Metadata-Version: 2.1
Name: htuneml
Version: 0.0.4
Summary: Monitor machine learning experiments
Home-page: http://github.com/johnsmithm/htuneml
Author: Ion Mosnoi
Author-email: moshnoi2000@gmail.com
License: MIT
Description: # HTUNEML - machine learning experiments monitoring and tuning
        
        **Quickstart:** ``pip install htuneml``. See the "Installing" section for more details.
        
        
        Project links:
        
        -  `PyPI <https://pypi.python.org/pypi/htuneml/>`__
        -  `Source code <https://github.com/htuneml/htuneml>`__
        -  `Issue tracker <https://github.com/htuneml/htuneml/issues>`__
        
        
        Examples
        --------
        
        See the ``examples/`` `directory <https://github.com/htuneml/htuneml/tree/master/examples>`__ in the repository root for usage examples:
        
        -  `Tensorflow Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__
        -  `Keras Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__
        -  `Pytorch Mnist <https://github.com/htuneml/htuneml/blob/master/examples/tensorflow-mnist.py>`__
        
        
        Requirements
        ------------
        
        To use all of the functionality of the library, you should have:
        
        * **Python** 2.6, 2.7, or 3.3+ (required)
        * **PyAudio** 0.2.11+ (required only if you need to use microphone input, ``Microphone``)
        
        
        Quick start
        ------------
        
        Register on website http://registru.ml, copy the api_key:
        
        .. code:: python
        
            import htuneml as ht
            
            job = Job('api_key')
            
            @job.monitor
            def train(par1=2,par2=2):
                for i in range(par1):
                    #do training here
                    job.log({'loss':i*4,'ep':i})
        
            job.setName('l2')
            #job.debug()# uncomment and no experiment will be created and no logs sent
            train(10, 2)
        
        This will print out something like the following:
        
        ::
        
            make experiment
            got key experimnet 5c5c8eaacbcfb9146641367a
        
        Also it is possible to sent the parameters from the web app. First on gpu/cpu set the lisener:
        
        .. code:: python
        
            import htuneml as ht
            
            job = Job('api_key')
            
            def train(par1=2,par2=2):
                for i in range(par1):
                    #do training here
                    job.log({'loss':i*4,'ep':i})
        
            job.sentParams(train)#sent the parameters list to the app
            job.waitTask(train)#wait for parameters from app
            
        
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