131 SHA256: 2d7bb7ea18d52a132d7a95bd19e2a735c6f446dcb905b23d1487bd7ee3ce1c2c SHA512: df7833bda257456058a6b6879f9f592296d2c39bbed3a0fa70d20926969518eedb6bc5cd44038ad807623ab4ec382bc01a3333a4de303d613f40023ecb43c272 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.14.0-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.14.0-1) Maintainer: Adam Cecile Installed-Size: 20 Depends: python3.7:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.14.0-1), python3-tensorflow (<< 1.14.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.14.0/tensorflow-tools_1.14.0-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 8264 MD5sum: c49e0e86466aaaea7a455d6072ac2c27 SHA1: dd811ba5abaf8d97d8db3ae3927860f640267caa SHA256: 79ba01ab1e0781894c08d78a9c242276b554e7747355487ec5e3defeea74c219 SHA512: 81a197efa139040a55e69c2e140a1eb6d139ca99425621b23fcf4e70d0bf9ffca83215c50282bff63dd250aed6cd34ed5cf40bb8df1f0e0c2d611a51e52601e3 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.15.4-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.15.4-1) Maintainer: Adam Cecile Installed-Size: 21 Depends: python3.7:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.15.4-1), python3-tensorflow (<< 1.15.4.0~) Filename: pool-buster/python-tensorflow-cuda/1.15.4/tensorflow-tools_1.15.4-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 8616 MD5sum: 31fcd4b2d8f71f327f0a73cf71a5a522 SHA1: 1c5f0443a447ab057cc20cf206377ec341a95a5e SHA256: 1ce62381dd113e926a1e5257879e09aa490c4c7f8209b4d7b0a30108d01eccfb SHA512: 13e537a52881af98aaefeb8988cd811ef7de4d8a698a063ccf625d80e0d625c1e7adcd1ea1b05758c521b9e445b561cb0f8e9988525203475d379c2dbba1fa3e Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.3.1-1+Debian-buster-testing Priority: optional Section: python Source: python-tensorflow-cuda (1.3.1-1) Maintainer: Adam Cecile Installed-Size: 11 Depends: python3-tensorflow (>= 1.3.1-1), python3-tensorflow (<< 1.3.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.3.1/tensorflow-tools_1.3.1-1+Debian-buster-testing_all.deb Size: 4064 MD5sum: 7925db650d2eb99192c88849030b573a SHA1: a01c7bab5191dcc07cdb68a9f671314ec4a8771e SHA256: 14f1b35f1fc74c5135af806124d295ce5121ec99c549038c61e51806b33c0159 SHA512: 327d6731e247efdaf9a3398d36cd884fffd41b6926589c2aaa97a309db844d5a3ff854c24c296d85c022c8a8fdc8deba208778a458d2cdc41348644e4b97bf90 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.4.0-1+Debian-buster-testing Priority: optional Section: python Source: python-tensorflow-cuda (1.4.0-1) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.4.0-1), python3-tensorflow (<< 1.4.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.4.0/tensorflow-tools_1.4.0-1+Debian-buster-testing_all.deb Size: 4700 MD5sum: fcd374556cf29ef3f00b16bb59bae75d SHA1: d2757e9ffb9248ba53a5a805302250d53ee7a167 SHA256: 2e27404cc8e4744a897455a1f4ffbfd78485052572e247d9849bab7a2e24dadf SHA512: 078c93a2229997aff9873e935511326e50c96024bc994e8cd6132671c4f2e6b92b172a60ac846e743551c09e8df57b75a2f9a5e838dbd30c7023273ccf26ae9f Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.4.1-1+Debian-buster-testing Priority: optional Section: python Source: python-tensorflow-cuda (1.4.1-1) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.4.1-1), python3-tensorflow (<< 1.4.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.4.1/tensorflow-tools_1.4.1-1+Debian-buster-testing_all.deb Size: 4852 MD5sum: b350330138d788675d1f66aaacec45ba SHA1: 2fb11cd59d88875a7926235bf971a8e5788a4ed3 SHA256: b75103ec9eeac2cbb92c499f9fade831a0120ec96298919b6d08deb68364bf5a SHA512: 3490b3add85edbecebf17fc70892db4bbe2b3021db972ec1861f40b5a5e1731b50d264c373b73089444cd15fa9afd7c6bfc7ddb87ca1a29b9259921cfa6938a2 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.4.1-2+Debian-buster-testing Priority: optional Section: python Source: python-tensorflow-cuda (1.4.1-2) Maintainer: Adam Cecile Installed-Size: 12 Depends: python3-tensorflow (>= 1.4.1-2), python3-tensorflow (<< 1.4.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.4.1/tensorflow-tools_1.4.1-2+Debian-buster-testing_all.deb Size: 5048 MD5sum: 3cb2f3e5bd1dad08484684cca67cfb9b SHA1: a118f5eb075ca72778a4f7e093b03d72ad2c9887 SHA256: 45f780c08d0051d95092a55f3c59a7393877725c05f21e00a91d22f48a0b3b5a SHA512: fc2e2ce9517d090d7ddff3a8b7c2028168281691edcea5f324403bfca08119d2e93b5c47008c45b0e84ed9cc8afd6e0c89f00c719e8c00816c43c96b6a851aac Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.5.0-1+Debian-buster-testing Priority: optional Section: python Source: python-tensorflow-cuda (1.5.0-1) Maintainer: Adam Cecile Installed-Size: 15 Depends: python3-tensorflow (>= 1.5.0-1), python3-tensorflow (<< 1.5.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.5.0/tensorflow-tools_1.5.0-1+Debian-buster-testing_all.deb Size: 4464 MD5sum: a24f98fbb63e0d0c826887146dfcea85 SHA1: b8745afeeb79ba2d2d39c9c359c3d04016971a44 SHA256: 775ccb8614908c6146d80da185e7bb2d6e02a3e45392dcbc56ce30d9f5965a92 SHA512: 5c964caf65519d44fd3265b7028f29fd733d80f4b6fc10c19e7651dbaf0ab52b9b5941cb18513c61757ebb18ef525dcc044b5edbd8463ca99d9b27ebd4adbfd9 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.5.1-1+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.5.1-1) Maintainer: Adam Cecile Installed-Size: 16 Depends: python3-tensorflow (>= 1.5.1-1), python3-tensorflow (<< 1.5.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.5.1/tensorflow-tools_1.5.1-1+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 5584 MD5sum: 59f7a248dc9e2cacb484cb3455f36119 SHA1: ed157abcc83ef8bc8a838d646d5648992f4b7b0e SHA256: 96241e55438e4fcc85fa9db2aefdb8af0121b9b87d6ec86efa9442879d40d479 SHA512: 0706aadbe00047072658e105f83ec10551a9e496f0fa373b20f85b62448ce4e5ff934a6a06d77acd81071205db2a5b47b1ba0cb49e83ad03fb9cdfaa4603d14c Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.6.0-4+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.6.0-4) Maintainer: Adam Cecile Installed-Size: 16 Depends: python3-tensorflow (>= 1.6.0-4), python3-tensorflow (<< 1.6.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.6.0/tensorflow-tools_1.6.0-4+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6092 MD5sum: e834e8c14ad22445ace1fe963f5a9b58 SHA1: 81e490dcce839b6041e0dfa9abe2c9466b3dea05 SHA256: a412460c8bdbfb27d4c5019638ef3cbf01c93473422e37c4a958275fc21bdc1d SHA512: cefd8b43259ab6e037f9314f8b4024e4ac42de13bb0db50e7ede8027523dc5f01671dcce62fc2369c39bf943e56e050e78f1c95deeb5a6ab08a09195f82a25b2 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.7.1-3+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.7.1-3) Maintainer: Adam Cecile Installed-Size: 16 Depends: python3.6:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.7.1-3), python3-tensorflow (<< 1.7.1.0~) Filename: pool-buster/python-tensorflow-cuda/1.7.1/tensorflow-tools_1.7.1-3+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6340 MD5sum: cd7bf06e11099be5878896f8a77b4a72 SHA1: 2886aa74dec05196bffbda3a988c4d1dda6d08e7 SHA256: 49ed4a462e112b6489d5191759777d98387a0f6869bf4a0f7e0d4ce9d0e26319 SHA512: 2f5da027f7c8fc909b8d3680e49986e8ca0ce683afde83b7b99701e97c528ef5e9c319f657e60faa3b8a7eaf17cfd626fb60cf2cbbf193c0e4f20154036d05bf Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.8.0-2+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.8.0-2) Maintainer: Adam Cecile Installed-Size: 16 Depends: python3.6:any, python3:any (>= 3.4~), python3-tensorflow (>= 1.8.0-2), python3-tensorflow (<< 1.8.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.8.0/tensorflow-tools_1.8.0-2+Debian.buster.testing+Cuda.9.1.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6376 MD5sum: 8c1bbe8e1e4aa926e9f258b9bc933864 SHA1: 49c39bd5cd5093e39cd6cc92b509a627c1c7c436 SHA256: 3529c371cd60719a1e5e441aa71e378fccf97e37b2a6550e896c3262446f227d SHA512: 606a74cb1116d9c18ae0bec2e1c3c6b4d250b18b231271000d9b09a70a6fbb3b745b7b3b76258653b77f12bdb21d470936f8166015ee8778da01bce26013d86f Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: tensorflow-tools Architecture: all Version: 1.9.0-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: python Source: python-tensorflow-cuda (1.9.0-1) Maintainer: Adam Cecile Installed-Size: 11 Depends: python3-tensorflow (>= 1.9.0-1), python3-tensorflow (<< 1.9.0.0~) Filename: pool-buster/python-tensorflow-cuda/1.9.0/tensorflow-tools_1.9.0-1+Debian.buster.testing+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 6072 MD5sum: c33614a1219f3ad5b6c09719b52e1c74 SHA1: 536ab0f140bf49173b021c5baff9530edf111a66 SHA256: 6fedea9a5fff1c27448a855b0f06e3186d1428cb9bc0d877a0f370b119000b95 SHA512: 21d00d16237b79f892ea0cb99f7361e257ad9392dbd84d0029de9240b52509957e1d0bd1e6fb65ac949f6f5cbff9a300c9e84a60b0e23ded755a175c5bf38ba2 Homepage: https://www.tensorflow.org/ Description: library for numerical computation using data flow graphs (cli tools) TensorFlow is an open source software library for numerical computation using data flow graphs. . Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. . The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. . This package contains "saved_model_cli" and "tensorboard" command line tools. Package: python-tensorflow-estimator Architecture: all Version: 1.13.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorflow-estimator (1.13.0-1) Maintainer: Adam Cecile Installed-Size: 1610 Depends: python-numpy (>= 1:1.16.0~rc1), python-numpy-abi9, python-absl (>= 0.1.6~), python-six (>= 1.10.0~), python:any (<< 2.8), python:any (>= 2.7~) Filename: pool-buster/python-tensorflow-estimator/1.13.0/python-tensorflow-estimator_1.13.0-1+Debian.buster.testing_all.deb Size: 176954 MD5sum: d5bf77a93e0fbc356b884f578c25f180 SHA1: 206d03c46b49b53a760fa970d5660cfd0c1b66e6 SHA256: ad34e12403efc997bfa3c74c8eff8d8decffda59bf967cbcbb8aa2e1d0d1c5a9 SHA512: 588d36419ae65415736575dce5d3cc1f4022cd8f4ddee49fa990099d81093662b74a30c2e3dcf14859ec5ac5d17767f56bbce22a4b248b90135e5010e2e9a2e2 Homepage: https://github.com/tensorflow/estimator Description: High-level TensorFlow API simplifying ML programming (Python 2) TensorFlow Estimator is a high-level TensorFlow API that greatly simplifies machine learning programming. . Estimators encapsulate training, evaluation, prediction, and exporting for your model. . This package installs the library for Python 2. Package: python3-tensorflow-estimator Architecture: all Version: 1.13.0-1+Debian.buster.testing Priority: optional Section: python Source: tensorflow-estimator (1.13.0-1) Maintainer: Adam Cecile Installed-Size: 1610 Depends: python3-numpy (>= 1:1.16.0~rc1), python3-numpy-abi9, python3-absl (>= 0.1.6~), python3-six (>= 1.10.0~), python3:any (>= 3.4~) Filename: pool-buster/python-tensorflow-estimator/1.13.0/python3-tensorflow-estimator_1.13.0-1+Debian.buster.testing_all.deb Size: 176894 MD5sum: b5b1e150e537328933ea08687227d25b SHA1: 74eda8760d9a993ae9a4c563a5d86f81803598da SHA256: 158a99a9001737fa977e6d6e9caf0da4163cd2cfe4bcaf7b0a48477dba52b714 SHA512: f76938139c8b3b9cec0929045b120f3c5992affeffbcfebba72c5c0afdc5021e9ecaf88ace6c9979fbc177990103f4cafbbdf28e2678163990aca2ced61e25c6 Homepage: https://github.com/tensorflow/estimator Description: High-level TensorFlow API simplifying ML programming (Python 3) TensorFlow Estimator is a high-level TensorFlow API that greatly simplifies machine learning programming. . Estimators encapsulate training, evaluation, prediction, and exporting for your model. . This package installs the library for Python 3. Package: python3-tensorflow-estimator Architecture: all Version: 1.14.0-1+Debian.10.buster Priority: optional Section: python Source: tensorflow-estimator (1.14.0-1) Maintainer: Adam Cecile Installed-Size: 2067 Depends: python3-numpy (>= 1:1.16.0~rc1), python3-numpy-abi9, python3-absl (>= 0.7.0~), python3-six (>= 1.10.0~), python3:any Filename: pool-buster/python-tensorflow-estimator/1.14.0/python3-tensorflow-estimator_1.14.0-1+Debian.10.buster_all.deb Size: 257482 MD5sum: 8317ecdab3311d51135c30ba5909fce5 SHA1: 6fa5402775fc652e27f8807842c30896d3ffdfe5 SHA256: 33d0e13d5d6c84454a178a07ae11ab78fcc77c21475d108c34b05ba746af6743 SHA512: 3632bae67eeb1e1e261770ff56f2e5654af292008654ad97f2f4c7cac8d3d0c3ec02cac33892dd90ce4362a2a9f3e4a0b1732e3cbc2e3fca59ae95e8272c7d2a Homepage: https://github.com/tensorflow/estimator Description: High-level TensorFlow API simplifying ML programming (Python 3) TensorFlow Estimator is a high-level TensorFlow API that greatly simplifies machine learning programming. . Estimators encapsulate training, evaluation, prediction, and exporting for your model. . This package installs the library for Python 3. Package: python3-tensorflow-estimator Architecture: all Version: 1.15.2-1+Debian.10.buster Priority: optional Section: python Source: tensorflow-estimator (1.15.2-1) Maintainer: Adam Cecile Installed-Size: 2126 Depends: python3-numpy (>= 1:1.16.0~rc1), python3-numpy-abi9, python3-absl (>= 0.7.0~), python3-six (>= 1.10.0~), python3:any Filename: pool-buster/python-tensorflow-estimator/1.15.2/python3-tensorflow-estimator_1.15.2-1+Debian.10.buster_all.deb Size: 266578 MD5sum: 950d04256cad721a28bbb0a349620888 SHA1: b6699d02aad94e0cdabd04c87cc0b307a684775f SHA256: 12c697bd6175bb8301449a314d9c96c1f6355cd1fd77f3e5cc50b4efdeb6f271 SHA512: 649ff8e5830d88dc46d4cb00b89568f52eca525f3a9dd19c426dac2bd930d64949c02cb543872f5a2bd856a1fb8eb2029d1c2d98c627574791110cba15a006e3 Homepage: https://github.com/tensorflow/estimator Description: High-level TensorFlow API simplifying ML programming (Python 3) TensorFlow Estimator is a high-level TensorFlow API that greatly simplifies machine learning programming. . Estimators encapsulate training, evaluation, prediction, and exporting for your model. . This package installs the library for Python 3. Package: equivs-tensorflow-estimator Architecture: all Version: 1.13.0~ Multi-Arch: foreign Priority: optional Section: misc Maintainer: Adam Cecile Installed-Size: 9 Provides: python-tensorflow-estimator (= 1.13.0~), python3-tensorflow-estimator (= 1.13.0~) Filename: pool-buster/python-tensorflow-estimator/equivs-cuda-tensorflow-estimator/equivs-tensorflow-estimator_1.13.0~_all.deb Size: 3200 MD5sum: 74d978357c3d8c1f1cab4243940fd3fa SHA1: a5eed76fcb4f4960cdba6f83fd6765d0e394cf62 SHA256: 09cb65dfa0afad456910fe9c77a06fecbd7fda874a55c90946813dd7e46b9a5a SHA512: 6daae0ca60166d6ea0f6e73fa776ac05d59f3f8b7868c6f0e886e6a4878334d28ee84f075a6163f2d4457e55a002b3d32138430469db1fb9a76a32adac46fdc5 Description: Fake package providing python(3)-tensorflow-estimator Because the real estimator package needs Tensorflow installed for building it but tensorflow installation actually depends on estimator package... Package: equivs-tensorflow-estimator Architecture: all Version: 1.14.0~ Multi-Arch: foreign Priority: optional Section: misc Maintainer: Adam Cecile Installed-Size: 9 Provides: python3-tensorflow-estimator (= 1.14.0~) Filename: pool-buster/python-tensorflow-estimator/equivs-cuda-tensorflow-estimator/equivs-tensorflow-estimator_1.14.0~_all.deb Size: 3264 MD5sum: 95d6781e8e4ac1af3897449e2b4022f7 SHA1: 0c61963f4f494d2c79d1a5d98f624ee81256f855 SHA256: 083ab11d3649e2d6a6ba7cb96fd0fcf6ce561e21f47ef0f365e59be7ffe744b5 SHA512: ed4ddf26d7fb109513d62facecef55c2929c1a2a6f1f5d0c72ee4aa55e3ef2573a8ae185ba374a9533cf4047c44d47d255b3c21bfee0b97314bb45ad33e3d4d2 Description: Fake package providing python3-tensorflow-estimator Because the real estimator package needs Tensorflow installed for building it but tensorflow installation actually depends on estimator package... 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Package: equivs-tensorflow-estimator Architecture: all Version: 2.0.0~ Multi-Arch: foreign Priority: optional Section: misc Maintainer: Adam Cecile Installed-Size: 9 Provides: python3-tensorflow-estimator (= 2.0.0~) Filename: pool-buster/python-tensorflow-estimator/equivs-cuda-tensorflow-estimator/equivs-tensorflow-estimator_2.0.0~_all.deb Size: 3264 MD5sum: 6371f406bf37a14c1082295d878b4829 SHA1: 5906eb1829a0e5bf81e7a62d23705af3eea917cb SHA256: 33e3a4c3435d81faafb4b90c8743ee1d2bc332db529dcac1b514e955cab735d4 SHA512: 0e85ad3b43d03df630d61e545453c77b711ab4795f559b8368d52878d30b799674967f43fcd277ab3974d1d6f3e697c48df58a59265c7e13f48351cc5e6ef502 Description: Fake package providing python3-tensorflow-estimator Because the real estimator package needs Tensorflow installed for building it but tensorflow installation actually depends on estimator package... Package: python3-timezonefinder Architecture: all Version: 4.0.2-0 Priority: optional Section: python Source: python-timezonefinder Maintainer: Adam Cecile Installed-Size: 46075 Depends: python3-importlib-resources, python3-numpy, python3:any (>= 3.4~) Recommends: python3-numba Filename: pool-buster/python-timezonefinder/python3-timezonefinder_4.0.2-0_all.deb Size: 21159928 MD5sum: 974bf9bfcb356ec222fdebc3bb3daae3 SHA1: 0b6e244dc65ec5c4af63ca040d6225b197330ee5 SHA256: a9cf0ba25b8f024a52c1a2778ed180439d76770ffe6a59b6a6cacde8329e686b SHA512: 57b983f87cdbaf85d86dbf42b0a710c9dd03fc5ec36f5ab4910484d6eadb37478cc145c08d3414b64cf489230f28df88f5e8b460f67f3a80ca19f58322f3f82f Homepage: https://github.com/MrMinimal64/timezonefinder Description: Finding the timezone of any point on earth offline (Python 3) This is a fast and lightweight python project for looking up the corresponding timezone for a given lat/lng on earth entirely offline. . This package installs the library for Python 3. 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Package: python-wrapt-doc Architecture: all Version: 1.12.1-4~bpo+Debian.10.buster Priority: optional Section: doc Source: python-wrapt (1.12.1-4~bpo) Maintainer: Debian OpenStack Installed-Size: 544 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common (>= 0.4.3+dfsg) Filename: pool-buster/python-wrapt/python-wrapt-doc_1.12.1-4~bpo+Debian.10.buster_all.deb Size: 68326 MD5sum: e4828290dc5e80741fa8d5401a78a7ae SHA1: e56d4bcf0ed2360df412acfe9469d754b304c871 SHA256: e8539b42b379cdefbdebe8880ba97ee92152fce11881ac830442b6d8b5d919be SHA512: ebbc33785059181cb0ce9ad9ae6ca31822f5ef1ba862ca357a76b19580516215faa0bb3d5ec6d99c7988aee0ce98beb6afb2855c53c5f2d0446cab0e56f47159 Homepage: https://github.com/GrahamDumpleton/wrapt Description: decorators, wrappers and monkey patching. - doc The aim of the wrapt module is to provide a transparent object proxy for Python, which can be used as the basis for the construction of function wrappers and decorator functions. . The wrapt module focuses very much on correctness. It therefore goes way beyond existing mechanisms such as functools.wraps() to ensure that decorators preserve introspectability, signatures, type checking abilities etc. The decorators that can be constructed using this module will work in far more scenarios than typical decorators and provide more predictable and consistent behaviour. . To ensure that the overhead is as minimal as possible, a C extension module is used for performance critical components. An automatic fallback to a pure Python implementation is also provided where a target system does not have a compiler to allow the C extension to be compiled. . This package contains the documentation. Package: python-pytorch-sphinx-theme Architecture: all Version: 0.0.24.git20191017.master.9e76b28-1+Debian.10.buster Priority: optional Section: python Source: pytorch-sphinx-theme (0.0.24.git20191017.master.9e76b28-1) Maintainer: Adam Cecile Installed-Size: 431 Depends: python-sphinx, python:any (<< 2.8), python:any (>= 2.7~) Filename: pool-buster/pytorch-sphinx-theme/python-pytorch-sphinx-theme_0.0.24.git20191017.master.9e76b28-1+Debian.10.buster_all.deb Size: 72718 MD5sum: 2aa320dd833f55e5559a7b02e243e553 SHA1: 2ee3e486f06c8129c560b3a23618e278949238e2 SHA256: 52212d57800693b6c96ca15b9a0d36fc97d1ad10e537a7448d92ad2b71aec2cb SHA512: 7c590e100bbd13a44e803f90a9883611dee85e55379191c38e14351fb232738ad25a9a6625309f4058f9ada97d2bf5d3d00a6356210b0277f66a41923d959de9 Homepage: https://github.com/pytorch/pytorch_sphinx_theme Description: Sphinx theme for PyTorch Docs and PyTorch Tutorials (Python 2) Sphinx theme for PyTorch Docs and PyTorch Tutorials based on the Read the Docs Sphinx Theme. . 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Package: python-torch-doc Architecture: all Version: 1.3.1+debian-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70 Priority: optional Section: doc Source: pytorch (1.3.1+debian-1) Maintainer: Adam Cecile Installed-Size: 19132 Filename: pool-buster/pytorch/pytorch/python-torch-doc_1.3.1+debian-1+Debian.10.buster+Cuda.9.2.Levels.30.35.37.50.52.53.60.61.70_all.deb Size: 2252820 MD5sum: b69e6759049c706ab1e925889f8ac21f SHA1: a5878840cf99a5c03c35712d3a923c0226e5b895 SHA256: 9e0236e3e0bbae60dae5e45b53c08bff59bd8f91a8cf411299be0f8c20c2eca4 SHA512: 274dafbcd1087db4ed452121b53514bbf1cf98692511c86b0127b2d18e8b78e7eb3bb36ba90b3c1bcf7f6ccde1cf33fec1df203f9fb696940e2006c970fc325f Homepage: http://pytorch.org/ Description: Facebook's Tensors and Dynamic neural networks (common documentation) 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 . 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This package installs the library for Python 2. Package: python3-torchvision Architecture: all Version: 0.1.9-1+Debian.buster.testing Priority: optional Section: python Source: torchvision (0.1.9-1) Maintainer: Adam Cecile Installed-Size: 151 Depends: python3:any (>= 3.4~), python3-numpy, python3-pil, python3-six Recommends: python3-torch Filename: pool-buster/pytorch/torchvision/python3-torchvision_0.1.9-1+Debian.buster.testing_all.deb Size: 31436 MD5sum: 7315d295775ef3a15e4aaa43f8b69ba4 SHA1: d9266d275e6f0f6a650cadc0c2a2e5609dc30395 SHA256: 012f163456dbfcf6c04c6ae8010de3bb5003bbd3c45856c4691869e2236610cd SHA512: 6f4f190812452fb923912393a0b8ea0b9fcbec360a7ff48783f28ad14b02c6511ce7f0a81657cc0a3b01795e29c28bf595fa92c7c5e13039194a1f55642d2c5c Homepage: https://github.com/pytorch/vision/ Description: Image and video datasets and models for torch deep learning (Python 3) The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. . This package installs the library for Python 3. Package: shark-doc Architecture: all Version: 3.1.4+ds1-1.1+Debian.buster.10 Built-Using: sphinx (= 1.8.4-1) Multi-Arch: foreign Priority: optional Section: doc Source: shark (3.1.4+ds1-1.1) Maintainer: Debian Science Maintainers Installed-Size: 102105 Depends: libjs-sphinxdoc (>= 1.0), libjs-mathjax Filename: pool-buster/shark/shark-doc_3.1.4+ds1-1.1+Debian.buster.10_all.deb Size: 21429646 MD5sum: c9e3a027e85f0995f533825f2c76c5e9 SHA1: 543b13e8418bf862476c2079cdc18bf9a65fc9b9 SHA256: 8199194874ae2ad136734c428d01f89ac432cc52c9acebf5d8b9ee2681567be8 SHA512: bdd56b4f97d807815f627bb20b1403ff458f837c7c991c802f6a894d2c8ca6762f5e609ec558e57e5ae1abc8271641c25fbcb3b0591643df4daecb43b0ea6048 Homepage: http://image.diku.dk/shark Description: documentation for Shark Shark is a modular C++ library for the design and optimization of adaptive systems. It provides methods for linear and nonlinear optimization, in particular evolutionary and gradient-based algorithms, kernel-based learning algorithms and neural networks, and various other machine learning techniques. . This package provides the documentation. Package: python-sortedcollections-doc Architecture: all Version: 1.0.1-1~bpo+Debian.buster.10 Priority: optional Section: doc Source: sortedcollections (1.0.1-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 296 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-buster/sortedcollections/python-sortedcollections-doc_1.0.1-1~bpo+Debian.buster.10_all.deb Size: 124934 MD5sum: 542cfa141a4aa2cf3d17edf8b3284bc2 SHA1: 7904e5a6c5cc1377cd63a414452751b5d799bd0a SHA256: e787001bf01ef317137be6786139fd1e58e7fee9709f5ad8b920ebdf937539f8 SHA512: ec94fedefec76a46c91cd669dcd8c87123b140628778229927fd1433ad6416e6fc01b4128994564a8687c07d783e3c85769a7de1b129413bb0870641e1a65af8 Homepage: http://www.grantjenks.com/docs/sortedcollections/ Description: Python documentation for Sorted Collections SortedCollections is an Apache2 licensed Python sorted collections library. . Features -------- . - Pure-Python - Depends on the SortedContainers module. - ValueSortedDict - Dictionary with (key, value) item pairs sorted by value. - ItemSortedDict - Dictionary with key-function support for item pairs. - OrderedDict - Ordered dictionary with numeric indexing support. - OrderedSet - Ordered set with numeric indexing support. - IndexableDict - Dictionary with numeric indexing support. - IndexableSet - Set with numeric indexing support. . This contains the documentation. Package: python3-sortedcollections Architecture: all Version: 1.0.1-1~bpo+Debian.buster.10 Priority: optional Section: python Source: sortedcollections (1.0.1-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 43 Depends: python3-sortedcontainers, python3:any Filename: pool-buster/sortedcollections/python3-sortedcollections_1.0.1-1~bpo+Debian.buster.10_all.deb Size: 9576 MD5sum: a61e699cb92f25a2b62752a2175dae19 SHA1: b5a01c3bb3f1caa03f7c711111dfbbe87920b3ea SHA256: 4457555c4f4815b4a88da412af3442534c870b838bba5652ac069d722778edb0 SHA512: 9cca103708c5850705c44429c78ffcd472d92302858957f2f0a55b221f001a3b6c500761df15a6b0c77db9250a889910385e90663ca660c35322fa36d41a67df Homepage: http://www.grantjenks.com/docs/sortedcollections/ Description: Python 3 Sorted Collections SortedCollections is an Apache2 licensed Python sorted collections library. . Features -------- . - Pure-Python - Depends on the SortedContainers module. - ValueSortedDict - Dictionary with (key, value) item pairs sorted by value. - ItemSortedDict - Dictionary with key-function support for item pairs. - OrderedDict - Ordered dictionary with numeric indexing support. - OrderedSet - Ordered set with numeric indexing support. - IndexableDict - Dictionary with numeric indexing support. - IndexableSet - Set with numeric indexing support. . This contains the Python 3 module Package: python-sortedcontainers-doc Architecture: all Version: 2.1.0-1~bpo+Debian.buster.10 Built-Using: alabaster (= 0.7.8-1), sphinx (= 1.8.4-1) Multi-Arch: foreign Priority: optional Section: doc Source: sortedcontainers (2.1.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 20520 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-buster/sortedcontainers/python-sortedcontainers-doc_2.1.0-1~bpo+Debian.buster.10_all.deb Size: 15952140 MD5sum: ba3b03f8901182a20a0b4c2e87d7b531 SHA1: e547bb662942e6fd03b1cc3362758a4f0d959e9b SHA256: 9ef14f55f2b839ed79f97f776052bc69c6f45250f97029f3f06e13abd7ad3297 SHA512: bfbbb776865abac3ffbb90def6a6fa0334390028908371ed6a5d2bec0aaf2676696aea43025aeab6d37d03635ce8d8bd2c9db88973b6114ff0a3660ab39cb704 Homepage: http://www.grantjenks.com/docs/sortedcontainers/ Description: sorted container types: SortedList, SortedDict, and SortedSet (documentation) Python’s standard library is great until you need a sorted container type. Many will attest that you can get really far without one, but the moment you really need a sorted list, dict, or set, you’re faced with a dozen different implementations, most using C-extensions without great documentation and benchmarking. . This package contains the documentation for sortedcontainers Python module. Package: python-sortedcontainers Architecture: all Version: 2.1.0-1~bpo+Debian.buster.10 Multi-Arch: foreign Priority: optional Section: python Source: sortedcontainers (2.1.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 158 Provides: python2.7-sortedcontainers Depends: python:any (<< 2.8), python:any (>= 2.7~) Suggests: python-sortedcontainers-doc Filename: pool-buster/sortedcontainers/python-sortedcontainers_2.1.0-1~bpo+Debian.buster.10_all.deb Size: 32422 MD5sum: 622dd865dd0f09d9d4c3047865932071 SHA1: 39531a1da44441c10dd05cd8b7bb32a39207afd1 SHA256: 2620e73f3c419fd94bf8c54a574a7bfcb759025d836bc8f9f2ebe960fbf8d718 SHA512: 9466a898580a4203c341dc904398456054df3ad9b28610607a43b03778105b7d9b570e5143e78900f808c786e9cc42f52c01b08a4caa5ad6b6011d4415192de5 Homepage: http://www.grantjenks.com/docs/sortedcontainers/ Description: sorted container types: SortedList, SortedDict, and SortedSet (Python 2) Python’s standard library is great until you need a sorted container type. Many will attest that you can get really far without one, but the moment you really need a sorted list, dict, or set, you’re faced with a dozen different implementations, most using C-extensions without great documentation and benchmarking. . This package contains the Python 2 version of sortedcontainers. Package: python3-sortedcontainers Architecture: all Version: 2.1.0-1~bpo+Debian.buster.10 Multi-Arch: foreign Priority: optional Section: python Source: sortedcontainers (2.1.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 158 Depends: python3:any Suggests: python-sortedcontainers-doc Filename: pool-buster/sortedcontainers/python3-sortedcontainers_2.1.0-1~bpo+Debian.buster.10_all.deb Size: 32506 MD5sum: 8b40032f1114f793628c1e56d6f89a2a SHA1: dd1c420f766120469291bb3411dd8d30ab200e8f SHA256: 492145493839d472b878c7545da97c81ceebf27bc03ebb4b6c3f2d5eddfd417e SHA512: 728576fde32630154594762f2cc7ddacd733aef89b1d018df04658a71fd5bd0610080ac2d01bf1bd6b548b4af758c2f7e4b83d349cc26e2a654bd31fb491b6d7 Homepage: http://www.grantjenks.com/docs/sortedcontainers/ Description: sorted container types: SortedList, SortedDict, and SortedSet (Python 3) Python’s standard library is great until you need a sorted container type. Many will attest that you can get really far without one, but the moment you really need a sorted list, dict, or set, you’re faced with a dozen different implementations, most using C-extensions without great documentation and benchmarking. . This package contains the Python 3 version of sortedcontainers. Package: python-statsmodels-doc Architecture: all Version: 0.9.0-0+Debian.buster.testing Priority: optional Section: doc Source: statsmodels (0.9.0-0) Maintainer: Debian Science Maintainers Installed-Size: 77553 Depends: libjs-requirejs, libjs-sphinxdoc (>= 1.0) Recommends: libjs-mathjax Suggests: python-statsmodels, python3-doc, python-numpy-doc, python-patsy-doc, python-pandas-doc, python-scipy-doc Breaks: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Filename: pool-buster/statsmodel/statsmodel/python-statsmodels-doc_0.9.0-0+Debian.buster.testing_all.deb Size: 11568234 MD5sum: a21921de0102c3995fd4dfaf4d19aedd SHA1: 5f143e4e1261b93085fa9c2c2dabe1296ee48e32 SHA256: 66a6e20c4e25aee36a206acf9f1368dcfa4871b2bd3041116a02455ef711414a SHA512: 49fdbab348e234e5e4352dc47428a345a9e969450a96e170ca01d6f78e959fb4be27cc0ede9385105617e6b59f7cbe0ce6daaa1675a1e98517f0997ce732a27d Homepage: http://statsmodels.sourceforge.net/ Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels Architecture: all Version: 0.9.0-0+Debian.buster.testing Priority: optional Section: python Source: statsmodels (0.9.0-0) Maintainer: Debian Science Maintainers Installed-Size: 21385 Provides: python2.7-statsmodels Depends: python-numpy, python:any (<< 2.8), python:any (>= 2.7~), python-scipy, python-statsmodels-lib (>= 0.9.0-0), python-patsy, python-pandas Recommends: python-matplotlib, python-joblib, python-cvxopt Suggests: python-statsmodels-doc Breaks: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Filename: pool-buster/statsmodel/statsmodel/python-statsmodels_0.9.0-0+Debian.buster.testing_all.deb Size: 6531880 MD5sum: 9f9a608a4ad345a1b13c0d15f156cc05 SHA1: 71328f4bbf7143307b8600ffbc5cec2916c0bd2c SHA256: f3855935742f06d911fbcb7a222654987bbc60bf74896b7285069daa7e3fb0f7 SHA512: a72234a7c6b39c2eaca9652db1e313790aef7b4e5ed58895fc0629d17e86caa65193c8bf04f42989a63e25d12c789b980cd248e5f3b0464d9975c24c968ac27f Homepage: http://statsmodels.sourceforge.net/ Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python3-statsmodels Architecture: all Version: 0.9.0-0+Debian.buster.testing Priority: optional Section: python Source: statsmodels (0.9.0-0) Maintainer: Debian Science Maintainers Installed-Size: 18196 Depends: python3-numpy, python3:any, python3-scipy, python3-statsmodels-lib (>= 0.9.0-0), python3-patsy, python3-pandas Recommends: python3-matplotlib, python3-joblib, python3-cvxopt Suggests: python-statsmodels-doc Filename: pool-buster/statsmodel/statsmodel/python3-statsmodels_0.9.0-0+Debian.buster.testing_all.deb Size: 3430576 MD5sum: 0a7e0d356438438c9e6df1cc2d1d0474 SHA1: a7554e6368b049efaa4e278e3cb7e0bdce583767 SHA256: 2729cf0fdf28d0e78700ca276d035c9fea6e97bd5bcab7da2364ef71ec8d8792 SHA512: 4a2d8f94449bbdd7065974055586a85570f1b7acab51c26399fca7cdf0763c9130bf9201ccff6efdfe92d66d4c0b91fc1223b368879e4b9e349c65152b72ed0b Homepage: http://statsmodels.sourceforge.net/ Description: Python3 module for the estimation of statistical models statsmodels Python3 module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python3-torchsummary Architecture: all Version: 1.2.0+3.5-0+Debian.10.buster Priority: optional Section: python Source: torch-summary (1.2.0+3.5-0) Maintainer: Adam Cecile Installed-Size: 59 Depends: python3:any Filename: pool-buster/torch-summary/python3-torchsummary_1.2.0+3.5-0+Debian.10.buster_all.deb Size: 15606 MD5sum: 8c787b0a69afea9178c02322cc7d829d SHA1: 83a14b57bcdcc38482914d3dc1d77046fa346eea SHA256: 72a7db021c07b0af9f4c5fcf390770821e6dc6639f1f1f9e46b48a4d07eb6fa7 SHA512: bb46b881bf762ecfaa58cb76597f0ac05c5e5d74bf243a61bf613c2931c87b05f6e8764d1366ff2d101bdacda2760b988daa209667196bba07ac7dccb531b5e6 Homepage: https://github.com/TylerYep/torch-summary/ Description: View model summaries in PyTorch (Python 3) Torch-summary provides information complementary to what is provided by print(your_model) in PyTorch, similar to Tensorflow's model.summary() API to view the visualization of the model, which is helpful while debugging your network. . In this project, we implement a similar functionality in PyTorch and create a clean, simple interface to use in your projects. . This package installs the library for Python 3.