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    "info": {
        "author": "Konstantin Lopuhin",
        "author_email": "kostia.lopuhin@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5"
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        "description": "tensorboard_logger\n==================\n\n\n.. image:: https://img.shields.io/pypi/v/tensorboard_logger.svg\n        :target: https://pypi.python.org/pypi/tensorboard_logger\n\n.. image:: https://img.shields.io/travis/TeamHG-Memex/tensorboard_logger.svg\n        :target: https://travis-ci.org/TeamHG-Memex/tensorboard_logger\n\n\nLog TensorBoard events without TensorFlow\n-----------------------------------------\n\n`TensorBoard <https://www.tensorflow.org/how_tos/summaries_and_tensorboard/>`_\nis a visualization tool (not this project, it's a part of\n`TensorFlow <https://www.tensorflow.org>`_ framework)\nthat makes it easy to check training progress, compare between\ndifferent runs, and has lots of other cool features.\n\n.. image:: tensorboard_example.png\n\n``tensorboard_logger`` library allows to write TensorBoard events without TensorFlow::\n\n    from tensorboard_logger import configure, log_value\n\n    configure(\"runs/run-1234\")\n\n    for step in range(1000):\n        v1, v2 = do_stuff()\n        log_value('v1', v1, step)\n        log_value('v2', v2, step)\n\n\nNote: if you are already using TensorFlow in your project,\nyou probably don't need this library.\n\n\nInstallation\n------------\n\nTensorFlow is required only for viewing logged events: please check installation guide\non the `official site <https://www.tensorflow.org/get_started/os_setup.html#download-and-setup>`_\n(you probably want a CPU-only version).\n\n``tensorboard_logger`` can be installed with pip::\n\n    pip install tensorboard_logger\n\n\nUsage\n-----\n\nYou can either use default logger with ``tensorboard_logger.configure``\nand ``tensorboard_logger.log_value`` functions, or use ``tensorboard_logger.Logger`` class.\n\nThis library can be used to log numerical values of some variables in TensorBoard format, so you can\nuse TensorBoard to visualize how they changed, and compare same variables between different runs.\nLog file is written into a directory, so you need a separate directory for each run\n(you can place other logs or output files you use in the same directory).\nDirectories from different runs you wish to compare should have the same parent\n(there can be other files or directories with the same parent, TensorBoard will figure out\nwhich directories contain logs).\n\nApart from variable names and their values, another important thing is the **step**: this must\nbe an integer that represents some increasing step - it can be a step in training or some\nother number. The values are ordered by step in TensorBoard, although you can view them\nordered by time or relative step too.\n\nA simple usage example::\n\n    from tensorboard_logger import configure, log_value\n\n    configure(\"runs/run-1234\", flush_secs=5)\n\n    for step in range(1000):\n        v1, v2 = do_stuff()\n        log_value('v1', v1, step)\n        log_value('v2', v2, step)\n\n\nYou can start TensorBoard right away::\n\n    tensorboard --logdir runs\n\nAnd go check the metrics to TensorBoard UI at http://localhost:6006\n(note that it binds to 0.0.0.0 by default).\nMetrics are refreshed on switch to browser tab, and there is also a refresh button\nat the top right.\n\nRuntime overhead rather large, about 0.1 - 0.2 ms for a single value logged\n(so about 5,000 - 10,000 operations per second).\n\n\nAPI\n---\n\n``tensorboard_logger.configure(logdir, flush_secs=2)``\n\nConfigure logging: a file will be written to ``logdir``, and flushed every ``flush_secs``.\n**NOTE:** right now file is flushed after each event written.\n\n``tensorboard_logger.log_value(name, value, step=None)``\n\nLog new ``value`` for given ``name`` on given ``step``.\n``value`` should be a real number (it will be converted to float),\nand ``name`` should be a string (it will be converted to a valid\nTensorFlow summary name). ``step`` should be an non-negative integer,\nand is used for visualization: you can log several different\nvariables on one step, but should not log different values\nof the same variable on the same step (this is not checked).\nYou can also omit step entirely.\n\n``tensorboard_logger.Logger``\n\nA class for writing logs in a directory.\nUse it if default logger used in two above functions is not enough for you\n(e.g. you want to log into several different directories, or don't like\nglobal variables).\nConstructor has the same signature as ``tensorboard_logger.configure``,\nand it has a single ``log_value`` method with the same signature as\n``tensorboard_logger.log_value``.\n\n\nDevelopment\n-----------\n\nCompiling python protobuf files::\n\n    protoc --python_out . tensorboard_logger/tf_protobuf/summary.proto\n    protoc --python_out . tensorboard_logger/tf_protobuf/event.proto\n\n\nLicense\n-------\n\n`MIT license`_\n\n.. _MIT license: LICENSE\n\n----\n\n.. image:: https://hyperiongray.s3.amazonaws.com/define-hg.svg\n\t:target: https://hyperiongray.com/?pk_campaign=github&pk_kwd=tensorboard_logger\n\t:alt: define hyperiongray\n\n\nHistory\n=======\n\n0.1.0 (2018-02-08)\n------------------\n\n* Ability to log histograms and images (#18)\n\n\n0.0.4 (2017-07-31)\n------------------\n\n* Fix working with TensorFlow installed (#11)\n\n\n0.0.3 (2016-11-17)\n------------------\n\n* Fix PyPI packaging\n\n\n0.0.2 (2016-11-03)\n------------------\n\n* TensorFlow is no longer required (#1)\n* Travis build is fixed (#2)\n\n\n0.0.1 (2016-11-01)\n------------------\n\n* First release on PyPI.\n",
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