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        "description": "# HTUNEML - machine learning experiments monitoring and tuning\n\n**Quickstart:** ``pip install htuneml``. See the \"Installing\" section for more details.\n\n\nProject links:\n\n-  `PyPI <https://pypi.python.org/pypi/htuneml/>`__\n-  `Source code <https://github.com/johnsmithm/htuneml>`__\n-  `Issue tracker <https://github.com/johnsmithm/htuneml/issues>`__\n\n\nExamples\n--------\n\nSee the ``examples/`` `directory <https://github.com/htuneml/htuneml/tree/master/examples>`__ in the repository root for usage examples:\n\n-  `Tensorflow Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__\n-  `Keras Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__\n-  `Pytorch Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__\n\n\nRequirements\n------------\n\nTo use all of the functionality of the library, you should have:\n\n* **Python** 2.6, 2.7, or 3.3+ (required)\n* **PyAudio** 0.2.11+ (required only if you need to use microphone input, ``Microphone``)\n\n\nQuick start\n------------\n\nRegister on website http://registru.ml, copy the api_key:\n\n.. code:: python\n\n    import htuneml as ht\n    \n    job = Job('api_key')\n    \n    @job.monitor\n    def train(par1=2,par2=2):\n        for i in range(par1):\n            #do training here\n            job.log({'loss':i*4,'ep':i})\n\n    job.setName('l2')\n    #job.debug()# uncomment and no experiment will be created and no logs sent\n    train(10, 2)\n\nThis will print out something like the following:\n\n::\n\n    make experiment\n    got key experimnet 5c5c8eaacbcfb9146641367a\n\nAlso it is possible to sent the parameters from the web app. First on gpu/cpu set the lisener:\n\n.. code:: python\n\n    import htuneml as ht\n    \n    job = Job('api_key')\n    \n    def train(par1=2,par2=2):\n        for i in range(par1):\n            #do training here\n            job.log({'loss':i*4,'ep':i})\n\n    job.sentParams(train)#sent the parameters list to the app\n    job.waitTask(train)#wait for parameters from app\n    \n",
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