Package: python-absl Architecture: all Version: 0.1.11-1+Debian.stretch.9.4 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-stretch/absl-py/python-absl_0.1.11-1+Debian.stretch.9.4_all.deb Size: 70188 MD5sum: 8076b6d4edfcc21abb2067c73632682d SHA1: 3b7479d406bdcf24b6fb7c61f8d6f4dd5ce73cdf SHA256: 1abd17f3dc19e0adc9f40612db4de158b79c7325a9410c8b60edcb440c7c8bc1 SHA512: d943ddb7979e75e555589f8997f3b5788bedf28bf584d9d0e103fd05964e5cc76b0f186f51098a5887958e6a3db9fc262bd3ef182f99af9af7fa838205b00442 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. Package: python-absl Architecture: all Version: 0.7.1-1+Debian.stretch.9.8 Priority: optional Section: python Source: absl-py (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 408 Depends: python-enum34, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/absl-py/python-absl_0.7.1-1+Debian.stretch.9.8_all.deb Size: 84942 MD5sum: b1bc4866da281fd5685a30330161b029 SHA1: d0474cb923982954e0209b762d6fc4e411e97f6b SHA256: dc25e2bd8773ea1bf6d17e5f62de3131cbd046efd8d4272677eb308813729a95 SHA512: 652b7858e8fccc3e6b86207947789a139d9d29404c945ea064c5d38905185617625041fc880b91d634493aab96a18b8333c37a921a655ca338e4ce057d0a4cf9 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. Package: python3-absl Architecture: all Version: 0.1.11-1+Debian.stretch.9.4 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-stretch/absl-py/python3-absl_0.1.11-1+Debian.stretch.9.4_all.deb Size: 70290 MD5sum: ed709d987f90a0fe4fc1e7db31c9940b SHA1: 81d48285ad5ae6ec96ad3ef585f7ecb688d093dc SHA256: af528216f005f841b1c1e05b568e88c0876d125ea45289a21d87eb4534d3c7a7 SHA512: 3f82b631209efe6c84c09a977dbd7dd2dbdf8e8aec75f286b05597e37d5f8a4cbfa8ed0b9a9a35230a499b8a1fd9acc32d1716252103afe52c9be786a3ef0040 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.stretch.9.8 Priority: optional Section: python Source: absl-py (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 401 Depends: python3-six, python3:any (>= 3.4~) Filename: pool-stretch/absl-py/python3-absl_0.7.1-1+Debian.stretch.9.8_all.deb Size: 84030 MD5sum: ca36f286237f33c53779575977225216 SHA1: d64a46070e68b989874b7232b0fed30a2759564d SHA256: a3658430deeaac010f40049738494667733a0e897bb1d9dd5efe4d8501275de5 SHA512: be4082b65a8352196e9a6004a725498207d44f334a5d7255872690e53637e43ba0ce3dbda088e5ef17eb51723eb428876cdb29bb86c27c912c6b138cc066923c 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.stretch.9.9 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 (>= 2.7.5-5~) Filename: pool-stretch/cloudpickle/python-cloudpickle_0.8.0-1~bpo+Debian.stretch.9.9_all.deb Size: 17476 MD5sum: 4522c62d3b85325c3bdc0e018d672d03 SHA1: b06afe96fada67eaf9b1b523c291f6fa43bfabd2 SHA256: dcd59a38d1bed530aa30ead036bac0addc29f9438b33769cd7a851c82c3a1c3c SHA512: 4a80ca3248de396e01bb90292e946c3029d855b8f83757ddc28a40b187e9babd0d5396cd9365835f00ac327b54328f419917a09b2cc82f9328f13b802b89e9e3 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. . 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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: cython-doc Architecture: all Version: 0.29.2-2~bpo9+0 Multi-Arch: foreign Priority: optional Section: doc Source: cython Maintainer: Python Applications Packaging Team Installed-Size: 2745 Depends: libjs-jquery, libjs-underscore Suggests: cython Filename: pool-stretch/cython/cython-doc_0.29.2-2~bpo9+0_all.deb Size: 622258 MD5sum: 7a171755e09fc6dc3ca48cb342d189d6 SHA1: ad5031c7f92fefc1a8fe7f9df14bc7323b1dd558 SHA256: 55bcf08ce9b84fe14f96b82d1c47386cf81b732d7ad4c7a9d63b451f7dbd0b7b SHA512: 009043a8ebff93069fc9a80cf83d3ddd5440ac6a512252bf14229faf713a5b39a65563c08d569ba34bd68138f3ea84b6821df9e85c86841b7061742f315865a7 Homepage: http://cython.org/ Description: C-Extensions for Python - documentation This package contains documentation for Cython. 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Package: python-distributed-doc Architecture: all Version: 1.18.0+ds.1-1~bpo9+0 Multi-Arch: foreign Priority: optional Section: doc Source: dask.distributed Maintainer: Debian Python Modules Team Installed-Size: 3425 Filename: pool-stretch/dask.distributed/python-distributed-doc_1.18.0+ds.1-1~bpo9+0_all.deb Size: 269044 MD5sum: 4e26c8d21fe1f818e83c76288eec03c9 SHA1: 6fb43082671b90eccf6faa37336cb6f9bd01060f SHA256: c8c97e2f2649eb39c9fcf14f309fb71083dbe367736fcbe1454b4df349bbb6fc SHA512: b748d7a887de6605e10574344a1d499f5043982f3b7bdd6221fc12fe2583632f5a0a0812927e0576573138e619d9c9c321e75090cb9c35914b560726ed20e957 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. . 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It extends both the concurrent.futures and dask APIs to moderate sized clusters. . This contains the Python 3 version Package: python3-dask Architecture: all Version: 0.15.1-3~bpo9+0 Priority: optional Section: python Source: dask Maintainer: Debian Python Modules Team Installed-Size: 2017 Depends: python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-toolz Suggests: python3-cloudpickle, python3-distributed (>= 1.16), python3-graphviz, python3-pandas (>= 0.19.0), python3-partd, python3-psutil Filename: pool-stretch/dask/python3-dask_0.15.1-3~bpo9+0_all.deb Size: 359140 MD5sum: 8a55cd352ce4547a1f5e051fc30e02d4 SHA1: 6375edcedb31f191e4f8b39f0ca38cded5d30e16 SHA256: 2f77a17efbdbed80464849290c922066603be20dddb03d61c7b6ddea4bda22b7 SHA512: 6d65301db441e5a4f939d47cfd61b9bc3b1bab4480697d1e396bb8b6ca298a64e158e3dca9fc92c0ff9a07bd0dafb242f06c826d8deac647efb01d08fbf6e8aa Homepage: http://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: equivs-cuda-cublas-8-0 Architecture: all Version: 8.0 Multi-Arch: foreign Priority: optional Section: misc Maintainer: Adam Cecile Installed-Size: 9 Provides: cuda-cublas-8-0 Depends: libcublas8.0, libcudart8.0 Filename: pool-stretch/equivs-cuda-cublas/equivs-cuda-cublas-8-0_8.0_all.deb Size: 3198 MD5sum: 0dc80feb85119ee25b24f70fa2a61895 SHA1: 431161e6d71a17897829cac77cea0898c44e45ae SHA256: 0737343a45182d3615f1d4b9480dd23b28c6a68d3ce22f23975e1204cce6f96d SHA512: 8d70b191d5ab5163161718fbf856513bf02c088b44850903c6069af1696de7e4f3e1989ff877cc8872b506037e03f49d5c22eb5be181481a557bd5b9786f3f72 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. 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Package: gcj-4.9-jre-lib Architecture: all Version: 4.9.2-10 Priority: optional Section: java Source: gcc-4.9 Maintainer: Debian GCC Maintainers Installed-Size: 11193 Depends: gcc-4.9-base (>= 4.9), libgcj15 (>= 4.9) Filename: pool-stretch/gcc_4.9_from_jessie/gcj-4.9-jre-lib_4.9.2-10_all.deb Size: 10349962 MD5sum: fbfeb3d171c9c17fb53e16f706a2c32f SHA1: 05d014a9c69236f28ab8b466dedda164832c5a19 SHA256: 1fb114b97f2691cd659f04f7e820a7dfc077b2850c79135849794c67a1beca50 SHA512: e57d1461ff4b81cadcf15dcbe443b357ce8bc5881d7bf9b58163789dffa0d5fdb954923c6ec2c3c7c0623eb3e8c91312dc356907b48bedc6fdabcc75a7934f70 Homepage: http://gcc.gnu.org/ Description: Java runtime library for use with gcj (jar files) This is the jar file that goes along with the gcj front end to gcc. Package: gcj-4.9-source Architecture: all Version: 4.9.2-10 Priority: optional Section: java Source: gcc-4.9 Maintainer: Debian GCC Maintainers Installed-Size: 12667 Depends: gcc-4.9-base (>= 4.9), gcj-4.9-jdk (>= 4.9) Filename: pool-stretch/gcc_4.9_from_jessie/gcj-4.9-source_4.9.2-10_all.deb Size: 11662602 MD5sum: 0abe255c7a1fb63563b17feddf6fc9b8 SHA1: dea3e1b40fa096e956bb3fdabfaf7e48ab44f9a1 SHA256: 95e113181985663fd0fd216ad940f9028e7c3089e87c5d1d69099c7c252b2bd6 SHA512: 8cf3a50062267a5e7341e848afb6019ea0fdcc16a2167e2c70b0fcb6d34d8e32593172e8068dd8e5507a71502e98a6a95fb29999ea90eb43ee3196b739d6127c Homepage: http://gcc.gnu.org/ Description: GCJ java sources for use in IDEs like eclipse and netbeans These are the java source files packaged as a zip file for use in development environments like eclipse and netbeans. Package: libgcj-doc Architecture: all Version: 4.9.2-10 Priority: optional Section: doc Source: gcc-4.9 Maintainer: Debian GCC Maintainers Installed-Size: 429793 Provides: classpath-doc Depends: gcc-4.9-base (>= 4.9) Enhances: libgcj15-dev Filename: pool-stretch/gcc_4.9_from_jessie/libgcj-doc_4.9.2-10_all.deb Size: 18866976 MD5sum: bf80dfcad5dfec4266e58ac2def2a639 SHA1: 2084d75730d9e0900d88993f0a3f1521e20340f2 SHA256: 47264bcfebdab93479668a798b4a66f322c098de05b0b5482ec6f8419566afdd SHA512: 17aa2534f7891b4949eba6a93c05c17b99821ee0066dbad6e175b9fb87931f79dabfe9c4ce3c55802051cd5ab6d056eb8d7728fb0307ff10721a5b5403d3cfd8 Homepage: http://gcc.gnu.org/ Description: libgcj API documentation and example programs Autogenerated documentation describing the API of the libgcj library. Sources and precompiled example programs from the Classpath library. 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One set is the distribution documentation, the other set is the source documentation including a namespace list, class hierarchy, alphabetical list, compound list, file list, namespace members, compound members and file members. Package: python-hdbscan-doc Architecture: all Version: 0.8.13-1+Debian.stretch.9.4 Priority: optional Section: doc Source: hdbscan (0.8.13-1) Maintainer: Adam Cecile Installed-Size: 6690 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/hdbscan/python-hdbscan-doc_0.8.13-1+Debian.stretch.9.4_all.deb Size: 5341820 MD5sum: b28c742c79d4c86bc99d56a57a5bacdb SHA1: 4420e2a50014c7b5b82a6c2826e216bce48447f8 SHA256: ee89102704abfcbf06b63457dc2cca7ddc4fde97e19d5997c47ba448fabf5770 SHA512: 66c8a48fca4255ef65faa0df35db4b56afadc67c7b4291d2d4402212af17e277201a57008eb9f6b43bc4fcfd877c8c60ae1e743f44e494c135ddea4d6bd206dd 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: jupyter-nbformat Architecture: all Version: 4.4.0-1~bpo9+1 Priority: optional Section: utils Source: nbformat Maintainer: Debian Python Modules Team Installed-Size: 16 Depends: python3:any (>= 3.3~), python3-nbformat (= 4.4.0-1~bpo9+1), jupyter-core Filename: pool-stretch/jupyter-notebook/backports/nbformat/jupyter-nbformat_4.4.0-1~bpo9+1_all.deb Size: 6348 MD5sum: 6dba6117b3d780debb49ea832e97a3ff SHA1: 8478e68f8d221716b8dd3820b824d87d13b8be82 SHA256: c03806203bc4cd563803800f63df1b3355576cc18a5bc0eb04f32bb49495ed0b SHA512: 6579432d9a215a4a5eb0fda2b555f49a4119abe3ab5b21fe5e3e10f6a5e8b8da0b95d7deccc9d5e96bd8439bfbba7b2198e0f8d48a7a550eed81096b870b6efc Homepage: https://github.com/jupyter/nbformat Description: Jupyter notebook format (tools) This software component contains the reference implementation of the Jupyter notebook format, and Python APIs to work with notebooks. . This package installs the notebook signing tool. Package: python-nbformat-doc Architecture: all Version: 4.4.0-1~bpo9+1 Built-Using: alabaster (= 0.7.8-1), sphinx (= 1.4.9-2) Multi-Arch: foreign Priority: optional Section: doc Source: nbformat Maintainer: Debian Python Modules Team Installed-Size: 180 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-stretch/jupyter-notebook/backports/nbformat/python-nbformat-doc_4.4.0-1~bpo9+1_all.deb Size: 37488 MD5sum: 793e548837decb1a7c5df860da168168 SHA1: 5a5db99b3a6369409008fac54befc0c7e98c1fbd SHA256: 76a97f0d26c652386d3198ff9825b8a49b0cd77340f3c23eeb4a5687fed3505a SHA512: f2093bddab355eddbb1d069b7b11e157e276ffb23774f31e24af3a6f2bfba6e7896f8a2ef10d0669dfc2d8995ee700e906071380b6799a4b2a52f63b13ea4110 Homepage: https://github.com/jupyter/nbformat Description: Jupyter notebook format (documentation) This software component contains the reference implementation of the Jupyter notebook format, and Python APIs to work with notebooks. . This package installs the documentation. Package: python-nbformat Architecture: all Version: 4.4.0-1~bpo9+1 Priority: optional Section: python Source: nbformat Maintainer: Debian Python Modules Team Installed-Size: 202 Depends: python-ipython-genutils, python-jsonschema, python-jupyter-core, python-traitlets, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/jupyter-notebook/backports/nbformat/python-nbformat_4.4.0-1~bpo9+1_all.deb Size: 31106 MD5sum: 037ca07c25a17fbec37c417b4bdfa915 SHA1: 1326ce58bb9bbbbbdbbe81507753d05b032f5220 SHA256: 5a187a0d7405b5e402c4907f00e986e3d4a35d0d8d9c59f2b0c24f8ca7299625 SHA512: 77e3f7e05dfb2cbd703cd950ef175c191b073bb91b87cbb321ccb3e2f4ac2988a8f42c2733a0f2612c10d71c8026404ead60b36a8df8fa0fffc748dc2bedba0d Homepage: https://github.com/jupyter/nbformat Description: Jupyter notebook format (Python 2) This software component contains the reference implementation of the Jupyter notebook format, and Python APIs to work with notebooks. . This package installs the library for Python 2. Package: python3-nbformat Architecture: all Version: 4.4.0-1~bpo9+1 Priority: optional Section: python Source: nbformat Maintainer: Debian Python Modules Team Installed-Size: 202 Depends: python3-ipython-genutils, python3-jsonschema, python3-jupyter-core, python3-traitlets, python3:any (>= 3.3.2-2~) Filename: pool-stretch/jupyter-notebook/backports/nbformat/python3-nbformat_4.4.0-1~bpo9+1_all.deb Size: 31180 MD5sum: cebcd2f849bcd2e59c1671cde4fc5707 SHA1: 1b89fe9e9d2de1c7302aad234e1ebd43f380a2bc SHA256: 84c3b639a35b2952bed885e69bf1a14f0d93db094ccdd0d80fc61e006d24c0de SHA512: 4db5527b1b791b1af02547f765595fb5fc2a830e8a564958674cb5c481d1865f3070a0a81b5c74d2670799200f3173e3239bc5c72c908ba4438b84a7f69ee22a Homepage: https://github.com/jupyter/nbformat Description: Jupyter notebook format (Python 3) This software component contains the reference implementation of the Jupyter notebook format, and Python APIs to work with notebooks. . This package installs the library for Python 3. Package: python-nbsphinx-doc Architecture: all Version: 0.2.14+ds-2~bpo9+1 Priority: optional Section: doc Source: nbsphinx Maintainer: Debian Python Modules Team Installed-Size: 1382 Depends: libjs-mathjax, libjs-sphinxdoc (>= 1.0) Suggests: www-browser Enhances: python-nbsphinx (= 0.2.14+ds-2~bpo9+1), python3-nbsphinx (= 0.2.14+ds-2~bpo9+1) Filename: pool-stretch/jupyter-notebook/backports/nbsphinx/python-nbsphinx-doc_0.2.14+ds-2~bpo9+1_all.deb Size: 867474 MD5sum: 61b356fd9487542d5c50c603bea64874 SHA1: b16adc7d7a6223a0769908509dc58ec74c6abe6c SHA256: 76d564faede510e47881bd43e4892b4e8a7c3e6f25f1e9006acc626f3bad7c87 SHA512: d450c33533eeba6ef111b23898c67d464ff28a943e7c79919761c26e222a7c729d83b5006cfaf55b3b3f0e3fcb49522199c3528825f7c0f26684d7dc35523a13 Homepage: http://nbsphinx.rtfd.org/ Description: Jupyter Notebook Tools for Sphinx -- doc nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluatednotebooks, i.e., notebooks without stored output cells, will be automatically executed during the Sphinx build process. . This is the common documentation package. Package: python-nbsphinx Architecture: all Version: 0.2.14+ds-2~bpo9+1 Priority: optional Section: python Source: nbsphinx Maintainer: Debian Python Modules Team Installed-Size: 110 Depends: python-docutils, python-jinja2, python-nbconvert, python-nbformat, python-sphinx, python-traitlets, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-nbsphinx-doc (= 0.2.14+ds-2~bpo9+1) Filename: pool-stretch/jupyter-notebook/backports/nbsphinx/python-nbsphinx_0.2.14+ds-2~bpo9+1_all.deb Size: 26248 MD5sum: 8cb0a423e026355f78ac842d695e75ec SHA1: 48029830b447761744834b7d269754a596f0b36c SHA256: 222e8a0db8e1a283b3b6bba1db2ab1b27d45149a59b637f2b326a937965cd757 SHA512: 52ce2cf058588c3988340acc69bec3e9db17f47a0335c4bf2ea7f764974d824ea306709280f0dbddfa9f2692028238deef7656aa7ec62e1905dad01be60948e1 Homepage: http://nbsphinx.rtfd.org/ Description: Jupyter Notebook Tools for Sphinx -- Python nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluatednotebooks, i.e., notebooks without stored output cells, will be automatically executed during the Sphinx build process. . This package installs the library for Python 2. Package: python3-nbsphinx Architecture: all Version: 0.2.14+ds-2~bpo9+1 Priority: optional Section: python Source: nbsphinx Maintainer: Debian Python Modules Team Installed-Size: 110 Depends: python3-docutils, python3-jinja2, python3-nbconvert, python3-nbformat, python3-sphinx, python3-traitlets, python3:any (>= 3.3.2-2~) Suggests: python-nbsphinx-doc (= 0.2.14+ds-2~bpo9+1) Filename: pool-stretch/jupyter-notebook/backports/nbsphinx/python3-nbsphinx_0.2.14+ds-2~bpo9+1_all.deb Size: 26336 MD5sum: 6c2c69f5ef228ff18a724417a0a61355 SHA1: 73a1b35cabcd4cd4ee6ba0b0f0e49bf51ddd1ae8 SHA256: 974a39836547110cc51fe78f3b6eb48da22739d74937cd7e5a3d006d6312ae91 SHA512: 368a8968d277f3f61a8b2de5a0c742a8bfdaf0b75fc60953d9fac2d02721c4e293931b153141080e7719a2512b871ec0a414c0b1c34d3a72622bd797e1a67be4 Homepage: http://nbsphinx.rtfd.org/ Description: Jupyter Notebook Tools for Sphinx -- Python 3 nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluatednotebooks, i.e., notebooks without stored output cells, will be automatically executed during the Sphinx build process. . This package installs the library for Python 3. Package: libjs-jed Architecture: all Version: 1.1.1-1~bpo9+1 Priority: optional Section: javascript Source: node-jed Maintainer: Debian Javascript Maintainers Installed-Size: 50 Depends: javascript-common Filename: pool-stretch/jupyter-notebook/backports/node-jed/libjs-jed_1.1.1-1~bpo9+1_all.deb Size: 13280 MD5sum: ac27eab0132138983c12bc8238088aad SHA1: 69e70d9aaba342efe66b013339e6210979ee6277 SHA256: 391c529714475ef01534fffd66928a26dab841d3979955c3fa1b63edf4ed3420 SHA512: 4170e37af5993ccd781acc69c9a5130c21c1ac7edda04daede404a439953f41644a0ba8db90f04eae204566c5985254c19a258f8332e608f0df7e7ed09a69f60 Homepage: https://github.com/SlexAxton/Jed#readme Description: Gettext Style i18n for Modern JavaScript Apps - JavaScript library If you don't specifically need a gettext implementation, look at MessageFormat instead, as it has better support for plurals/gender and has built-in locale data. . Jed doesn't include a Gettext file parser, but several third-party parsers exist that can have their output adapted for Jed. . This package contains jed suitable for use with browser environments. Package: node-jed Architecture: all Version: 1.1.1-1~bpo9+1 Priority: optional Section: web Maintainer: Debian Javascript Maintainers Installed-Size: 21 Depends: nodejs, libjs-jed Filename: pool-stretch/jupyter-notebook/backports/node-jed/node-jed_1.1.1-1~bpo9+1_all.deb Size: 5856 MD5sum: 037633d85315f26cf211e1d74d5cfbcd SHA1: 43533b63fc00535f0a6cbbf124aacc636046b046 SHA256: 731e18bbce29c4a5edfc098639ab7283eba32ce19be2e2a6c327468041727ad0 SHA512: c9da42ec87d37919c1c4d31e7283543022d039584ebd006ac97c9ce29a7438da5e0398c59f1bb801937f189519652ef60042b1d0a7bdcef85c227d71fda34f25 Homepage: https://github.com/SlexAxton/Jed#readme Description: Gettext Style i18n for Modern JavaScript Apps - Node.js module If you don't specifically need a gettext implementation, look at MessageFormat instead, as it has better support for plurals/gender and has built-in locale data. . Jed doesn't include a Gettext file parser, but several third-party parsers exist that can have their output adapted for Jed. . Node.js is an event-based server-side JavaScript engine. Package: libjs-xterm Architecture: all Version: 2.7.0+ds1-1~bpo9+1 Priority: optional Section: web Source: node-xterm Maintainer: Debian Javascript Maintainers Installed-Size: 227 Filename: pool-stretch/jupyter-notebook/backports/node-xterm/libjs-xterm_2.7.0+ds1-1~bpo9+1_all.deb Size: 32030 MD5sum: a24fecb8f83a59ff4bdc0d5000f7ef2f SHA1: 14106e2b9dd5ae0df236256dcdeace67d2c330c5 SHA256: e66ada3a8bab8fde197060d4316f0696c930627b84a3bdf8cd4002b8287fa7ff SHA512: ba27ef9cb67d7b500badfc547a1de6625a84e933a7f1eb55e5f224065dfb168d719e403d67f3573a10f51a770f854f853672100fbc56117b763b8321605bea2e Homepage: https://xtermjs.org Description: terminal front-end component for the browser - browser library Xterm.js is a terminal front-end component written in JavaScript that works in the browser. . It enables applications to provide fully featured terminals to their users and create great development experiences. . Features: . - **Text-based application support**: Use xterm.js to work with applications like `bash`, `git` etc. - **Curses-based application support**: Use xterm.js to work with applications like `vim`, `tmux` etc. - **Mouse events support**: Xterm.js captures mouse events like click and scroll and passes them to the terminal's back-end controlling process - **CJK (Chinese, Japanese, Korean) character support**: Xterm.js renders CJK characters seamlessly - **IME support**: Insert international (including CJK) characters using IME input with your keyboard - **Self-contained library**: Xterm.js works on its own. It does not require any external libraries like jQuery or React to work - **Modular, event-based API**: Lets you build addons and themes with ease . This package contains the standalone packed library suitable for running in a web browser. Package: node-xterm Architecture: all Version: 2.7.0+ds1-1~bpo9+1 Priority: optional Section: web Maintainer: Debian Javascript Maintainers Installed-Size: 562 Depends: libjs-xterm Suggests: nodejs Filename: pool-stretch/jupyter-notebook/backports/node-xterm/node-xterm_2.7.0+ds1-1~bpo9+1_all.deb Size: 64760 MD5sum: 259363b0916ff3bc44c5ac68ad30396e SHA1: cad18782de853cdb49a92f05cd57200844089ab9 SHA256: 77910b22a13450546adb4650f8cfd8d6630f844eb470c378deb584308d61341b SHA512: 7e472b0764b87fd8cacf0553c3ac457a025a59a37e99b7ca7558b99a6e9f2a109f08db41887081e12fde270b0527ea1f562bdccd38699565e53fb039d1de7d67 Homepage: https://xtermjs.org Description: terminal front-end component for the browser - NodeJS modules Xterm.js is a terminal front-end component written in JavaScript that works in the browser. . It enables applications to provide fully featured terminals to their users and create great development experiences. . Features: . - **Text-based application support**: Use xterm.js to work with applications like `bash`, `git` etc. - **Curses-based application support**: Use xterm.js to work with applications like `vim`, `tmux` etc. - **Mouse events support**: Xterm.js captures mouse events like click and scroll and passes them to the terminal's back-end controlling process - **CJK (Chinese, Japanese, Korean) character support**: Xterm.js renders CJK characters seamlessly - **IME support**: Insert international (including CJK) characters using IME input with your keyboard - **Self-contained library**: Xterm.js works on its own. It does not require any external libraries like jQuery or React to work - **Modular, event-based API**: Lets you build addons and themes with ease . This package contains the unpacked individual xterm CommonJS modules. Package: python-pandocfilters Architecture: all Version: 1.4.2-1~bpo9+1 Priority: optional Section: python Maintainer: Debian Python Modules Team Installed-Size: 76 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: pandoc (>= 1.16~) Filename: pool-stretch/jupyter-notebook/backports/python-pandocfilters/python-pandocfilters_1.4.2-1~bpo9+1_all.deb Size: 19676 MD5sum: 7af8b7f6206cbe8dbd698cf47a258fba SHA1: d02a91378455e4e5d980cc8f58c52d6e0fc76e8b SHA256: 9eacdaa52932ede4093c1b60f4f96cdc39e7706ca63973bf8875f194402cd456 SHA512: e46262139fc6731dde1ce51723a155dd88d084245ca455284faddbd213dbe6621c7cc895edcc8750031cbf06f0f61310def9b146cefccd8807363f7e11954747 Homepage: https://pypi.python.org/pypi/pandocfilters/ Description: python bindings for Pandoc's filters Pandoc is a powerful utility to transform various input formats into a wide range of output formats. To alter the exported output document, Pandoc allows the usage of filters, which are pipes that read a JSON serialization of the Pandoc AST from stdin, transform it in some way, and write it to stdout. It allows therefore to alter the processing of Pandoc's supported input formats, for instance one can add new syntax elements to markdown, etc. . This package provides Python bindings. Package: python3-pandocfilters Architecture: all Version: 1.4.2-1~bpo9+1 Priority: optional Section: python Source: python-pandocfilters Maintainer: Debian Python Modules Team Installed-Size: 76 Depends: python3:any (>= 3.3.2-2~) Recommends: pandoc (>= 1.16~) Filename: pool-stretch/jupyter-notebook/backports/python-pandocfilters/python3-pandocfilters_1.4.2-1~bpo9+1_all.deb Size: 19770 MD5sum: f9d6593a95670af7b6c0be36c9e12b64 SHA1: e004fd048ec8542d63bcd34a0db43eb04e46a366 SHA256: 3bc9baa26f86d0e7513b5731172580ca3a8d8a5c5c03c7ee8818bd69acb303e9 SHA512: 720dc10412d62471fc9c22baf28991a56c9f50d8676d97bd31f70503a5a078af94dfeb2a4221bb0adee70451bf19c3a44609bbd48d7c8a53575fc9533834eabe Homepage: https://pypi.python.org/pypi/pandocfilters/ Description: python3 bindings for Pandoc's filters Pandoc is a powerful utility to transform various input formats into a wide range of output formats. To alter the exported output document, Pandoc allows the usage of filters, which are pipes that read a JSON serialization of the Pandoc AST from stdin, transform it in some way, and write it to stdout. It allows therefore to alter the processing of Pandoc's supported input formats, for instance one can add new syntax elements to markdown, etc. . This package provides Python3 bindings. Package: node-typescript-types Architecture: all Version: 20170519-1~bpo9+1 Priority: extra Section: libs Source: typescript-types Maintainer: Debian Javascript Maintainers Installed-Size: 2081 Filename: pool-stretch/jupyter-notebook/backports/typescript-types/node-typescript-types_20170519-1~bpo9+1_all.deb Size: 177774 MD5sum: a39b55d94fdbd0b751d9fa4ed5b84a21 SHA1: 2a2fdbfeaa19c71b5ee8221700a3492827bd6678 SHA256: a341892c823534b582ddb28092a691eb7d7096428d3f60630dae4c446b659df1 SHA512: 6202132bf1ab9bebe6d89360e308dede9eb2e2f3084b098b59a55b9770d5ed6a58de1cd398b2c7e8789e218f1308083667fc114152fa5d0164da2bd0a0798214 Homepage: http://definitelytyped.org/ Description: Supposedly "high quality" TypeScript type definitions TypeScript type definitions supplied by the DefinitelyTyped project, for JavaScript packages that don't supply their own type definitions. . This description would be longer, but upstream does not give one on their website nor on their Github page. After some very painful experience using NPM, one can eventually deduce that these definitions are needed for certain typescript packages that build on top of javascript packages, where these latter packages don't themselves define any typescript types. . This package contains a subset of the upstream type definitions because there are a ridiculous amount (a few hundred megabytes) and the vast majority of them are probably never going to be needed for Debian. Currently these are: . @types/backbone 1.3.33 @types/chai 3.5.2 @types/expect.js 0.3.29 @types/fs-extra 3.0.1 @types/handlebars 4.0.32 @types/highlight.js 9.1.9 @types/jquery 2.0.45 @types/jsdom 2.0.30 @types/lodash 4.14.64 @types/marked 0.0.28 @types/mathjax 0.0.31 @types/minimatch 2.0.29 @types/minimist 1.2.0 @types/mocha 2.2.41 @types/node 7.0.18 @types/requirejs 2.1.29 @types/semver 5.3.31 @types/shelljs 0.7.1 @types/sinon 2.2.2 @types/underscore 1.8.0 . If you need more than these, please file a bug report asking for git access so that you can update this package yourself. Package: jupyter-notebook Architecture: all Version: 5.1.0-1~bpo9+1 Priority: optional Section: science Maintainer: Debian Python Modules Team Installed-Size: 27 Depends: python3:any (>= 3.3~), python3-notebook (= 5.1.0-1~bpo9+1), jupyter-core Filename: pool-stretch/jupyter-notebook/unreleased/jupyter-notebook/jupyter-notebook_5.1.0-1~bpo9+1_all.deb Size: 7520 MD5sum: c7c16ce63e7c703ee711237a1f17d993 SHA1: d81dcaee7e2fa891585b77a5fc652cf5cccb47a6 SHA256: 9358d20d8d9715b7663c7f4d81e09ff2213a44f661ae66e43c0842bc6b19cf7b SHA512: 2aadb0d21059475af1d155c91d19b890125a659510805391938a93cb5b7474ad02562bce3e5ce911cc4ddb5f9615f253ddf35a328c4c80d431f4386bd7bc3afc Homepage: https://github.com/jupyter/notebook Description: Jupyter interactive notebook The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Notebook has support for multiple programming languages, sharing, and interactive widgets. . This package provides the jupyter subcommands "notebook", "nbextension", "serverextension" and "bundlerextension". Package: python-notebook-doc Architecture: all Version: 5.1.0-1~bpo9+1 Built-Using: sphinx (= 1.4.9-2) Priority: optional Section: doc Source: jupyter-notebook Maintainer: Debian Python Modules Team Installed-Size: 6065 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common, libjs-mathjax Filename: pool-stretch/jupyter-notebook/unreleased/jupyter-notebook/python-notebook-doc_5.1.0-1~bpo9+1_all.deb Size: 2392666 MD5sum: 719edd5ac2ae4e443ddff698541c7320 SHA1: 5983ca71012c5f5e76528e1416111a79887cdb60 SHA256: 4adb8014a66a64783afbcbcf46dc995ddc5144da7e90f07cdb6d11f2e66146e2 SHA512: da3a413caaba084d9f1c57b34628bbe8d2e6a7ab918a278c74b454d013ffef60aeaca6af864df5a9ca040ff86501caacbfd8354e927fd775bf70cbdf75eaf895 Homepage: https://github.com/jupyter/notebook Description: Jupyter interactive notebook (documentation) The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Notebook has support for multiple programming languages, sharing, and interactive widgets. . This package contains the documentation. Package: python-notebook Architecture: all Version: 5.1.0-1~bpo9+1 Built-Using: backbone (= 1.3.3~dfsg-1), codemirror-js (= 5.4.0-1), jquery (= 3.1.1-2), jquery-typeahead.js (= 2.7.6+dfsg1-1), jqueryui (= 1.12.1+dfsg-4), libjs-requirejs-text (= 2.0.12-1), node-bootstrap-tour (= 0.11.0+dfsg-1), node-jed (= 1.1.1-1~bpo9+1), node-moment (= 2.17.1+ds-1), node-xterm (= 2.7.0+ds1-1~bpo9+1), requirejs (= 2.3.2-1), twitter-bootstrap3 (= 3.3.7+dfsg-2), underscore (= 1.8.3~dfsg-1) Priority: optional Section: python Source: jupyter-notebook Maintainer: Debian Python Modules Team Installed-Size: 13969 Depends: python-ipykernel, python-ipython-genutils, python-jinja2, python-jupyter-client, python-jupyter-core, python-nbconvert, python-nbformat, python-tornado, python-traitlets, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-terminado (>= 0.3.3), libjs-backbone (>= 1.2), libjs-bootstrap (>= 3.3), libjs-bootstrap-tour (>= 0.9), libjs-codemirror, libjs-es6-promise (>= 1.0), fonts-font-awesome (>= 4.2), libjs-jed (>= 1.1.1), libjs-jquery, libjs-jquery-ui (>= 1.12), libjs-marked (>= 0.3), libjs-mathjax (>= 2.5), libjs-moment (>= 2.8.4), libjs-requirejs (>= 2.1), libjs-requirejs-text, libjs-text-encoding (>= 0.1), libjs-underscore (>= 1.5), libjs-jquery-typeahead (>= 2.0), libjs-xterm (>= 2.3) Recommends: python-ipywidgets Suggests: python-notebook-doc Filename: pool-stretch/jupyter-notebook/unreleased/jupyter-notebook/python-notebook_5.1.0-1~bpo9+1_all.deb Size: 933096 MD5sum: ae02a6be39abf732c50731381e2e5748 SHA1: 31fca524bb3df59f9a5d347c05ee5127e64ac520 SHA256: 8ab6c905b9ee8d11db1129ecccc1b5139b7fb10e28513111b680ef4121b329bc SHA512: d403cd48c7a6707b33dd2918ae75caff8068b93ff97640eed2f1d877733516362d2aa3ff4b42d614c5bf4acbe10730e2f04ae3d9a32575e1686cfe7778008b5a Homepage: https://github.com/jupyter/notebook Description: Jupyter interactive notebook (Python 2) The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Notebook has support for multiple programming languages, sharing, and interactive widgets. . This package contains the Python 2 library. . This package is not required to run Python 2 code in the notebook, only to allow Python 2 code to interact directly with the notebook server. Package: python3-notebook Architecture: all Version: 5.1.0-1~bpo9+1 Built-Using: backbone (= 1.3.3~dfsg-1), codemirror-js (= 5.4.0-1), jquery (= 3.1.1-2), jquery-typeahead.js (= 2.7.6+dfsg1-1), jqueryui (= 1.12.1+dfsg-4), libjs-requirejs-text (= 2.0.12-1), node-bootstrap-tour (= 0.11.0+dfsg-1), node-jed (= 1.1.1-1~bpo9+1), node-moment (= 2.17.1+ds-1), node-xterm (= 2.7.0+ds1-1~bpo9+1), requirejs (= 2.3.2-1), twitter-bootstrap3 (= 3.3.7+dfsg-2), underscore (= 1.8.3~dfsg-1) Priority: optional Section: python Source: jupyter-notebook Maintainer: Debian Python Modules Team Installed-Size: 13969 Depends: python3-ipykernel, python3-ipython-genutils, python3-jinja2, python3-jupyter-client, python3-jupyter-core, python3-nbconvert (>= 5), python3-nbformat (>= 4.4), python3-tornado, python3-traitlets, python3:any (>= 3.3.2-2~), python3-terminado (>= 0.3.3), libjs-backbone (>= 1.2), libjs-bootstrap (>= 3.3), libjs-bootstrap-tour (>= 0.9), libjs-codemirror, libjs-es6-promise (>= 1.0), fonts-font-awesome (>= 4.2), libjs-jed (>= 1.1.1), libjs-jquery, libjs-jquery-ui (>= 1.12), libjs-marked (>= 0.3), libjs-mathjax (>= 2.5), libjs-moment (>= 2.8.4), libjs-requirejs (>= 2.1), libjs-requirejs-text, libjs-text-encoding (>= 0.1), libjs-underscore (>= 1.5), libjs-jquery-typeahead (>= 2.0), libjs-xterm (>= 2.3) Recommends: python3-ipywidgets Suggests: python-notebook-doc Filename: pool-stretch/jupyter-notebook/unreleased/jupyter-notebook/python3-notebook_5.1.0-1~bpo9+1_all.deb Size: 933214 MD5sum: 1042bc1e635f1608b41a70ec97d87e29 SHA1: da2aba37e736d16e69f603d805b8e811bc3e0562 SHA256: 0f58e1037c323f275614ed199db0e46138488be8c40b6e814dab0a9ef1435cc1 SHA512: d63187226a245ed75c8e9be2826347b28195cb3445e02cca2ef5a84d062830ae78b1bd8a87739c56ee9efd5652ccdef302595af4077c3eaa2a2c957181501044 Homepage: https://github.com/jupyter/notebook Description: Jupyter interactive notebook (Python 3) The Jupyter Notebook is a web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Notebook has support for multiple programming languages, sharing, and interactive widgets. . This package contains the Python 3 library. Package: jupyter-nbconvert Architecture: all Version: 5.3.1-0~bpo9+1 Priority: optional Section: utils Source: nbconvert Maintainer: Debian Python Modules Team Installed-Size: 21 Depends: python3-nbconvert (= 5.3.1-0~bpo9+1), python3:any (>= 3.3~) Filename: pool-stretch/jupyter-notebook/unreleased/nbconvert/jupyter-nbconvert_5.3.1-0~bpo9+1_all.deb Size: 10360 MD5sum: 2b5c877603daa25d4eaf27bee6214614 SHA1: 5630e159f8169dde095fd195f457a9589b5debe8 SHA256: c8967204d26bd70db29beaf171c935d426ed9fb42e2f20a3716c9ae9d8896e6c SHA512: b1d4099a48950d561e74c16917cf6ff1aec43e146d4b957175f5ff9fc01cbec5a347f2eecdc7fad9674ba7e413eb43ca701ccb238b19b1c67aa939b4db8c7197 Homepage: https://github.com/jupyter/nbconvert Description: Jupyter notebook conversion (scripts) Jupyter nbconvert converts notebooks to various other formats using Jinja templates. . This package installs the jupyter-nbconvert script. Package: python-nbconvert-doc Architecture: all Version: 5.3.1-0~bpo9+1 Built-Using: sphinx (= 1.4.9-2) Priority: optional Section: doc Source: nbconvert Maintainer: Debian Python Modules Team Installed-Size: 1995 Depends: libjs-jquery, libjs-mathjax, libjs-requirejs, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/jupyter-notebook/unreleased/nbconvert/python-nbconvert-doc_5.3.1-0~bpo9+1_all.deb Size: 243764 MD5sum: 70b0d60d466f088b787ed42de9043208 SHA1: 66a8eef8f7da428f6598b28cff504d35e8ce03a6 SHA256: 38e00adb17259551bfbcbb902f77b49b097e1bfc00ab2834e45f1bd41f8a176d SHA512: 214aa046b0af40aef0566936812922694b352c97ea2e7dbbcca5179f72e06784328818344e9e3d72024a5231d624077ba42c1c71eee68bf201d95ea7243f2692 Homepage: https://github.com/jupyter/nbconvert Description: Jupyter notebook conversion (documentation) Jupyter nbconvert converts notebooks to various other formats using Jinja templates. . This package installs the documentation. Package: python-nbconvert Architecture: all Version: 5.3.1-0~bpo9+1 Priority: optional Section: python Source: nbconvert Maintainer: Debian Python Modules Team Installed-Size: 579 Depends: python-entrypoints, python-ipython (>= 5), python-jupyter-client, python-testpath, python-bleach, python-jinja2, python-jupyter-core, python-mistune, python-nbformat, python-pandocfilters, python-pygments, python-traitlets, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: pandoc Filename: pool-stretch/jupyter-notebook/unreleased/nbconvert/python-nbconvert_5.3.1-0~bpo9+1_all.deb Size: 96114 MD5sum: 812f3ab0607ef87ba9e798a3c197f95c SHA1: 5abe3084f0a796093da10a62798757562a9dc414 SHA256: 146526244fba3401886becd04085981e8760a13686d8cafafc524b264f43d0b4 SHA512: b45f2817d07d82bf2403de6b1e57df6fd949aee12014d3b84495cb66c71c5a65e1af2e4b60107c51da9fca5c37d60070b7cbe06c64f33e45a8cb1f71502e8885 Homepage: https://github.com/jupyter/nbconvert Description: Jupyter notebook conversion (Python 2) Jupyter nbconvert converts notebooks to various other formats using Jinja templates. . This package installs the library for Python 2. Package: python3-nbconvert Architecture: all Version: 5.3.1-0~bpo9+1 Priority: optional Section: python Source: nbconvert Maintainer: Debian Python Modules Team Installed-Size: 579 Depends: python3-entrypoints, python3-ipython (>= 5.0.0), python3-jupyter-client, python3-testpath, python3-bleach, python3-jinja2, python3-jupyter-core, python3-mistune, python3-nbformat (>= 4.4.0), python3-pandocfilters (>= 1.4), python3-pygments, python3-traitlets, python3:any (>= 3.3.2-2~) Recommends: pandoc Filename: pool-stretch/jupyter-notebook/unreleased/nbconvert/python3-nbconvert_5.3.1-0~bpo9+1_all.deb Size: 96214 MD5sum: 4dc41f918b7b1fb43a68ac309e5f4433 SHA1: d352edeab412e4cb7588ecf2f6e43c97c3337944 SHA256: 4cd624ac179511462c80dd560707c8554a07faf7f81dc810b8edd9659a29ab57 SHA512: 4b883d047dbf0a5178880838f36072e2e0bee550f0088497d2892cc99339ff0c858f857a8d61d0dba3fa2acfcc2d2309ffa0600f7d0362d6fc998460a357e771 Homepage: https://github.com/jupyter/nbconvert Description: Jupyter notebook conversion (Python 3) Jupyter nbconvert converts notebooks to various other formats using Jinja templates. . This package installs the library for Python 3. Package: keras-doc Architecture: all Version: 2.2.4-1~bpo+Debian.stretch.9.7 Priority: optional Section: doc Source: keras (2.2.4-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 2942 Suggests: python3-keras Filename: pool-stretch/keras/keras-doc_2.2.4-1~bpo+Debian.stretch.9.7_all.deb Size: 985648 MD5sum: 2481d417ad2fc6873f588bc3f2b66992 SHA1: c0a7649e4a6e458a5fdb42b4b6ed8c836009724d SHA256: e6bfb1899cbb2f354a11b5b29a30ae671a24f65fcf92d9dd451241c16a472161 SHA512: d07597bf4fb0aef277fee4893f99507bf0dea843269e9c1c397feea56ba37a5cf3bd4634143c8ac4383c409b8d8e6f850fb5345f966a7ba6bfb5cfff37b70dac 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.stretch.9.7 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 (>= 2.7.5-5~) Recommends: python-keras Filename: pool-stretch/keras/python-keras-applications_1.0.6-1~bpo+Debian.stretch.9.7_all.deb Size: 24904 MD5sum: 60c83efe1eccac7b70be95e8454feff8 SHA1: df589719ab8a3eed9590a5f8779755d7bfb3a044 SHA256: beaca92e1f632ea5018d9f9922cc1e77c8ecc12663debd903cd9dc810a40d803 SHA512: 45c6d206fc7497326ee5a8e4f2c1480e4d81a21cba6f031cbfa37a74c7fb8ab7273f6f6c65b84130d1b52c7948a44838a4b9d162d3723aeb2c177d12333869a3 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: 2.0.8-1+Debian-stretch-9.1 Priority: optional Section: doc Source: keras (2.0.8-1) Maintainer: Adam Cecile Installed-Size: 2348 Filename: pool-stretch/keras/python-keras-doc_2.0.8-1+Debian-stretch-9.1_all.deb Size: 764998 MD5sum: b934d2df01ff99386b3555ea229262a0 SHA1: 3e12b996ab561db74493c1f4aa7a520be107d9a6 SHA256: 62a4e71f82eb92bcdf2f71daf77d1521f3d18632bea3786a01f551e287a0f60b SHA512: 891dda09569632a73da8d41dbd8a4d6341ba4e34a515df758a021895b28d4244f5de999be445bd4e2c37ce58971d30849094de72abfad700ea4d8da5fc26b1cf 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-doc Architecture: all Version: 2.1.5-1+Debian-stretch-9.4 Priority: optional Section: doc Source: keras (2.1.5-1) Maintainer: Adam Cecile Installed-Size: 2459 Filename: pool-stretch/keras/python-keras-doc_2.1.5-1+Debian-stretch-9.4_all.deb Size: 789324 MD5sum: 291322ba6ea62aed777ffb23acbf041f SHA1: 31926c1033023b20727ff99d2c08eaf41686c4ef SHA256: 6e0ae5ab4e8a4826f1ca7768178a99ad824b6e0c36c941524684e60129583c50 SHA512: ade16d5911d3815d219d1e148600eda5efeb1f616d568b20968390d7d2577b57d993bf6ba23c55782f1ebe7137f024ad7128284f6234449e0f8d71071702d4f7 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.stretch.9.7 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 (>= 2.7.5-5~) Recommends: python-keras Filename: pool-stretch/keras/python-keras-preprocessing_1.0.5-1~bpo+Debian.stretch.9.7_all.deb Size: 29600 MD5sum: e3d47deae2a666e6cb1fcdd445cef605 SHA1: 452a40066c3a2433f7373c4c43841273be618437 SHA256: f7033775da0acbe0acc1841b1c6e3e198c5e96e6f7a3890f0588ce6ce0683c0b SHA512: 81c1c54727b3d09ff6ed8833561c66f6af497def3e0633440c4856b2b77b4d0fcff918c48a0a8e794bd00ec8662aaa02dea51ba48b259d7b3b4818160b8dc83d 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: 2.0.8-1+Debian-stretch-9.1 Priority: optional Section: python Source: keras (2.0.8-1) Maintainer: Adam Cecile Installed-Size: 1346 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-stretch/keras/python-keras_2.0.8-1+Debian-stretch-9.1_all.deb Size: 179724 MD5sum: e51dc549bb2ec309a807c39da4cb6b9f SHA1: e31c18990b85a650250dd456c1bb09622a8c8840 SHA256: 5b11e6c656a5ad83f1bf13908830dc2cadb15117dd3a6397a495ccb4f1144c92 SHA512: 4488004ed3184620d6c37484bd9cc34c1494d24df2d74252c5f3838324b22f3b9a403d8de52889d5b3127dcf400810879a57fd42ea9bc0ee11421a000bfc7d4e 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: 2.1.5-1+Debian-stretch-9.4 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-stretch/keras/python-keras_2.1.5-1+Debian-stretch-9.4_all.deb Size: 208988 MD5sum: 93acd5cb6a2768a3f59b8e9d2df0ca09 SHA1: 337f54b7e3df3022835306b3d116b8e882bd4b68 SHA256: 6aac15b4155c224ff27cca9374f8ff728c1b09e2682d52f9371fe8cd3195b7df SHA512: d90b3c70c54fb292ed4f678b19d4b17a9f6bc94c82adf9121b4372e433cf8c482059f881a75bccb07ef8485eb607f4aaa1651acdf2d4f6a5a1bfc6a6861c79e6 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.stretch.9.7 Priority: optional Section: python Source: keras (2.2.4-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 184631 Depends: python-numpy, python-scipy, python-h5py, python-theano, python (<< 2.8), python (>= 2.7~), python-keras-applications, python-keras-preprocessing, python-six (>= 1.9.0), python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/keras/python-keras_2.2.4-1~bpo+Debian.stretch.9.7_all.deb Size: 15808472 MD5sum: 7ce4447134c4ff582357f0b997ea1270 SHA1: 5214fdd39aee8940a129162c8e7b4bd5d096e7d1 SHA256: 410ed299c767415201154b271b1f40f7c9fbdb70de35741f0c713d84727cf3a5 SHA512: 8a76bf1cf3f4a877152ac0f06c1b3002e5692caf16005b80ef7e50c4c68e75391e497d6540c01b4c8e90bc97b23a5584dc34465a49289df43177cff9f0e22811 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.stretch.9.7 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-stretch/keras/python3-keras-applications_1.0.6-1~bpo+Debian.stretch.9.7_all.deb Size: 24968 MD5sum: cb3f1bb1f5a53e26c7fab24fc3b112b5 SHA1: 3e28ece92fedefc412ef636c554d5c81adbf4798 SHA256: eb335aa7cd2bd017661a4e8b7f781167127875266b4824676bc36b61735bf58b SHA512: 3849a15d9fc02afd1294bdd934fed8d5aba45851a5409fb86373feaf9184cb32f66267293b6a914c52b40926cbaf1116c7f9d81f369b273f11280e49c89741ab 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-preprocessing Architecture: all Version: 1.0.5-1~bpo+Debian.stretch.9.7 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-stretch/keras/python3-keras-preprocessing_1.0.5-1~bpo+Debian.stretch.9.7_all.deb Size: 29686 MD5sum: 5661f34aa7887b538d1373071a15de95 SHA1: 5d4c706d4c03535673b563c317dd2c35a3f79014 SHA256: a574efa34c54b8d69f0d924647b64c535856b929965db571d305491076001cda SHA512: 3ccb6934b503de8237ee1d41e2c5bcd1368d1473dd76e0e7e015411af3636483d8404769d395c06cbd5d5bee568718145ee6e312b15d9e5616b3993f3da3593c 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: 2.0.8-1+Debian-stretch-9.1 Priority: optional Section: python Source: keras (2.0.8-1) Maintainer: Adam Cecile Installed-Size: 1346 Depends: python3-numpy, python3-scipy, python3-six (>= 1.9.0), python3-yaml, python3:any (>= 3.3.2-2~) Recommends: python3-tensorflow Suggests: python-keras-doc Filename: pool-stretch/keras/python3-keras_2.0.8-1+Debian-stretch-9.1_all.deb Size: 179852 MD5sum: 2f3beaba651950b1beee89b73f7bc5e2 SHA1: e15d77a0c0dd46b450887db0bd2fde1f2376fe52 SHA256: 178d96192db19dd3e9349f765f017a6b3faa4c51314f0572d7d7c688c186cac9 SHA512: bb7203b07bdaa5e32729287147a365e95499a4017189e2ec0ee80a7e757aa0cebb7cc3a08bc56f22a2b9aceb6ad1919893af53df78fede8a4b738770f274f73f 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: 2.1.5-1+Debian-stretch-9.4 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-stretch/keras/python3-keras_2.1.5-1+Debian-stretch-9.4_all.deb Size: 209106 MD5sum: 6981a973eeb329ef46dd2066343aa7d3 SHA1: fc634fc338c49ced656fb32be8e27b4b358a65ec SHA256: 5cbdb05e8ce5d02a408f3df066f1a27044d7a0c78b14148921895d6007f10e91 SHA512: 03b239d787008434e85dd99c0513f2e02c935549d43806a518cee81ee7fa0b992d25d706c821e17e72cafe9d96e732a82451c451d53b8b03ad097c2c8f4764b3 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.stretch.9.7 Priority: optional Section: python Source: keras (2.2.4-1~bpo) Maintainer: Debian Science Maintainers Installed-Size: 190887 Depends: python3-numpy, python3-scipy, python3-h5py, python3-theano, python3 (<< 3.6), python3 (>= 3.5~), python3-keras-applications, python3-keras-preprocessing, python3-six (>= 1.9.0), python3-yaml, python3:any (>= 3.4~) Filename: pool-stretch/keras/python3-keras_2.2.4-1~bpo+Debian.stretch.9.7_all.deb Size: 16643378 MD5sum: e86f47b6fe3b115a157378a1c95023ac SHA1: e53d7c34d2ff53d60955cb04a83ad4b6608d88e4 SHA256: 20f46f3c147656d93aa3e53ffeb92cc36c97293ce4e8b5b9c2b331d591f14668 SHA512: dafbc6aed92a90d0a1e19f915512ae750eeb7d57f420f9ab4c1481acc2b264cd39ac804ae04a21333349ea45ca14574b886b19a4e6e62e8368a46817fdd2f3cc 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: lightgbm-dev Architecture: all Version: 2.0.7+debian-2+Debian.stretch.9.1 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-stretch/lightgbm/lightgbm-dev_2.0.7+debian-2+Debian.stretch.9.1_all.deb Size: 38578 MD5sum: b2332386e21878b3dcd7489e46f5da4c SHA1: 139e3f915a5238b9241805e9bc8e404bba17bf5d SHA256: 593b5fc1581a8e0a3d80a6345f8f1ef26e3b95bbbc97b3b069a24712d4c4894d SHA512: e30f6390c079cf50845b1f3cd8abe2564f4a587d9442b76b28e76810943e9e1170dcbf8a0cff7ef826459bc7da7e3016c9aa4d0006235668b74b2be75e76e352 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.stretch.9.1 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-stretch/lightgbm/lightgbm-dev_2.0.8+debian-1+Debian.stretch.9.1_all.deb Size: 39056 MD5sum: 5553ebc60891d1e2766124c9cb6deba4 SHA1: 82329fa37eabb6f0011b47a2a82e165c63ab2cb7 SHA256: a14e4bfd2be32988f75005ce990eed65c132f7811c222070b3ed61bb6005dc04 SHA512: b45144f42c5c6be9bc20035f2b5b6d73c2ff7415ec69b7ede570c7cf62c1c61b6a46f6bb885058f14ac79b7aeceaa5bff0b286dcb5de15cba4f7d3f9b54aa460 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.stretch.9.5 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-stretch/lightgbm/lightgbm-dev_2.2.1+debian-1+Debian.stretch.9.5_all.deb Size: 49580 MD5sum: 03f0e3dac8a6503ac81fbaddae3b1e71 SHA1: a43d43e131853da257c192d1813f8df3dffc920c SHA256: 02687540b955e0f67366b7a8dcfc7c244a406acfc0223bd87eca11606fdb34a3 SHA512: 2baaf62df16f9e753060718e1b154a2fdbd689f23cde6d9232f339a00ca820ae6a094a5f62b805e6cb075f0bc6d9733feecadc910f7a67194ae54d18d044d3cd 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: 2.2.3+debian-1+Debian.stretch.9.8 Priority: optional Section: devel Source: lightgbm (2.2.3+debian-1) Maintainer: Adam Cecile Installed-Size: 279 Depends: liblightgbm (>= 2.2.3+debian-1), liblightgbm (<< 2.2.3+debian-1.1~) Filename: pool-stretch/lightgbm/lightgbm-dev_2.2.3+debian-1+Debian.stretch.9.8_all.deb Size: 49886 MD5sum: ef17a0ff9ddfafc43f7e2f561267b9c6 SHA1: 09dc45ecca83076a13a68f7982482a5d385292a3 SHA256: a30bfe74529c89d26f8d749a1469783b1936d010b1681bc0fcd5e6cf702e49eb SHA512: 0d970c314a1d00d670a90279d0dfb2d7786502e26486a9a20214c5445cba1ecedb8abf90b7c30fc0c943aefa793e3a474480de111d0401482233d5a870b0f08d 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: lightgbm-dev Architecture: all Version: 2.3.0+debian-1+Debian.stretch.9.11 Priority: optional Section: devel Source: lightgbm (2.3.0+debian-1) Maintainer: Adam Cecile Installed-Size: 306 Depends: liblightgbm (>= 2.3.0+debian-1), liblightgbm (<< 2.3.0+debian-1.1~) Filename: pool-stretch/lightgbm/lightgbm-dev_2.3.0+debian-1+Debian.stretch.9.11_all.deb Size: 52756 MD5sum: cbff7e11e76501273cac9c110993e4fe SHA1: d69a8371ddd2df7733aa1f78f9ad7946839c9d19 SHA256: 3b45382181af6e477abcbd5d5a47b8073c50a8d2286b18eedd0d434587a0e5f6 SHA512: cc25795e64c0d5d1c6b4c5c5367adcfac2e52fa0039f65d1eadad8846b4d75cea2d517c1574f9456b7541ae03f0a99c6b13e2f91df016264b553935cb845f319 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: python-lightgbm Architecture: all Version: 2.0.7+debian-2+Debian.stretch.9.1 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-stretch/lightgbm/python-lightgbm_2.0.7+debian-2+Debian.stretch.9.1_all.deb Size: 32574 MD5sum: a015829d9208a34e5964afd867cec175 SHA1: 159b70d551d35c30feaa04995a1ffbdcfd19f84b SHA256: 11ec00adf729873f2a191a0f974f7a2f51b170af2a8c14887cc07a9222d0ca05 SHA512: e3e555bc8339f3153761e4bd0540dba377a4cedabebaf2f57d4385a64696aa598959e76b39e7bf58868b1b5a9b9a1cf573f6693d19b2ac18fc2af69df391fefe 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. . 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 Python2 module. Package: python-lightgbm Architecture: all Version: 2.0.8+debian-1+Debian.stretch.9.1 Priority: optional Section: python Source: lightgbm (2.0.8+debian-1) Maintainer: Adam Cecile Installed-Size: 188 Depends: python-numpy, python-scipy, python-sklearn, python:any (<< 2.8), python:any (>= 2.7.5-5~), liblightgbm (>= 2.0.8+debian-1), liblightgbm (<< 2.0.8+debian-1.1~) Filename: pool-stretch/lightgbm/python-lightgbm_2.0.8+debian-1+Debian.stretch.9.1_all.deb Size: 32576 MD5sum: 19795ca2d101bcfbb0bee1398084a294 SHA1: 2510fb82392a49292b50327d270d60dadfcccb30 SHA256: 9ce9714454bb83d53b9a767e1699c2342ac0dd9bf6068085e5795f664e3f8b46 SHA512: 732f1210eeae40a4273dcc461e1138cb458ef735777250c2a235aa88188fa6c6fabf1728b05b34a6017ff0967bc3ec7a7e4800c833dcbbd4387f3482af81fbb2 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. . 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 Python2 module. Package: python-lightgbm Architecture: all Version: 2.2.1+debian-1+Debian.stretch.9.5 Priority: optional Section: python Source: lightgbm (2.2.1+debian-1) Maintainer: Adam Cecile Installed-Size: 222 Depends: python-numpy, python-scipy, python-sklearn, python:any (<< 2.8), python:any (>= 2.7.5-5~), liblightgbm (>= 2.2.1+debian-1), liblightgbm (<< 2.2.1+debian-1.1~) Filename: pool-stretch/lightgbm/python-lightgbm_2.2.1+debian-1+Debian.stretch.9.5_all.deb Size: 38068 MD5sum: 9eab720f3c9e2d53fc41deb09da5ad5b SHA1: 39874d1a7ff790ece2921c742a6e6c49d3629ea6 SHA256: 0e97dbe7d8acf0efacf384ba64dcd59a7e3b7597626676e15d6e80c2f71520ce SHA512: 6be309c8607257c28db7b14aed50cbed0b0084f3bdfac6732fb8df4c93a364b70cedd1517d6f882eed0c5d596846c6467a23fd850e4bcb2d9b54ba402b83bc27 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. . 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 Python2 module. Package: python-lightgbm Architecture: all Version: 2.2.3+debian-1+Debian.stretch.9.8 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 (>= 2.7.5-5~), liblightgbm (>= 2.2.3+debian-1), liblightgbm (<< 2.2.3+debian-1.1~) Filename: pool-stretch/lightgbm/python-lightgbm_2.2.3+debian-1+Debian.stretch.9.8_all.deb Size: 39182 MD5sum: 140e0473eea6d35f4e1b51b8b7b2fe6c SHA1: 45a602f3926701c2eed7d072159637de5d5dcc01 SHA256: 6c06b3a54881f0261e8ab98cc54d27d00f61da676a2935716410df94580763f2 SHA512: ca89df8ac40245e1fd25239e290412d0cba34b38da7306e092aae359190d3978442078993d68a3d9fbd53561a70dd51b5f57ae8456dba4e3b902c58e8dab8b38 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. . 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 Python2 module. Package: python-lightgbm Architecture: all Version: 2.3.0+debian-1+Debian.stretch.9.11 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 (>= 2.7.5-5~), liblightgbm (>= 2.3.0+debian-1), liblightgbm (<< 2.3.0+debian-1.1~) Filename: pool-stretch/lightgbm/python-lightgbm_2.3.0+debian-1+Debian.stretch.9.11_all.deb Size: 42942 MD5sum: a2f7f541bd90ec96868b659c77fa6373 SHA1: 76ef076c3f86c68765e470845c9c0193afdb6280 SHA256: 607cd686a1eb7db9e3f570e37a3741406b6a20227e7ce56c21ee9400771df054 SHA512: b964a6a9f7db5891437ed606098462b3870052ca1371ae634e7aa459c309abeaaab68bed6a5ce064b82bb863590b887e90ec56eb68bf51e73128f137b4fae1de 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. . 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 Python2 module. Package: python3-lightgbm Architecture: all Version: 2.0.7+debian-2+Debian.stretch.9.1 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-stretch/lightgbm/python3-lightgbm_2.0.7+debian-2+Debian.stretch.9.1_all.deb Size: 32658 MD5sum: 3667fe58e791a717030f715b0bb52732 SHA1: 7dedbb10422da27cd3a304ae5e20a7288115d963 SHA256: d7ef12cf7e776d899e815eed4ddda57367dc1ddda76a07da94047ef72314690a SHA512: b8504fcefb06898daf1ae135ac9bf455fb80b10914db2e01cfde5a145cc8a772c63e128f8c41ba8c37111e8d5217412d773d3d0bc59b5e77f77f61c547a7d432 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. . 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 Python3 module. Package: python3-lightgbm Architecture: all Version: 2.0.8+debian-1+Debian.stretch.9.1 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-stretch/lightgbm/python3-lightgbm_2.0.8+debian-1+Debian.stretch.9.1_all.deb Size: 32638 MD5sum: 186f7739b5a15d1d2005610639d88f93 SHA1: f55a52edba8f538ee7628b1629c71d44c9a530c6 SHA256: dbea1042795ed1400be3cd40d3a2813a7bd69d336ce295c4a1cae0e316eeeed4 SHA512: f43a48b045a2901810534c54c6bd206c055eebf25f9eb060ecc865c392bfd591d9bdf2485d955b8419c9911b137b28531e313d6c7a8e8ebf0e2e17b126e750d2 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. . 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 Python3 module. Package: python3-lightgbm Architecture: all Version: 2.2.1+debian-1+Debian.stretch.9.5 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-stretch/lightgbm/python3-lightgbm_2.2.1+debian-1+Debian.stretch.9.5_all.deb Size: 38150 MD5sum: cb4f51aad38baef66a7cfeeec601d62e SHA1: 9b36cee20f54b5eeac5779f379b4a3a0baab8a5b SHA256: 9e75467581d33e8055bce50317c9e7115a6c4dd61c2ea1afdfc4006dd4e1487b SHA512: 7d81acfbbd01a01362a774d757d6552f62b5d943a528bebb5a0b750bfbb62f7c57b57a03293c90a6f8ade648892897122430ce717544475b959ecebab900c2e7 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: clang-6.0-doc Architecture: all Version: 1:6.0.1-11~bpo9+0 Priority: optional Section: doc Source: llvm-toolchain-6.0 Maintainer: LLVM Packaging Team Installed-Size: 7407 Depends: libjs-mathjax Filename: pool-stretch/llvm-6.0.1/clang-6.0-doc_6.0.1-11~bpo9+0_all.deb Size: 927404 MD5sum: 815cc885309f64a8f56e186b1608052d SHA1: dae2ae1a8d913c6165390c5d4dcaa45b0fbf5d95 SHA256: 9e693fb7409637865a234f5b5d5e329975e81f073f5fe6fbbb834a928d3ce80f SHA512: 5374bf39b8b1610f70515f8503e71d0d70d0041ba0f94593bca0baa89d58a14370f87c73e3674d6ec6aa9656917cd9a6060559efa659ce00af0c5b336b5ead16 Homepage: https://www.llvm.org/ Description: C, C++ and Objective-C compiler - Documentation Clang project is a C, C++, Objective C and Objective C++ front-end based on the LLVM compiler. Its goal is to offer a replacement to the GNU Compiler Collection (GCC). . Clang implements all of the ISO C++ 1998, 11 and 14 standards and also provides most of the support of C++17. . This package contains the documentation. Package: llvm-6.0-doc Architecture: all Version: 1:6.0.1-11~bpo9+0 Priority: optional Section: doc Source: llvm-toolchain-6.0 Maintainer: LLVM Packaging Team Installed-Size: 13676 Depends: libjs-jquery, libjs-underscore Filename: pool-stretch/llvm-6.0.1/llvm-6.0-doc_6.0.1-11~bpo9+0_all.deb Size: 1872162 MD5sum: f2206687c1a5835e1a8eacd63e221e69 SHA1: f6714ecd8cb320d7d04ee364913eb151ed714f84 SHA256: e1d0d6b576119e02c8789d9b4fded2f0830fd710f6141c78d7894cda0f99be44 SHA512: 407411e5f558d37506e58b0601a29abb24079a7b6936c69c43030bea1252e0e3b3501a863434f51bdfa64ca2c5def5423f098a363a7a289de5c3434f48a729a5 Homepage: https://www.llvm.org/ Description: Modular compiler and toolchain technologies, documentation LLVM is a collection of libraries and tools that make it easy to build compilers, optimizers, just-in-time code generators, and many other compiler-related programs. . 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LLVM uses a single, language-independent virtual instruction set both as an offline code representation (to communicate code between compiler phases and to run-time systems) and as the compiler internal representation (to analyze and transform programs). This persistent code representation allows a common set of sophisticated compiler techniques to be applied at compile-time, link-time, install-time, run-time, or "idle-time" (between program runs). . This package contains examples for using LLVM, both in developing extensions to LLVM and in using it to compile code. Package: llvmlite-doc Architecture: all Version: 0.26.0-1~bpo9+0 Priority: optional Section: doc Source: llvmlite Maintainer: LLVM Packaging Team Installed-Size: 734 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Suggests: python-llvmlite, python3-llvmlite Filename: pool-stretch/llvmlite/llvmlite-doc_0.26.0-1~bpo9+0_all.deb Size: 98266 MD5sum: 80bab10e21aae7ac43c87f65d436d48d SHA1: ff238d4a7065653fc18d941869494a5ad884556b SHA256: 84f842e7acabbb5f7a9e4db39515e83fe692585af69f059e575cf7855d9d4653 SHA512: 522555ec9ce40cdff9c2bb9a2352aa119f7fa0c7571a5e45d06f1616017287658997bed2e5b2930654f3f2f3794698cf91ac2d982b39ba1dedc13aa3d8d8f01f Homepage: http://llvmlite.pydata.org/ Description: LLVM Python binding for writing JIT compilers (docs) llvmlite uses the LLVM library for JIT (just-in-time) compilation of Python code into native machine instructions during runtime. 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Package: libmkldnn-docs Architecture: all Version: 0.17.2-1+Debian.stretch.9.6 Priority: optional Section: doc Source: mkl-dnn (0.17.2-1) Maintainer: Adam Cecile Installed-Size: 11064 Suggests: libmkldnn-dev (= 0.17.2-1+Debian.stretch.9.6) Filename: pool-stretch/mkl-dnn/libmkldnn-docs_0.17.2-1+Debian.stretch.9.6_all.deb Size: 2180806 MD5sum: 04db6cdc23a1095380d4e6bd99c529f1 SHA1: 83a1ddabad21ce5e58f2221454abb0f28cd3b7fc SHA256: 59450aa4f9192478a0224347a4dfe1b5e41a975e2ddaafa42f85c6044a8b3ae8 SHA512: 90e39f0e0e098cb7d31b9890a89b5b2f0252bd570e7a464a0355ee214b09235844a3937f769a75b6478d05124cec47208bedd6acd1d9e42262b75aeb891f0c50 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. . 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Package: python-mxnet Architecture: all Version: 0.12.0+debian-1+Debian.stretch.9.1 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.stretch.9.1) | libmxnet-mkl-dnn (= 0.12.0+debian-1+Debian.stretch.9.1) | libmxnet-cuda (= 0.12.0+debian-1+Debian.stretch.9.1) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-1+Debian.stretch.9.1) | libmxnet (= 0.12.0+debian-1+Debian.stretch.9.1) Recommends: python-opencv, python-graphviz Filename: pool-stretch/mxnet/python-mxnet_0.12.0+debian-1+Debian.stretch.9.1_all.deb Size: 212404 MD5sum: 5587ee5eeacbbc7da70fe5964ca835bb SHA1: 5ddbbc9681f338c2c3cfdf9c685121c31d781907 SHA256: f1143c1b87cac993dab5ed35acd3c8cb2807e279c560f017626e96dfdc6e3f9a SHA512: bc02dca23ab434c6f5f199f1139862749a33097cd6476b69f18dacf2fc6228e7d9fed3f07121377664f8c4361700e655fa006448724873559bcc1dc46532e09a 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. . 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Package: python3-mxnet Architecture: all Version: 0.12.0+debian-1+Debian.stretch.9.1 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.stretch.9.1) | libmxnet-mkl-dnn (= 0.12.0+debian-1+Debian.stretch.9.1) | libmxnet-cuda (= 0.12.0+debian-1+Debian.stretch.9.1) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-1+Debian.stretch.9.1) | libmxnet (= 0.12.0+debian-1+Debian.stretch.9.1) Recommends: python3-opencv, python3-graphviz Filename: pool-stretch/mxnet/python3-mxnet_0.12.0+debian-1+Debian.stretch.9.1_all.deb Size: 212504 MD5sum: 4a8f1288296812a57da5fc0cf5e75a6e SHA1: 939c0656943f7a6139edab6f768594bd2a2132d4 SHA256: dbf75106998d05ad320bc3e0c03f2019897b63e92d0e570f1976647a1b1e40d0 SHA512: 9afd1df5eac0a555e298ef601184ce2fd4dab5e5f3b06e668a23b8857f6ca73555036233b91bfd02de7e5c5cfb64eecc9da7039a80723058b935e3b3e29af1aa 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. . 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Package: python3-mxnet Architecture: all Version: 0.12.0+debian-2+Debian.stretch.9.1 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.stretch.9.1) | libmxnet-mkl-dnn (= 0.12.0+debian-2+Debian.stretch.9.1) | libmxnet-cuda (= 0.12.0+debian-2+Debian.stretch.9.1) | libmxnet-cuda-mkl-dnn (= 0.12.0+debian-2+Debian.stretch.9.1) | libmxnet (= 0.12.0+debian-2+Debian.stretch.9.1) Recommends: python3-opencv, python3-graphviz Filename: pool-stretch/mxnet/python3-mxnet_0.12.0+debian-2+Debian.stretch.9.1_all.deb Size: 212682 MD5sum: b66abb2646a30babf7f963487adf17cb SHA1: 4f9e5b10477a762cdc412cb05103b1b23800a3ef SHA256: c2540a1b9004930d55a7d31151c6a4e01d7c31d98785663d89d75944ef69b904 SHA512: 5af06c3f08ef36e034b21f99fced6d577f48fa81e98e9b2aa947520f6b8fc98bdc6f06b94526ddbbdac4f43c1e96e154306776460df3231602bfac4d3f33647b 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.stretch.9.3 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.stretch.9.3) | libmxnet-mkl-dnn (= 1.0.0+debian-1+Debian.stretch.9.3) | libmxnet-cuda (= 1.0.0+debian-1+Debian.stretch.9.3) | libmxnet-cuda-mkl-dnn (= 1.0.0+debian-1+Debian.stretch.9.3) | libmxnet (= 1.0.0+debian-1+Debian.stretch.9.3) Recommends: python3-opencv, python3-graphviz Filename: pool-stretch/mxnet/python3-mxnet_1.0.0+debian-1+Debian.stretch.9.3_all.deb Size: 221294 MD5sum: b353fb5fae2d3493c115a0e946c4891a SHA1: c77f6885a40c617ffadb978ee4ef83ef9d5bb80e SHA256: c87a763f9ad91a08247ebe680f33f637370071b70306b26c87137d1427bc6f5a SHA512: 8e646fc656e7786a5d70007d34df0b306b81297062ba3c15beb37221307ad712cf1af28334c00bc9153b6d2debc64be1700d06080066d047585fcd83ba2caa5c 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-stretch/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-stretch/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-stretch/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-stretch/nnpack/ninja-syntax/python-ninja-syntax_1.7.2-1_all.deb Size: 5334 MD5sum: 1e365ae99e339644dfa7ddc6d3b6ea6f SHA1: 2d1df2468085e06511acc0d614e505b821f632fb SHA256: 39fbb9d6bdf2931ccf9a8a741d0b449f30e4ab3b4e280e06374eab12c1a76eeb SHA512: c3c49b34453d8118bd491a17e20445bd7657e4871507fc6ba422bd0e1dc6ab5dc317b3959f4fda9b9d103a94d4bb8f82e1d4bb145cb9af707602f453263c1821 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-stretch/nnpack/ninja-syntax/python3-ninja-syntax_1.7.2-1_all.deb Size: 5416 MD5sum: f7ce886fb7c7c3731a0ff09db86628a3 SHA1: af689cd144b91f5ed3e019f642e1d2b68c95fc01 SHA256: ea238b06eb4ff720e51578a0d8363bad23735c45d8d3573fb8758ae0355cccc9 SHA512: 37ef8a4c3520d7c1c37a61dac837205cbb6db7f7dbb6c88bb4f50b6dc51681ad9b21cdedf17ba01509dc1eab3107c033663fecf2efd1688d639c3bb153c074da 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-stretch/nnpack/opcodes/python-opcodes-doc_0.3.14-1_all.deb Size: 428096 MD5sum: 621b285d0359ded7ca72c3e149bb6678 SHA1: d871c2eafa5764fc234743ed3a5a19df7904e9ee SHA256: 15d3f794c6d51b5eb4b9910acafbb8098a2d4ceea3ac126217ced8eec9237716 SHA512: bee700280524246a59ccf47fc169e1a04315d4455f36bdbcd3bc2c5ebf609ec314b0bee5c76d7cf0c9931fd9ce37b2ceb8c904881ecee51287c11aeb2393bf03 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-stretch/nnpack/opcodes/python-opcodes_0.3.14-1_all.deb Size: 148458 MD5sum: 54acb7c5ff6849bd7ae9057fc6826368 SHA1: 1c103c16c3bb9ea52c5ff1a6486e2c51497dedf3 SHA256: 5d8b7a602716ab5ff779ce9268bf695a4c269031b7805ecf2cb818c58120d385 SHA512: 13111e3cc7052a0572a7f2bc391ef53260933d5a259fb876f4f06abc7e10cff1ef4ef02a5ae911f77e235ed8df29bb8c1dd1e597b13d021c8cdf4bf690a2bc76 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-stretch/nnpack/opcodes/python3-opcodes_0.3.14-1_all.deb Size: 148554 MD5sum: b72ea22f7d05b46f994581a7103845aa SHA1: 46b0cea0d8955f7152a97f6b016186205ec1b997 SHA256: 6cd0747cf16d8664cb4d61eb5e1125df7bb57af097a9451e8ef43b880a2d81e1 SHA512: 4ce7181a7d56df5ed22c840be71b3aaab895617b16ac7e80ed0a03ee67aa4dc6d89203c3c267bca3018a8bbd46bc32f5b0976959bdd88a9df0cd166db20a8207 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-stretch/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-stretch/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-stretch/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-stretch/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-stretch/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-stretch/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: numba-doc Architecture: all Version: 0.34.0-3~bpo9+0 Priority: optional Section: doc Source: numba Maintainer: Debian Science Maintainers Installed-Size: 4147 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Recommends: python-numba, python3-numba Filename: pool-stretch/numba/numba-doc_0.34.0-3~bpo9+0_all.deb Size: 641756 MD5sum: d0f108c397ae7d46ccb19bc2b13ddae9 SHA1: faa070c729995b45856f816f8c89189c4df6b147 SHA256: 11939f6d116c92f3851cc5419017ab407d1db10c59af7e24dd15d924f9a59835 SHA512: ea875a4be6f00e7de5f4f097585b9fbd35b1127f599c870cff3beb518eaa68abc98f381e2cf60ee5ed17bd002b4f0e2bec91c51964fda23c0853ae740e65f86f Homepage: http://numba.pydata.org/ Description: native machine code compiler for Python (docs) Numba compiles native machine code instructions from Python programs at runtime using the LLVM compiler infrastructure. Just-in-time compilation with Numba could be easily employed by decorating individual computation intensive functions in the Python code. Numba could significantly speed up the performance of computations, and optionally supports compilation to run on GPU processors through Nvidia's CUDA platform. It integrates well with the Python scientific software stack, and especially recognizes Numpy arrays. . This package contains the documentation and examples. 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-stretch/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: libopencv3.2-java Architecture: all Version: 1:3.2.0+dfsg-4.1+Debian.stretch.9.1 Priority: optional Section: java Source: opencv (1:3.2.0+dfsg-4.1) Maintainer: Debian Science Team Installed-Size: 438 Depends: libopencv3.2-jni (>= 1:3.2.0+dfsg-4.1+Debian.stretch.9.1) Breaks: libopencv2.4-java Replaces: libopencv2.4-java Filename: pool-stretch/opencv/libopencv3.2-java_3.2.0+dfsg-4.1+Debian.stretch.9.1_all.deb Size: 399106 MD5sum: c0485d022b1d511549eeb68af3aa0edf SHA1: 2b5447567453a2198c5f385669711ffcd4551b17 SHA256: af87d7e097a8a17d0c54f7ca7934d6a59a98406ec5276fed00ae345b367e913d SHA512: bd1fe1acd6436be06626217dfdfb4405a2223ba64b029b0e66e53e98b2fe99db7a4e901b804acd0fec24f2bbf2ea315c0bcad84e7f392754b700f5d76cbf4649 Homepage: https://opencv.org Description: Java bindings for the computer vision library This package contains Java bindings for the OpenCV (Open Computer Vision) library. . The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance. . OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction. Package: opencv-data Architecture: all Version: 1:3.2.0+dfsg-4.1+Debian.stretch.9.1 Priority: optional Section: libdevel Source: opencv (1:3.2.0+dfsg-4.1) Maintainer: Debian Science Team Installed-Size: 9848 Breaks: libopencv-dev (<= 2.3.1-12) Filename: pool-stretch/opencv/opencv-data_3.2.0+dfsg-4.1+Debian.stretch.9.1_all.deb Size: 1207804 MD5sum: ae0493bdd9412ed446d5391257de6d35 SHA1: ad944862ccbbef1aef2e8c6ad5258a74d4394ce7 SHA256: b5fca6118d15374da2ee7e559e6f947048fa9b15992a1bf003e3519a50822a99 SHA512: 217fa0ef39c20537d8d6c77379717e93b46fbfeebfe118d52da2a8f9002d49eaac95be160c523bcb611a4dd0398eb6a6afb66c6eddb1af55786fbba2b1ead954 Homepage: https://opencv.org Description: development data for opencv This package contains some architecture independent files useful for development with OpenCV. . The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance. . OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction. Package: opencv-doc Architecture: all Version: 1:3.2.0+dfsg-4.1+Debian.stretch.9.1 Priority: optional Section: doc Source: opencv (1:3.2.0+dfsg-4.1) Maintainer: Debian Science Team Installed-Size: 174172 Depends: libjs-jquery, libjs-mathjax Conflicts: libopencv-doc Replaces: libopencv-doc Filename: pool-stretch/opencv/opencv-doc_3.2.0+dfsg-4.1+Debian.stretch.9.1_all.deb Size: 59836226 MD5sum: 7b642b25bb17c0c26655464958229a0a SHA1: 934a06010d1c579ff97d8ffdc54549ac7c57a08d SHA256: c45c6158551a12591b9eeafcf8a7ff3b3b93ea5c8de8c9599b9defb253163625 SHA512: f4b3ef4598134163c6ba6aaba690fad2ddc27eb82dff0bbb42cc60cb4f79a6db20d7a2ef6abad202ff8b530c2f448db873eb0320282d4c9520388f4dac896178 Homepage: https://opencv.org Description: OpenCV documentation and examples This package contains the OpenCV documentation and example programs. . The Open Computer Vision Library is a collection of algorithms and sample code for various computer vision problems. The library is compatible with IPL (Intel's Image Processing Library) and, if available, can use IPP (Intel's Integrated Performance Primitives) for better performance. . OpenCV provides low level portable data types and operators, and a set of high level functionalities for video acquisition, image processing and analysis, structural analysis, motion analysis and object tracking, object recognition, camera calibration and 3D reconstruction. Package: libprotobuf-java Architecture: all Version: 3.6.1-4~bpo9+1 Priority: optional Section: java Source: protobuf Maintainer: Laszlo Boszormenyi (GCS) Installed-Size: 1059 Filename: pool-stretch/protobuf/libprotobuf-java_3.6.1-4~bpo9+1_all.deb Size: 970986 MD5sum: a8f690c6acec3f214384909a9ecc091f SHA1: e29f740f1a7231adc2381e6bac126c9a33439b0c SHA256: 765b1c0c29d3e01ddb206a1371b07e46b812aa227dd9bb87aec3f7cdceb34c36 SHA512: 45b248451fcf75de0d9464ccea3c28479bc8c8da1616a47b5fda5025a368e404c2bdaafcb58147e7fa15e9520e4c44f2b852efc5639d068eb0125e5876fa0831 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. Package: libprotobuf-java Architecture: all Version: 3.6.1.3-2~bpo+Debian.stretch.9.8 Multi-Arch: foreign Priority: optional Section: java Source: protobuf (3.6.1.3-2~bpo) Maintainer: Laszlo Boszormenyi (GCS) Installed-Size: 1059 Filename: pool-stretch/protobuf/libprotobuf-java_3.6.1.3-2~bpo+Debian.stretch.9.8_all.deb Size: 971296 MD5sum: 5844a77430598b258d94779245a8b251 SHA1: 0391b1355debd6a17993d84627171639a50d7e86 SHA256: 0f588fc746ab7ae56ea76188c4cc218a4e0e86766a8e52256d882c687894a6db SHA512: e4317454b0511095fda042c77ce6ff08f6c08f5b9a71926cc745dbde0fc7253cd67be1a35eb4ffe3bfeee67e989fc7e57c23f6b2a136789a0e2c14567afe8bd2 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. Package: python-pyglet Architecture: all Version: 1.3.0-1.1~0+Debian.stretch.9.7 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 (>= 2.7.5-5~) Recommends: libasound2 | libopenal1 Filename: pool-stretch/pyglet/python-pyglet_1.3.0-1.1~0+Debian.stretch.9.7_all.deb Size: 1436602 MD5sum: 9bdaa9aea2d8cd7cf7b21a483700a96e SHA1: e75ec2414c3a18308ba4ce54534226d3f8ad326b SHA256: 22a21760f4127ca37e4ed5bbcf3a480245f69530583195c83c04da27cc287919 SHA512: 0c6a62545448ca1b760da021db60cdf980699fce4338ab29015e0d6b19b99d397e22b8ae1c1fe0bb6e02a532ce0b5bc10d6900dac427515630ab7eb625b38506 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.stretch.9.7 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-stretch/pyglet/python3-pyglet_1.3.0-1.1~0+Debian.stretch.9.7_all.deb Size: 1435694 MD5sum: 7a3d0339e67b9fb68438ffd18e9fd4ef SHA1: 1c8238c81bbfe3612ba24de23e59b6f0c9b9be27 SHA256: 05e2570f9e2823a6cb37eb413bd3bb17d15b62161593d2d6f9d10ad61310b991 SHA512: 4ca32f09014a33dffca11ca4de823cb0a3930b0808d4806639f9596062d39c28dca7f28d6cb9c23036ec05744e288e378408338059a401cf6d1c540b641d6027 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.stretch.9.5 Priority: optional Section: doc Source: astor (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 148 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/python-astor/python-astor-doc_0.7.1-1+Debian.stretch.9.5_all.deb Size: 39050 MD5sum: d08a0c49aa78aa6cf0086062564f201f SHA1: dd538f38840ae47b65e980ac4839a73fe91bb5c7 SHA256: 2f68fd4eabd7cc7fc2cc424b271b36815ccc2f16f89286774e195994b1a0e822 SHA512: 2cff05992d601eebaaa309ccf73a5c751a2a5ac0b95ab34e6b831bc2ac9ccf816d55ae2488e2150f81e5fd1b40b49ac339ef4af08b7357c9fe2c3d792f28310b 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.stretch.9.8 Priority: optional Section: doc Source: astor (0.7.1-2) Maintainer: Adam Cecile Installed-Size: 149 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/python-astor/python-astor-doc_0.7.1-2+Debian.stretch.9.8_all.deb Size: 39464 MD5sum: e1c18d678edd9f00205d4059120f99bc SHA1: 3898a56bb76d8251935406ae51a273f2163d018a SHA256: b70db6089fce31c7e9a54c41f7a22685effc1e62143ee4d14ca33fa11b68fbac SHA512: 572b126a2787eee88a1f3813390d1e1e5491251ec7b3063dce599c02644a5b9aa371c244221d02dd2a87623d7aeab226f01d923054821f4338d7b46e2baabc1e 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.stretch.9.5 Priority: optional Section: python Source: astor (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 8843 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-astor-doc Filename: pool-stretch/python-astor/python-astor_0.7.1-1+Debian.stretch.9.5_all.deb Size: 1507916 MD5sum: cfdb6b356467f2824533f3e2585f34e0 SHA1: 2cc0224c5af502c28c2b355799a45946a5cf7a30 SHA256: b93a92eca617cf7015e97e8ffb9aa7882ee156cf1ef5c74d51dab91ab89de46b SHA512: f80902d7fb4cf070316868295aadd719ccd490931ff12fad48abf1f73ff7774dbe93a58d622a62eb01bf87fd8ffe5a3bb4c4173bf10a6a8474172915c9975544 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.stretch.9.8 Priority: optional Section: python Source: astor (0.7.1-2) Maintainer: Adam Cecile Installed-Size: 98 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-astor-doc Filename: pool-stretch/python-astor/python-astor_0.7.1-2+Debian.stretch.9.8_all.deb Size: 25242 MD5sum: 608fe6699ea900fa7d493c21332eaecf SHA1: 3c413d6c7fc1eab8009721af9d20a42e4e77515f SHA256: c8cadd600f4f80004753a65d2bf17d9d9c098d6e81b9d2e2cf8f902a741544bc SHA512: befa4450fdcf8fdf54cd7659223e81df41176e4974c369b5abca73e6c7d4f15d1cb1c9264ab84266ed37f0ad314f35157e238f1ed5c5bb8b27767340f8871170 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.stretch.9.5 Priority: optional Section: python Source: astor (0.7.1-1) Maintainer: Adam Cecile Installed-Size: 8411 Depends: python3:any (>= 3.3.2-2~) Suggests: python-astor-doc Filename: pool-stretch/python-astor/python3-astor_0.7.1-1+Debian.stretch.9.5_all.deb Size: 1474090 MD5sum: 0acd8843bc19fcc0ddef39eb5d7218c9 SHA1: bff99e91143fe75fdad3c6a559bf1b795a9e713b SHA256: dd9342cfb923f967384a65456f30b32f8539c14feacb69793a7e6ae7ffa40246 SHA512: 27d245d524b9aa7a2f7ee063a9f40d03827f0a0f29bc7161af5f75bce35e7649ae053bdc71747587b89bdb1033f4c32b236a5c4630c54e0539a0754cafcaf519 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.stretch.9.8 Priority: optional Section: python Source: astor (0.7.1-2) Maintainer: Adam Cecile Installed-Size: 98 Depends: python3:any (>= 3.3.2-2~) Suggests: python-astor-doc Filename: pool-stretch/python-astor/python3-astor_0.7.1-2+Debian.stretch.9.8_all.deb Size: 25312 MD5sum: 15d58771161a906cee43b268e55cc10e SHA1: 06107e8fc946d37008a162a5f58da98e8910edb1 SHA256: aef03fbb724fce25384e1e7e02015bf120daeeea4f5381db06a02efd0c748b4b SHA512: 699c2d7dd79c3be90ff4cc166a23d670a216797c78107b2579368020a4d35319c577c9f0a40aaa6473e6b7b46f913bbea2755ef9b145ed5dad68cec41bc3eae2 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.stretch.9.5 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 Filename: pool-stretch/python-astunparse/python-astunparse-doc_1.5.0-1+Debian.stretch.9.5_all.deb Size: 24958 MD5sum: 1c5b070f681b171356054c51b7e5b30d SHA1: e60170af8745d0e8a503a63b291c2fe65717b857 SHA256: 310f168ca3d9d09b2de18d5173ab7966f4ef718e3a147c0b7d63896e5f85e6b6 SHA512: 180f663bcb04a635c3aac265fde43101453f812b1d9a35020151e21696057641b1404da11da71f05aea1b2f5d07e80f23150719cd01b979ffe02cf836977744e 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.stretch.9.8 Priority: optional Section: doc Source: astunparse (1.6.2-1) Maintainer: Adam Cecile Installed-Size: 145 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/python-astunparse/python-astunparse-doc_1.6.2-1+Debian.stretch.9.8_all.deb Size: 25516 MD5sum: d9e7f9608ec39841a1493f012b7f6902 SHA1: 73096f5619a35ffe6ab24f29dfb2a91caf5bccd4 SHA256: 0dc0aa267b9f34ec173e6a17e3f35454a46ba53170cf7a1cb16a60f0dab7dcea SHA512: 2d95bae6acf9efd83596064ccc3cb4ed74b93655ccaa135a6e63395ae4fc2b81fe459a740cac876b0729a87cf352dd6c112b9ce763c08ffcd0a78ffb8a5127d3 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.stretch.9.5 Priority: optional Section: python Source: astunparse (1.5.0-1) Maintainer: Adam Cecile Installed-Size: 55 Depends: python-six (>= 1.6.1), python-six (<< 2.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-astunparse-doc Filename: pool-stretch/python-astunparse/python-astunparse_1.5.0-1+Debian.stretch.9.5_all.deb Size: 11568 MD5sum: a863bc215e76ef9f9d33fb3d82edd0ec SHA1: d224b06639261c9e01476c2d9baf03a3109cf3f1 SHA256: f1d8bbb94e6a53b9c9211505144ae5cbdd2a1ce85978e568b9671ab2f74b5895 SHA512: 7ccf5d6ae2fe837a4a8349b74eded22e72310a4343508b5a57f26310afa164a880d1496b8b9f76601c8606ffd4e69b603dc3e8c5aed5278435e19a56419e3a48 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.stretch.9.8 Priority: optional Section: python Source: astunparse (1.6.2-1) Maintainer: Adam Cecile Installed-Size: 58 Depends: python-six (>= 1.6.1), python-six (<< 2.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-astunparse-doc Filename: pool-stretch/python-astunparse/python-astunparse_1.6.2-1+Debian.stretch.9.8_all.deb Size: 12272 MD5sum: ad0e6147856e3594b0fb5898f83b5d80 SHA1: 3431f6ba9bd021e8a6122f4047193862c473ac89 SHA256: 5629c8641dd83372ec161040084655c60f347dbcedf6744ddec9dace1d0b11f4 SHA512: 27b45760a7dba3fc6b998c92631b6d38a404538271c6c8f9a340ed6378bf801f5b624a5b83de058aed58675792b2a345ce17e1d6b9be3f7dba543ba72b7e33cc 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.stretch.9.5 Priority: optional Section: python Source: astunparse (1.5.0-1) Maintainer: Adam Cecile Installed-Size: 55 Depends: python3-six (>= 1.6.1), python3-six (<< 2.0), python3:any (>= 3.3.2-2~) Suggests: python-astunparse-doc Filename: pool-stretch/python-astunparse/python3-astunparse_1.5.0-1+Debian.stretch.9.5_all.deb Size: 11652 MD5sum: 26404e1ff11c53883d54ebc077a9ee18 SHA1: a4f0bfafc97ef3d981b28a238cef3ecd898c5360 SHA256: 823d25b78c047b01b64af2b749955dc01e39ab46940e093329f1f14565c96d9f SHA512: 8b6da0ee3745111f14cff06cc415685439e59d6e53e143569c802a7c3c715da8d19b8ea6e4608192cd3efbf3730f5140d4994e598c51649622c4b7c5c3683378 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.stretch.9.8 Priority: optional Section: python Source: astunparse (1.6.2-1) Maintainer: Adam Cecile Installed-Size: 58 Depends: python3-six (>= 1.6.1), python3-six (<< 2.0), python3:any (>= 3.3.2-2~) Suggests: python-astunparse-doc Filename: pool-stretch/python-astunparse/python3-astunparse_1.6.2-1+Debian.stretch.9.8_all.deb Size: 12348 MD5sum: dea5cf3412b2d2f5095eab1d19850e2d SHA1: 10878165c5525e0f963e2e385f4c2b545afde45f SHA256: 85ec5306dbdd70abeee2170d227855236121fbe3b31d67a21e799d8b14d4001b SHA512: 78f330dddfab7a3c77a2bf32657510c70a386d6eeb1be2ef9f7662487a55c1fe6b7e89a911a4831257c798ed1d43f0609430132f05989efcd14a11227ae2b434 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-backports.weakref Architecture: all Version: 1.0-2~bpo+0 Priority: optional Section: python Maintainer: Debian Science Maintainers Installed-Size: 40 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-backports.weakref/python-backports.weakref_1.0-2~bpo+0_all.deb Size: 8934 MD5sum: e29426b5ec95e469305b71f68628e280 SHA1: b4cc9aff089c2e177c3571a5b1d2ee045d4da76c SHA256: 38f70bd0815da7a90da3ef9e2dea87e72ad5552d902c38a0cee364837082cbc5 SHA512: f2ff5b82cea16a624f5b050fa38f52d6f29fa1664555a46088b4873724cee4b7ae547909ecb4369c75b5af5f8215c359d71b92c86d21cac85cd2039608c762b2 Homepage: https://github.com/pjdelport/backports.weakref Description: backports of new features in Python 2 weakref module This package provides backports of new features in Python's weakref module under the backports namespace. . This package provides the Python 2 version of the module. Package: python3-backports.weakref Architecture: all Version: 1.0-2~bpo+0 Priority: optional Section: python Source: python-backports.weakref Maintainer: Debian Science Maintainers Installed-Size: 37 Depends: python3:any (>= 3.3.2-2~) Filename: pool-stretch/python-backports.weakref/python3-backports.weakref_1.0-2~bpo+0_all.deb Size: 8948 MD5sum: 7f27ccdb47bea456a0b25f59ad4173f0 SHA1: db59b244d8c199d28f9c17e6593a0bfcb86f2881 SHA256: 375cdca956c2c1c7b94a3c83e4cb3716c985024fb88db5f8d3f6b0ca5beb0aa1 SHA512: 72fee6b65323b9d01365e880e9f3ed109b363279d5ed345c47f22b981eea9cfca09f22e6b8107e06fb254ed2072cf9bd9645d514111ae9d9ea22ee11940db4fe Homepage: https://github.com/pjdelport/backports.weakref Description: backports of new features in Python 3 weakref module This package provides backports of new features in Python's weakref module under the backports namespace. . This package provides the Python 3 version of the module. Package: python-bayes-opt-doc Architecture: all Version: 1.0.1-0+Debian.stretch.9.9 Priority: optional Section: doc Source: python-bayesian-optimization (1.0.1-0) Maintainer: Adam Cecile Installed-Size: 16975 Filename: pool-stretch/python-bayesian-optimization/python-bayes-opt-doc_1.0.1-0+Debian.stretch.9.9_all.deb Size: 17000486 MD5sum: 1422a2031ad28c89637b1535e2b6eeb4 SHA1: 5891e4281c9cfa1841a46a263d4495da79905fd7 SHA256: 36e95452bab6992d9df02f7c80968e837818cacd1735daa185ff3a2fe238da41 SHA512: 1217a3352456f5ee6be0bafa06a1fcb5fe9aeb41cbc9a85a60e4d01ba90effec8fbd1e9741a7456a2bef732fd5da8d970ec1b88858028f46e3816e99180ba1a7 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.stretch.9.9 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-stretch/python-bayesian-optimization/python3-bayes-opt_1.0.1-0+Debian.stretch.9.9_all.deb Size: 10852 MD5sum: af79bafaeba89fa9892b096c7959a683 SHA1: cbcf8d49444e9141132337523def3a6ae65bd8e4 SHA256: abf49a29977878fa9ae963c871f49579c416c4881d23ec73c360e7e21373f8d5 SHA512: bde1ba9e1862baa59296c98f1980785be6a60fc58baf2b55afbe8824cdaef48b65286fec553b50ae0cb9b5b8039bf4e8a87a5d9e211e3088a6c0e6011bde319b 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.stretch.9.7 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-stretch/python-box2d/python-box2d-doc_2.3.2~dfsg-2.1~0+Debian.stretch.9.7_all.deb Size: 689106 MD5sum: 5724120798a0fa0138036c51ee97391d SHA1: 70ef01a85ed10ef05ff8bb110333c0b8a8d8acaf SHA256: b754a03e5a86b508d0caa1cf3e4840e28152801812b92380112ab8afcfcbd836 SHA512: e77e8e2f1c95f99b2862deedb3a06a3a8a2218cd639f71100b8b48a0ec4840b4beeeff7990fe72764cec4a1d13c3fbbf560552ae447d13bd762401eebce7efe1 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-concurrent.futures Architecture: all Version: 3.2.0-2~bpo9+0 Priority: optional Section: python Maintainer: Debian Python Modules Team Installed-Size: 210 Provides: python-futures Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-sphinxdoc (>= 1.0) Filename: pool-stretch/python-concurrent.futures/python-concurrent.futures_3.2.0-2~bpo9+0_all.deb Size: 39990 MD5sum: a522f6485c6c9b2dbfd6da9300e910d3 SHA1: 84fb9b6c9fc4ad1dcf9f23a00fc533b766913f97 SHA256: 1a9359abd621f2ad8fb7eb09632f913a2dff71e593f5942a112032bba92edf23 SHA512: 663b2238fc38c3a8198fcf2f1b81005162c03d8c10163f47737556e599abd50562ed50d35e137847ea4bd82baa7b6512802adb6418e6d83448ef9cf557fb46e2 Homepage: https://github.com/agronholm/pythonfutures Description: backport of concurrent.futures package from Python 3.2 The concurrent.futures module provides a high-level interface for asynchronously executing callables. . This is a backport for concurrent.futures as of PEP-3148 and included in Python 3.2 Package: python-gast Architecture: all Version: 0.2.0-1+Debian.stretch.9.5 Priority: optional Section: python Source: gast (0.2.0-1) Maintainer: Adam Cecile Installed-Size: 46 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-gast/python-gast_0.2.0-1+Debian.stretch.9.5_all.deb Size: 8100 MD5sum: 31646a5abf772781815e634e2e46c4a5 SHA1: f0adde93c538b78db0fa574b5dd875ec3d4ae508 SHA256: 9c58b0d441f38c092ad88e7e597330ef613a2ac95ac93c27a4981e919cffbd68 SHA512: 193aaaf299c4681b9dbdbdb1b2adeb96b9f8a836205727c9f13c1f101ab193357a428dca5f5e8a35d532d223ff929a61fbc4b16fd3dac29ef0c0ca39468ec572 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.stretch.9.8 Priority: optional Section: python Source: gast (0.2.2-1) Maintainer: Adam Cecile Installed-Size: 45 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-gast/python-gast_0.2.2-1+Debian.stretch.9.8_all.deb Size: 8034 MD5sum: f6d2cfac297abd90180b172cb618b86c SHA1: a296f8d931e3d62e8d02bc89a4ee5f886e3a1412 SHA256: 4fe532c3d4a4ddf2bda7003344a1cd8a31cea4532116cc7f5b8714dc9cb84c72 SHA512: 8a724791fb6fabb00efbfb2380e83b67058f64e4a80fa59ebf06aac6610ff3258053885390efc3983c529b9cecba2f298002f27acbf3ea5c27f0a2514e9dff4a 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.stretch.9.5 Priority: optional Section: python Source: gast (0.2.0-1) Maintainer: Adam Cecile Installed-Size: 46 Depends: python3:any (>= 3.3.2-2~) Filename: pool-stretch/python-gast/python3-gast_0.2.0-1+Debian.stretch.9.5_all.deb Size: 8166 MD5sum: b47198be4fb95f62659cd063095ce0a8 SHA1: d9a7c11f7627cc2fb602baab1f34f20713f1c450 SHA256: 4cd2387ff926564cf8d207f2d8d6d5b5dadd5136846976d9bfdd728e9c05b61b SHA512: d37bf453256cfe8a47d1e431e6c943b333e8a520fd2eaa2eced38d1d4a3c08a553692af7d42420a231f815283bba92a27d3b4c86920f6c98175302cc3484da0f 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.stretch.9.8 Priority: optional Section: python Source: gast (0.2.2-1) Maintainer: Adam Cecile Installed-Size: 45 Depends: python3:any (>= 3.3.2-2~) Filename: pool-stretch/python-gast/python3-gast_0.2.2-1+Debian.stretch.9.8_all.deb Size: 8114 MD5sum: 7cb78a82e341b9dde00e119c63f6d734 SHA1: 9817bdcb16c3dd365132f091d3412641295940be SHA256: 127b3f74d6ec53849fea57671f3f0182142d90b198325bee63da15dbf79af667 SHA512: 3dd38437e259d302f97b3a56232ab2c67464243234eecf537c4bc27f41bfdbdfcdd100c590ab8d402805f9b7da32e93c5c85fe77d551f13870cea7165ddddcf1 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.stretch.9.5 Priority: optional Section: doc Source: python-grpcio (1.14.1-1) Maintainer: Adam Cecile Installed-Size: 1174 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/python-grpcio/python-grpcio-doc_1.14.1-1+Debian.stretch.9.5_all.deb Size: 80304 MD5sum: 5a0b491a5c37a03efc0b158ecc6f33ca SHA1: afd473d687e1cd0d32a875035ce44cef72696891 SHA256: e2fd1de387d01cbaa9503b9f227808dcba274a4ed3e632b8a8dca18a455b0707 SHA512: 671f111a484061fab00d6fec135b1917e012af44218994b4b65cbfbb3da9f948eabb3067b9870c1c03ef734e51b9c3be773bd61eec2d086646df15b7a1edac6c 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. Package: python-grpcio-doc Architecture: all Version: 1.20.0-1+Debian.stretch.9.8 Priority: optional Section: doc Source: python-grpcio (1.20.0-1) Maintainer: Adam Cecile Installed-Size: 1192 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/python-grpcio/python-grpcio-doc_1.20.0-1+Debian.stretch.9.8_all.deb Size: 81878 MD5sum: bf4ab5841f15d2fe63bb41b53b76d95a SHA1: f8a4fd64e92b39faadbac268d365dfbcc0645996 SHA256: 92be84d14ea86d9509aad0c3912e9a5f1a8b022af56b08cd475577c9007aaed1 SHA512: c521c69b1854530f442be14b5816021fbe3edcb60a041280d06b6001301dfe2ddae526b713209b9c8a4119d146584c012470919726f8368efc90968b3baceee4 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. Package: python-importlib-resources-doc Architecture: all Version: 1.0.2-0+Debian.stretch.9.9 Priority: optional Section: doc Source: python-importlib-resources (1.0.2-0) Maintainer: Adam Cecile Installed-Size: 156 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-stretch/python-importlib-resources/python-importlib-resources-doc_1.0.2-0+Debian.stretch.9.9_all.deb Size: 36530 MD5sum: a8d8d2460afb63c62a3f6b1406003c69 SHA1: 737f8d2d29f99c6f0d14710823dd8280dc99425c SHA256: 58b6d5cfb808d170f7390961b3dfcb15997fba55b6116621339f05495937e940 SHA512: b16203fc08a769c424d6d7f64a88c2e9ae209e51896a0cd23ad8cff58ac44d8ea6cdb04e6368466cc1bb79edbf8a3dc1af95ab19e79650a736e7a6d5454c16a7 Homepage: https://importlib-resources.readthedocs.io Description: Backport of Python 3.7's stdlib importlib.resources (common documentation) Backport of Python 3.7’s standard library importlib.resources module for Python 2.7, and 3.4 through 3.6. 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 is the common documentation package. Package: python-importlib-resources Architecture: all Version: 1.0.2-0+Debian.stretch.9.9 Priority: optional Section: python Source: python-importlib-resources (1.0.2-0) Maintainer: Adam Cecile Installed-Size: 38 Depends: python-pathlib2, python-typing, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-importlib-resources-doc Filename: pool-stretch/python-importlib-resources/python-importlib-resources_1.0.2-0+Debian.stretch.9.9_all.deb Size: 8272 MD5sum: e70d5d4f63f6bee75812a6ddf3ba69da SHA1: a586c772883e7d5011861d8684cf2c8a3fcbbb4b SHA256: 846c28db6a0d76abd493cc89ba7624b1793f9216b6c5272cfb4947fd16c83fd1 SHA512: 9995d2727f5d78b9a7328e3d300e247f182c6757e7099ace9bae361333b3bc4808b41e0e1249ff9e88facc15d4ce2009c7a05fa00da19e1f175eac3589b866b0 Homepage: https://importlib-resources.readthedocs.io Description: Backport of Python 3.7's stdlib importlib.resources (Python 2) Backport of Python 3.7’s standard library importlib.resources module for Python 2.7, and 3.4 through 3.6. 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 2. Package: python3-importlib-resources Architecture: all Version: 1.0.2-0+Debian.stretch.9.9 Priority: optional Section: python Source: python-importlib-resources (1.0.2-0) Maintainer: Adam Cecile Installed-Size: 40 Depends: python3:any (>= 3.4~), python3-typing | python3 (>= 3.5) Suggests: python-importlib-resources-doc Filename: pool-stretch/python-importlib-resources/python3-importlib-resources_1.0.2-0+Debian.stretch.9.9_all.deb Size: 8718 MD5sum: 9a57cb7a163f5eae0bed2f907bddf0c2 SHA1: 25d5efc097919429dea687dfbf53a48a761de5eb SHA256: 126cbb8e303c030d2bfc4f8cb1123364ab938f369824e5064cb8f61ee93bfbe4 SHA512: 1f0d184f2c70a58730d73ad8d70197866b2335881418be53abe785ee2d203e5613cba9ab5e5586decd12c8d63ddaa1b6774fee0b72b6e481dbe653ec667ec661 Homepage: https://importlib-resources.readthedocs.io Description: Backport of Python 3.7's stdlib importlib.resources (Python 3) Backport of Python 3.7’s standard library importlib.resources module for Python 2.7, and 3.4 through 3.6. 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.stretch.9.6 Priority: optional Section: doc Source: onnx-tensorflow (1.2.0-1) Maintainer: Adam Cecile Installed-Size: 17 Filename: pool-stretch/python-onnx-tf/python-onnx-tf-doc_1.2.0-1+Debian.stretch.9.6_all.deb Size: 5528 MD5sum: 64a36b14ac25517ae972b61cfc722747 SHA1: 5240c0b61c378a3ac19fbe891449d0eb37fceada SHA256: 8bdb7a021007f55899ff788cb3f593e8625cef298397f5579d052ecce6e25f40 SHA512: 98eca4770b43ab282ec52140d3e702807c8892e2f988fbb30c88f2298d7a0e6ce36807eac5503c6ac949f348a2cda40efa4d86485c64a50bd492feb961ab64a3 Homepage: https://github.com/onnx/onnx-tensorflow Description: Tensorflow Backend and Frontend for ONNX (common documentation) Convert models between Tensorflow and ONNX. . This is the common documentation package. Package: python-onnx-tf Architecture: all Version: 1.2.0-1+Debian.stretch.9.6 Priority: optional Section: python Source: onnx-tensorflow (1.2.0-1) Maintainer: Adam Cecile Installed-Size: 344 Depends: python-onnx, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-onnx-tf-doc Filename: pool-stretch/python-onnx-tf/python-onnx-tf_1.2.0-1+Debian.stretch.9.6_all.deb Size: 43872 MD5sum: 7c045d33e10dfe2d47d690638d307e53 SHA1: 63c8f5900435cdc30a5f1215872ba501b9ecde8d SHA256: 006c68c4dd2863458933872eb2f98114ef2bb014d6e0179b25cd0c90f35ce92d SHA512: 0c6d87d362cfd9b73747cd2d2186fc5bb7f796d4d3504db612be3363d39daa1015fd626f52ae14ec6b18d7654ace1a6a36323a4cb358cf976970248fd85c7c16 Homepage: https://github.com/onnx/onnx-tensorflow Description: Tensorflow Backend and Frontend for ONNX (Python 2) Convert models between Tensorflow and ONNX. . This package installs the library for Python 2. Package: python3-onnx-tf Architecture: all Version: 1.2.0-1+Debian.stretch.9.6 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-stretch/python-onnx-tf/python3-onnx-tf_1.2.0-1+Debian.stretch.9.6_all.deb Size: 43996 MD5sum: 49941d0341daf1eb9798110dc8f84a73 SHA1: 531bb2df2e4c80aa988e2898774e99c0e3ddf833 SHA256: 2a34fa3dd766854666dad1679f41970ed500d136663abf05b96ba89f739f61e5 SHA512: e66af83770d3792fa54fc0eb06d5b41f7d6a69fcdf28673c7b312255d1d3b4c86dc98f2b443217f9dd3f28562c47208206b5e1583e3078880fdc006ce6cd967b 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.stretch.9.5 Priority: optional Section: doc Source: onnx (1.3.0+debian-1) Maintainer: Adam Cecile Installed-Size: 277 Filename: pool-stretch/python-onnx/python-onnx-doc_1.3.0+debian-1+Debian.stretch.9.5_all.deb Size: 199014 MD5sum: fea10942691fde91f3d622cf37c6d6d3 SHA1: 97d1b3ad8a302cd6b72623b310e3007c5ea56f91 SHA256: 4fd41d9f5f8a34da63aab3eee50c65c7955bbbddd65b133633ecb418359ca258 SHA512: 31c67162f31f011232f5645b40e3e10966de2b68fc50d71d65b08cbbe8964b436f316e2be7901575af206669576751d27fd8bd3920fd5df0471e2b204cc5163b 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. . It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. . Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. . This is the common documentation package. Package: python-onnx-doc Architecture: all Version: 1.5.0+debian-1+Debian.stretch.9.9 Priority: optional Section: doc Source: onnx (1.5.0+debian-1) Maintainer: Adam Cecile Installed-Size: 337 Filename: pool-stretch/python-onnx/python-onnx-doc_1.5.0+debian-1+Debian.stretch.9.9_all.deb Size: 248074 MD5sum: 61766a4e0e600b85ba59e97d038b5992 SHA1: e67d9c6d4c4ca7f548ce60160196e0fb1c1a1d8b SHA256: 62372b9233a9bd639b15088e0154dd3c73ff8c4b0343e2d4c384ed8f493ce332 SHA512: 683578f3f9819792e3b7398a992d6f7b41a8576e2c9a75db86518baac23cec64cb823c95fa2cd366586aa3c9c68043ff9d7f9161f8d1e3f268b9e4d2ec4b2be0 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. . It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types. . Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are developing ONNX support. . This is the common documentation package. Package: python-parameterized Architecture: all Version: 0.6.1-2~bpo9+0 Priority: optional Section: python Maintainer: Debian Python Modules Team Installed-Size: 58 Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-parameterized/python-parameterized_0.6.1-2~bpo9+0_all.deb Size: 14212 MD5sum: 7d48aba133428bffec57db57ceb54dbe SHA1: c48832fcea8a8214e6f2b7154211a132be311ee7 SHA256: 5da52a3ef6d0871da1fedb6bc15d252ffebac81819aeaaef92e6709f60e466c2 SHA512: 6fdfb5c87e4ed3eaea0f8c3c34499f3bbe3b800a597daa79c5e4dc664bba91da4b58d14156f88ec5fea8a35fab05ea7621b2b8121f813f4a6d17badc1fb17fd6 Homepage: https://github.com/wolever/parameterized Description: parameterized testing for Python 2 The parameterized module provides a set of decorators for parameterized testing in Python. It supports nose, nose2, pytest and unittest. . The @parameterized decorator can be used for class methods, and standalone functions, whilst the @parameterized.expand should be used for classes deriving from unittest.TestCase. . This package provides the modules for Python 2. Package: python3-parameterized Architecture: all Version: 0.6.1-2~bpo9+0 Priority: optional Section: python Source: python-parameterized Maintainer: Debian Python Modules Team Installed-Size: 58 Depends: python3:any (>= 3.3.2-2~) Filename: pool-stretch/python-parameterized/python3-parameterized_0.6.1-2~bpo9+0_all.deb Size: 14292 MD5sum: ef76164a26cff2e9c12a97da94163edb SHA1: 46472efcdf7b35fbb1fd64a2f86383e15549a3d8 SHA256: 45fa39366ffb168f7dbe0431837f5c8356583b44e32e2b74196588c5ab3fa0ed SHA512: 73c886ac5ba6f62ec05c8b46b259f05044985f0947339c1a7dd45c2f860475563134b851b7f9e0ac7be07fdbbe9778e5ce1db8d3b4885f37551dd63a325fce00 Homepage: https://github.com/wolever/parameterized Description: parameterized testing for Python 3 The parameterized module provides a set of decorators for parameterized testing in Python. It supports nose, nose2, pytest and unittest. . The @parameterized decorator can be used for class methods, and standalone functions, whilst the @parameterized.expand should be used for classes deriving from unittest.TestCase. . This package provides the modules for Python 3. Package: python-pbr-doc Architecture: all Version: 4.2.0-4~bpo9+0 Priority: optional Section: doc Source: python-pbr Maintainer: Debian OpenStack Installed-Size: 2461 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-stretch/python-pbr/python-pbr-doc_4.2.0-4~bpo9+0_all.deb Size: 948164 MD5sum: 9e09daefc99e0eb6e7bcc78c289e2680 SHA1: e586b114ec6a3c40495bfaa867b2ab73060baf02 SHA256: 11d6903ed84f45bb70a8ec1870c1c88b5ae8c42556b03252e7c7785d3b4098bd SHA512: fe9b1ece6e23ef231f546ee1e6e603dcadc61df2d30288f247307ba2b2a497fb2b981ec0d711752d3ac27298e3c325604ecb6d3c95f947d4d85d7258ee86d665 Homepage: https://pypi.python.org/pypi/pbr Description: inject useful and sensible default behaviors into setuptools - doc PBR (Python Build Reasonableness) is a library that injects some useful and sensible default behaviors into your setuptools run. PBR can: * Manage version number based on git revisions and tags (Version file). * Generate AUTHORS file from git log * Generate ChangeLog from git log * Generate Sphinx autodoc stub files for your whole module * Store your dependencies in a pip requirements file * Use your README file as a long_description * Smartly find packages under your root package . PBR is only mildly configurable. The basic idea is that there's a decent way to run things and if you do, you should reap the rewards, because then it's simple and repeatable. If you want to do things differently, cool! But you've already got the power of Python at your fingertips, so you don't really need PBR. . PBR builds on top of the work that d2to1 started to provide for declarative configuration. d2to1 is itself an implementation of the ideas behind distutils2. Although distutils2 is now abandoned in favor of work towards PEP 426 and Metadata 2.0, declarative config is still a great idea and specifically important in trying to distribute setup code as a library when that library itself will alter how the setup is processed. As Metadata 2.0 and other modern Python packaging PEPs come out, PBR aims to support them as quickly as possible. . This package provides the documentation. Package: python-pbr Architecture: all Version: 4.2.0-4~bpo9+0 Priority: optional Section: python Maintainer: Debian OpenStack Installed-Size: 296 Depends: python-pkg-resources, python-setuptools, python-six, python2.7:any, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-pbr/python-pbr_4.2.0-4~bpo9+0_all.deb Size: 57480 MD5sum: 13067e295fdbc3a52a8d8284530e3885 SHA1: 8456a77d7f215d48ec50f8b1494a16040792cd36 SHA256: a0c4ae2273ba846f9eb707c414a723db760dc218499e67a4c3c8da49da8f8159 SHA512: 175c96f726ecf68c5a9874cbe4860bf518b8ebd563d5883dcfe1118f477cd2763bd6e114d285a8f2af20a72a1e2646d6e1bac6047cbed6cb42f35a4956a115b1 Homepage: https://pypi.python.org/pypi/pbr Description: inject useful and sensible default behaviors into setuptools - Python 2.x PBR (Python Build Reasonableness) is a library that injects some useful and sensible default behaviors into your setuptools run. PBR can: * Manage version number based on git revisions and tags (Version file). * Generate AUTHORS file from git log * Generate ChangeLog from git log * Generate Sphinx autodoc stub files for your whole module * Store your dependencies in a pip requirements file * Use your README file as a long_description * Smartly find packages under your root package . PBR is only mildly configurable. The basic idea is that there's a decent way to run things and if you do, you should reap the rewards, because then it's simple and repeatable. If you want to do things differently, cool! But you've already got the power of Python at your fingertips, so you don't really need PBR. . PBR builds on top of the work that d2to1 started to provide for declarative configuration. d2to1 is itself an implementation of the ideas behind distutils2. Although distutils2 is now abandoned in favor of work towards PEP 426 and Metadata 2.0, declarative config is still a great idea and specifically important in trying to distribute setup code as a library when that library itself will alter how the setup is processed. As Metadata 2.0 and other modern Python packaging PEPs come out, PBR aims to support them as quickly as possible. . This package provides support for Python 2.x. Package: python3-pbr Architecture: all Version: 4.2.0-4~bpo9+0 Priority: optional Section: python Source: python-pbr Maintainer: Debian OpenStack Installed-Size: 296 Depends: python3-pkg-resources, python3-setuptools, python3-six, python3:any (>= 3.3.2-2~) Filename: pool-stretch/python-pbr/python3-pbr_4.2.0-4~bpo9+0_all.deb Size: 57540 MD5sum: 047ab48780351964d984f9bb654e3e2b SHA1: 879159be287f95a59acec7b9505361cb953eaf87 SHA256: b6b598fb39c064eb3aa4ba277cfdb7afe598a463b1a95c954b4bd2b47625641f SHA512: 9ea8286bdd6b8e1534cff349fd5c22ff5253d85937a040f73cac325c5bfd26d65f746198b518522e64af68416954bf6e36592346175da5a887f505fe58c791e8 Homepage: https://pypi.python.org/pypi/pbr Description: inject useful and sensible default behaviors into setuptools - Python 3.x PBR (Python Build Reasonableness) is a library that injects some useful and sensible default behaviors into your setuptools run. PBR can: * Manage version number based on git revisions and tags (Version file). * Generate AUTHORS file from git log * Generate ChangeLog from git log * Generate Sphinx autodoc stub files for your whole module * Store your dependencies in a pip requirements file * Use your README file as a long_description * Smartly find packages under your root package . PBR is only mildly configurable. The basic idea is that there's a decent way to run things and if you do, you should reap the rewards, because then it's simple and repeatable. If you want to do things differently, cool! But you've already got the power of Python at your fingertips, so you don't really need PBR. . PBR builds on top of the work that d2to1 started to provide for declarative configuration. d2to1 is itself an implementation of the ideas behind distutils2. Although distutils2 is now abandoned in favor of work towards PEP 426 and Metadata 2.0, declarative config is still a great idea and specifically important in trying to distribute setup code as a library when that library itself will alter how the setup is processed. As Metadata 2.0 and other modern Python packaging PEPs come out, PBR aims to support them as quickly as possible. . This package provides support for Python 3.x. Package: python-shap-doc Architecture: all Version: 0.24.0-1+Debian.stretch.9.5 Priority: optional Section: doc Source: python-shap (0.24.0-1) Maintainer: Adam Cecile Installed-Size: 146 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Filename: pool-stretch/python-shap/python-shap-doc_0.24.0-1+Debian.stretch.9.5_all.deb Size: 54900 MD5sum: fedfaae7faadc3139817398620b8fa15 SHA1: bba2f5d0fe9e4446d7a1850577d78163a1515031 SHA256: ece14d1759a7adb7f0d8e212961ebbee753b0435864420d171842007e5e57d87 SHA512: f6282ca0ad41fbe2ff48f70a44da4da3956e8ad58f6453dabebf14728ced43b4b873314df4e21ff142f634f0bad9ff0df21bc30148235359db5b812e1165fbaa Description: Unified approach to explain machine learning model (common documentation) SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. . SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations. . This is the common documentation package. Package: python-tensorboard Architecture: all Version: 0.1.7-1+Debian-stretch-9.1 Priority: optional Section: python Source: tensorboard (0.1.7-1) Maintainer: Adam Cecile Installed-Size: 1896 Depends: python-bleach (>= 1.5.0), python-html5lib (>= 0.9999999), python-markdown (>= 2.6.8), python-numpy (>= 1.11.0), python-protobuf (>= 3.2.0), python-six (>= 1.10.0), python-werkzeug (>= 0.11.10), python-wheel (>= 0.26), python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-tensorboard/0.1.7/python-tensorboard_0.1.7-1+Debian-stretch-9.1_all.deb Size: 1573714 MD5sum: dc40a9d4ea01180939473600b7141b6e SHA1: 7fb42090e10c71fad19da1344a40c1415551eeac SHA256: ede231b282db788e92ead20615ec743bdb752adee583fd236e916eda9efcc0a6 SHA512: d5d4baf86a82ea7c1a76bec87394643dbc7197b7fdbaad4226a563af0bca412697b924428ed67448612d1a8ad77ac943a797df249ffe2cd3ca53c7bc5d47abec 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: 0.1.7-1+Debian-stretch-9.1 Priority: optional Section: python Source: tensorboard (0.1.7-1) Maintainer: Adam Cecile Installed-Size: 1897 Depends: python3-bleach (>= 1.5.0), python3-html5lib (>= 0.9999999), python3-markdown (>= 2.6.8), python3-numpy (>= 1.11.0), python3-protobuf (>= 3.2.0), python3-six (>= 1.10.0), python3-werkzeug (>= 0.11.10), python3-wheel (>= 0.26), python3:any (>= 3.4~) Filename: pool-stretch/python-tensorboard/0.1.7/python3-tensorboard_0.1.7-1+Debian-stretch-9.1_all.deb Size: 1573864 MD5sum: 2bc39ceba788ca1e00e1979552e49cb0 SHA1: d07c1a4293702038ffeb3ab7e4c940d550c0a664 SHA256: 266edff360b4beaaf73e46ff9457fb71f39f528a87925c36a1e03be04a69423d SHA512: 1bd4c271d12613a7eb93811b5fc7829f08a48731fe9c4ddd72943e941cb88207f9c598749e5909dd7b16f4cd5bc25af952fe6e18c9fe6866cda1c073d5016ba6 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: 0.1.8-1+Debian-stretch-9.1 Priority: optional Section: python Source: tensorboard (0.1.8-1) Maintainer: Adam Cecile Installed-Size: 1897 Depends: python-bleach (>= 1.5.0), python-html5lib (>= 0.9999999), python-markdown (>= 2.6.8), python-numpy (>= 1.11.0), python-protobuf (>= 3.2.0), python-six (>= 1.10.0), python-werkzeug (>= 0.11.10), python-wheel, python:any (<< 2.8), python:any (>= 2.7.5-5~) Filename: pool-stretch/python-tensorboard/0.1.8/python-tensorboard_0.1.8-1+Debian-stretch-9.1_all.deb Size: 1574132 MD5sum: 653a68ad937638e770198d95ed340fcf SHA1: 779b7bbab90dbc2ab09c6e60e809a6a30c42efd7 SHA256: c514ed13135911e9212b75ea0bade54899d5baaa740ea522ace832b8cda8e977 SHA512: 4ed9b9b6914677bdc2521bdd3693da4a551e5c67f1063da9ea7725d3752606f290aa0e48aba7fffc0a620b1fcf3558436ec8bc9262ddf8175ca0f0143a8964d3 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. 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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 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 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. 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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 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 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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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. 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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. 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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. 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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: 1.4.1-1+Debian-stretch-9.3 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-stretch/python-tensorflow-cuda/1.4.1/tensorflow-tools_1.4.1-1+Debian-stretch-9.3_all.deb Size: 4766 MD5sum: 6eaf25985cd854f61ad58969aa48930e SHA1: 8eaa15151514717d6cae6a11f6c03d03a2fdab98 SHA256: a30d33d60991ada1c0bb71b3d7313ce3da00f8ff180477799394ba7d60091a6b SHA512: b19d0259d7c687f8731b0dc2ed75381c64c8217a0516e90c95939124f5e1bf66843f93713ae40335d34e482d6d8b064d4130b3ce6fe03944579f452e83052908 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-stretch-9.4 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-stretch/python-tensorflow-cuda/1.4.1/tensorflow-tools_1.4.1-2+Debian-stretch-9.4_all.deb Size: 4962 MD5sum: df67231123ea77052375f045ce4e351b SHA1: e24ae6b6f79d164ea4b3781289409529af140d39 SHA256: 46bc6b2fe403f1df09b4599d7d89f501969a14341811e2391591429c728e827e SHA512: dd9ade0e442c237b16effda843fdfafe1524aad555ec490321faf02d14a0d598003e6d6d2d8fbc17e7a82fc3b5d4cc18dd5099cf2cd9843e9d6102116c7e0d85 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. 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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. 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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. 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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. 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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. 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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. 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This package installs the library for Python 3. Package: python-torch-doc Architecture: all Version: 0.2.0-1+Debian.stretch.9.1 Priority: optional Section: doc Source: pytorch (0.2.0-1) Maintainer: Adam Cecile Installed-Size: 3039 Filename: pool-stretch/pytorch/pytorch/python-torch-doc_0.2.0-1+Debian.stretch.9.1_all.deb Size: 1750490 MD5sum: 6efa0d4906d801a4bf9cb12d30ea848e SHA1: 9f95582eeb1cc7ad3738ee4891d141297fbb7534 SHA256: a38aa61f48bc8b0982fc23770ef0f126a4a969639fbabfe0f3273c2d29ae45ee SHA512: c6545c533b3c81b45081e8c59d1974ef3180d04bf4eea2f4c6ba85bfb4462a6b67710f8e0aa3498180191b56175a717a1335d381aa04bb2f4c98988f35073033 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 3. Package: shark-doc Architecture: all Version: 3.1.4+ds1-1.1+Debian.stretch.9.9 Built-Using: sphinx (= 1.4.9-2) Multi-Arch: foreign Priority: optional Section: doc Source: shark (3.1.4+ds1-1.1) Maintainer: Debian Science Maintainers Installed-Size: 101553 Depends: libjs-sphinxdoc (>= 1.0), libjs-mathjax Filename: pool-stretch/shark/shark-doc_3.1.4+ds1-1.1+Debian.stretch.9.9_all.deb Size: 20965528 MD5sum: 5dfc20511a9eb6404c0a1b70de10e992 SHA1: 3b3587900090c5932b6a8a451a14eb9eb2b7588b SHA256: 0ba0c54d50ca21111a2d8c8c6c5b71105c23be8c1632beb03450d14d65d88d7b SHA512: a2ada5c1fd09cbd9648aa434ded4a25f5290c5b02d5677f0a628a73705065d893e0a898c8715dcfb1678d8b40074a9fe528fef424f9ab4ff7073d8a45e8826b8 Homepage: http://image.diku.dk/shark Description: documentation for Shark Shark is a modular C++ library for the design and optimization of adaptive systems. 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Package: python-sortedcollections-doc Architecture: all Version: 1.0.1-1~bpo+Debian.stretch.9.9 Priority: optional Section: doc Source: sortedcollections (1.0.1-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 266 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-stretch/sortedcollections/python-sortedcollections-doc_1.0.1-1~bpo+Debian.stretch.9.9_all.deb Size: 122062 MD5sum: f21323c356369935ea6a239fb3bf0659 SHA1: 374bf8442009430fdbcddba2667ef23e71645eaa SHA256: 3ba050ed6ef0d635b601cf18a0f0f07448b604ec73815c9975fba1287077c2d9 SHA512: 82970c415d4bcb8919b9a164391198c93d660b9bbad3fc444fb4d703a8b132edc5d556d7d82585a3314c390e5c67963aa6431e1308953533766bb523e6770506 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.stretch.9.9 Priority: optional Section: python Source: sortedcollections (1.0.1-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 43 Depends: python3-sortedcontainers, python3:any (>= 3.3.2-2~) Filename: pool-stretch/sortedcollections/python3-sortedcollections_1.0.1-1~bpo+Debian.stretch.9.9_all.deb Size: 9494 MD5sum: 8306c8ad5867eabf062148d9e73e312c SHA1: 24641d59f32910ecf7db16b32863e30f6ab201e2 SHA256: 910ef043931ef38dd6629e8462c321de23c9676fc3021af8f9cdffb454711888 SHA512: c23e7f83b4f27e873ba8d68aff1e4733ceec31f8e23fcfff30373f3c7b92f781028e665081f2f0fef89ec45209343a7bddb921d730f3051932374b25066f5377 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.stretch.9.9 Built-Using: alabaster (= 0.7.8-1), sphinx (= 1.4.9-2) Multi-Arch: foreign Priority: optional Section: doc Source: sortedcontainers (2.1.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 20509 Depends: libjs-sphinxdoc (>= 1.0) Filename: pool-stretch/sortedcontainers/python-sortedcontainers-doc_2.1.0-1~bpo+Debian.stretch.9.9_all.deb Size: 15948518 MD5sum: ce3335be1e12adedfca4ffc4650f9544 SHA1: 4332d32a30e7cd18b53bb5429473622bc04ad111 SHA256: a8087edac85cb34c30eaac928bd74c862df080cb84d9b1eb68a11f00aa447bd8 SHA512: 52da1b6082a3cdc0b609638b97c03ec2afad530af59c073f74a2e3cf4ce8417d3f2d27e87fa278d767b4183612314b0b684f5f48b579504770236b66d26e2d92 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.stretch.9.9 Multi-Arch: foreign Priority: optional Section: python Source: sortedcontainers (2.1.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 155 Provides: python2.7-sortedcontainers Depends: python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-sortedcontainers-doc Filename: pool-stretch/sortedcontainers/python-sortedcontainers_2.1.0-1~bpo+Debian.stretch.9.9_all.deb Size: 28298 MD5sum: bc6ea9407b4c0e1beee67a1c0aeb9ef5 SHA1: 37eddde5e59e4fae2c8302d1191ed07b79646eac SHA256: d54ba70290ec05499b422c3ab9bd968fc98e74ea1511382e9882e40b8b58fbc1 SHA512: b2b08b0d35fcd77057e7740960f4188102efccfee7347f1c851e460724f08a0adf02f745b4dec92eb5356c6591a6a07a18c322a5ef35988db30b4fdde9e5ef1b 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.stretch.9.9 Multi-Arch: foreign Priority: optional Section: python Source: sortedcontainers (2.1.0-1~bpo) Maintainer: Debian Python Modules Team Installed-Size: 155 Depends: python3:any (>= 3.3.2-2~) Suggests: python-sortedcontainers-doc Filename: pool-stretch/sortedcontainers/python3-sortedcontainers_2.1.0-1~bpo+Debian.stretch.9.9_all.deb Size: 28376 MD5sum: af962e2af8bae6ecd509bae042d86cf0 SHA1: a7854bb71c5c7d51484066a45105e94dff1309ae SHA256: 95f1b6b54d0a80024fe68769aa44a6c4f6ce6d748ce8c125a0f313a2d07fd908 SHA512: a745d66b3df0f69c1d4f61be8ad186019e733c29642cb84b7a9786905430985b9e4367f12789f4f6a6c604eb2969a5c7b1ea1aeee13057a9131b4d593539ea0d 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-cvxopt-doc Architecture: all Version: 1.1.9+dfsg-2~bpo9+1 Priority: optional Section: doc Source: cvxopt Maintainer: Debian Science Team Installed-Size: 2110 Depends: libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common, libjs-mathjax Breaks: python-cvxopt (<< 1.1.8) Replaces: python-cvxopt (<< 1.1.8) Filename: pool-stretch/statsmodel/cvxopt/python-cvxopt-doc_1.1.9+dfsg-2~bpo9+1_all.deb Size: 994712 MD5sum: 6eff6289b0062d693f962ceadb46df99 SHA1: e129e542254a8e3cfa55a7293fe2087faa378e96 SHA256: 5663d01970de3af21b705b4f630c5dd3fe197f3a8a67973de9a991449957069f SHA512: f538f19e7f51e8b007e1ad335a4ec85abc4fa39b7591bfbacfb5c47b6aa3fd4b251a2839491d67ce05f20a181ccd633971ffdccf4f42e0bdcd252b7792b452a4 Homepage: http://cvxopt.org/ Description: Python package for convex optimization (documentation) CVXOPT is a Python package for convex optimization. It includes * Python classes for storing and manipulating dense and sparse matrices * an interface to most of the double-precision real and complex BLAS * an interface to the dense linear equation solvers and eigenvalue routines from LAPACK * interfaces to the sparse LU and Cholesky solvers from UMFPACK and CHOLMOD. * routines for solving convex optimization problems, an interface to the linear programming solver in GLPK, and interfaces to the linear and quadratic programming solvers in MOSEK * a modeling tool for specifying convex piecewise-linear optimization problems. . This package contains the documentation of the Python module. Package: python-statsmodels-doc Architecture: all Version: 0.9.0-0+Debian.stretch.9.6 Priority: optional Section: doc Source: statsmodels (0.9.0-0) Maintainer: Debian Science Maintainers Installed-Size: 79150 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-stretch/statsmodel/statsmodel/python-statsmodels-doc_0.9.0-0+Debian.stretch.9.6_all.deb Size: 12684176 MD5sum: 222696023de3cb3f2c5a183d0d599843 SHA1: bcb02314c38f3ebfd85cd1ea60d871a9b74859fd SHA256: 01c8819a903ff6c80f8c7de79d425253e3dfd32bf9407d796db9397b1e9cabce SHA512: 796f5292414ccd38be6db57c4bdee1bcbc35c57cb81427f5ac8dc326f40208b950d88687ea05bf3c336cceb01dd38507d0aef8222b5fdc3b0f1507c00196a1ce 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: 0.8.0-6~bpo9+1 Priority: optional Section: python Source: statsmodels Maintainer: Debian Science Maintainers Installed-Size: 15921 Provides: python2.7-statsmodels Depends: python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-scipy, python-statsmodels-lib (>= 0.8.0-6~bpo9+1), 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-stretch/statsmodel/statsmodel/python-statsmodels_0.8.0-6~bpo9+1_all.deb Size: 3321602 MD5sum: 7a954c412674eb4939469ecf4fcf9cc0 SHA1: 3c67b07610b757caf84a98ccc9eccbe3ad26238f SHA256: 27dd07ab7ab9a5b20a3ba425109bbd1a30d9cab8402630c9adc437e3cb8f2074 SHA512: 40c59536931a10c4ddcb182600e33496c4dcd75375311171792958c7d596d795a4c7dd30b10d9fa1e6338a1bc05e83e45c86c1177410765211011278ad6edc43 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: python-statsmodels Architecture: all Version: 0.9.0-0+Debian.stretch.9.6 Priority: optional Section: python Source: statsmodels (0.9.0-0) Maintainer: Debian Science Maintainers Installed-Size: 21456 Provides: python2.7-statsmodels Depends: python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~), 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-stretch/statsmodel/statsmodel/python-statsmodels_0.9.0-0+Debian.stretch.9.6_all.deb Size: 6605450 MD5sum: 802af163a8db6457704e04651c4de00a SHA1: b113f037a22cc33b0a062a6fd1d03b9a1d0fff69 SHA256: dc954a552355935807c186730fad5ef9c87d16e09311cf6491e58eda588a6356 SHA512: ca5a7d8455d3fec69ac0bac9c6f1cc637913631868119dc3448cdfe68785c1640e973a61d04f59442034b76633d6b2feab57e3dc7f5a483be13198c165ad8ac0 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: 0.8.0-6~bpo9+1 Priority: optional Section: python Source: statsmodels Maintainer: Debian Science Maintainers Installed-Size: 15471 Depends: python3-numpy, python3:any (>= 3.3.2-2~), python3-scipy, python3-statsmodels-lib (>= 0.8.0-6~bpo9+1), python3-patsy, python3-pandas Recommends: python3-matplotlib, python3-joblib, python3-cvxopt Suggests: python-statsmodels-doc Filename: pool-stretch/statsmodel/statsmodel/python3-statsmodels_0.8.0-6~bpo9+1_all.deb Size: 3006700 MD5sum: 2dc3718ca01d2273c03f0a6e65c07410 SHA1: 4e1594683ae57fae407fb06a422aa1745fad0edc SHA256: 5541bb4253802e61abdf1a56e83ba828faa18968954253699867cebe17a543b0 SHA512: c8ca1ac56bd6999e555028d7d88dae8e3e77658c5df8e97da16bfe5013d67cd412679c4345457d59740c6acb3ca07ae360859daccc7452cf3626ac239340866a 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-statsmodels Architecture: all Version: 0.9.0-0+Debian.stretch.9.6 Priority: optional Section: python Source: statsmodels (0.9.0-0) Maintainer: Debian Science Maintainers Installed-Size: 18196 Depends: python3-numpy, python3:any (>= 3.3.2-2~), 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-stretch/statsmodel/statsmodel/python3-statsmodels_0.9.0-0+Debian.stretch.9.6_all.deb Size: 3433702 MD5sum: 77cb8003a6c549753ae358f30b4e8a05 SHA1: 676c3610e580a5f12992b8b5f62682fe865b14d3 SHA256: 0eefe1658c46cce27406e46c6e83472c49085e0a18b5551d6ad5cd0af89ed73e SHA512: 6f63a89e659fbec3ae7dea78ea405206acc67bbdbee257a23f46dd69eb3913e00eed8796640503a42012782a11c708c8ab562eaf2b4482dbeb055e03108a1bad 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.9.12.stretch Priority: optional Section: python Source: torch-summary (1.2.0+3.5-0) Maintainer: Adam Cecile Installed-Size: 59 Depends: python3:any Filename: pool-stretch/torch-summary/python3-torchsummary_1.2.0+3.5-0+Debian.9.12.stretch_all.deb Size: 15490 MD5sum: e729afcf39af9d6a50ead526211c53de SHA1: 3541385d770ecfe16dc386bc54df459167d329a9 SHA256: 019beca1c42ca3eec244d01542971c752d66608082c090dd9e988b3dd8c5726a SHA512: 0413fb37df6f284ecdf40be48724865ed3738b03e5af566697976ec998f54630fbf520c11ffab73f717c2a04edc20e0597964b156633f866e6863f6758636595 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. Package: python3-zict Architecture: all Version: 0.1.3-1~bpo9+0 Priority: optional Section: python Source: zict Maintainer: Debian Python Modules Team Installed-Size: 46 Depends: python3-heapdict, python3:any (>= 3.3.2-2~) Filename: pool-stretch/zict/python3-zict_0.1.3-1~bpo9+0_all.deb Size: 10192 MD5sum: da3d3630afda12a46066637743a98508 SHA1: 4a1dc518bd48dc8a964c24bd44622b75191f89bb SHA256: 2beeb09885ca4995e628eeb1a443b519698b9f73e4b4cfd00d25bb09a8ccbfb0 SHA512: 5ff686038a6c1d818064d93d23d5ede51887ce2cf796800829e9be7a61a1f59f3a6b63c2091b9d069deda5d98d0ef17ccae8e45e048fe8a810db3504a558a19a Homepage: http://github.com/mrocklin/zict/ Description: Mutable mapping tools for Python 3 The dictionary / mutable mapping interface is powerful and multi-faceted. . * stores data in different locations such as in-memory, on disk, in archive files, etc.. * manage old data with different policies like LRU, random eviction, etc.. * might encode or transform data as it arrives or departs the dictionary through compression, encoding, etc.. . This contains the Python 3 version