Metadata-Version: 1.1
Name: maggy
Version: 0.2
Summary: UNKNOWN
Home-page: https://github.com/logicalclocks/maggy
Author: Moritz Meister
Author-email: meister.mo@gmail.com
License: GNU Affero General Public License v3
Description: Maggy
        =====
        
        Maggy is a framework for efficient asynchronous optimization of expensive
        black-box functions on top of Apache Spark. Compared to existing frameworks,
        maggy is not bound to stage based optimization algorithms and therefore it is
        able to make extensive use of early stopping in order to achieve efficient
        resource utilization.
        
        Right now, maggy supports asynchronous hyperparameter tuning of machine
        learning and deep learning models, but other use cases include ablation studies
        and asynchronous distributed training.
        
        Moreover, it provides a developer API that allows advanced usage by
        implementing custom optimization algorithms and early stopping criteria.
        
        In order to make decisions on early stopping, the Spark executors are sending
        heart beats with the current performance of the model they are training to the
        maggy experiment driver which is running on the Spark driver. We call the
        process of training a model with a certain hyperparameter combination a
        *trial*. The experiment driver then uses all information of finished trials and
        the currently running ones to check in a specified interval, which of the
        trials should be stopped early.
        Subsequently, the experiment driver provides a new trial to the Spark
        executor.
        
        Quick Start
        -----------
        
        To Install:
        
        >>> pip install maggy
        
        The programming model is that you wrap the code containing the model training
        inside a wrapper function. Inside that wrapper function provide all imports and
        parts that make up your experiment.
        
        There are three requirements for this wrapper function:
        
        1. The function should take the hyperparameters as arguments, plus one
           additional parameter reporter which is needed for reporting the current
           metric to the experiment driver.
        2. The function should return the metric that you want to optimize for. This
           should coincide with the metric being reported in the Keras callback (see
           next point).
        3. In order to leverage on the early stopping capabilities of maggy, you need
           to make use of the maggy reporter API. By including the reporter in your
           training loop, you are telling maggy which metric to report back to the
           experiment driver for optimization and to check for global stopping. It is
           as easy as adding reporter.broadcast(metric=YOUR_METRIC) for example at the
           end of your epoch or batch training step and adding a reporter argument to
           your function signature. If you are not writing your own training loop you
           can use the pre-written Keras callbacks in the `maggy.callbacks` module.
        
        Sample usage:
        
        >>> # Define Searchspace
        >>> from maggy import Searchspace
        >>> # The searchspace can be instantiated with parameters
        >>> sp = Searchspace(kernel=('INTEGER', [2, 8]), pool=('INTEGER', [2, 8]))
        >>> # Or additional parameters can be added one by one
        >>> sp.add('dropout', ('DOUBLE', [0.01, 0.99]))
        
        >>> # Define training wrapper function:
        >>> def mnist(kernel, pool, dropout, reporter):
        >>>     # This is your training iteration loop
        >>>     for i in range(number_iterations):
        >>>         ...
        >>>         # add the maggy reporter to report the metric to be optimized
        >>>         reporter.broadcast(metric=accuracy)
        >>>         ...
        >>>     # Return the same final metric
        >>>     return accuracy
        
        >>> # Launch maggy experiment
        >>> from maggy import experiment
        >>> result = experiment.lagom(map_fun=mnist,
        >>>                            searchspace=sp,
        >>>                            optimizer='randomsearch',
        >>>                            direction='max',
        >>>                            num_trials=15,
        >>>                            name='MNIST'
        >>>                           )
        
        **lagom** is a Swedish word meaning "just the right amount". This is how maggy
        uses your resources.
        
        MNIST Example
        -------------
        
        For a full MNIST example with random search using Keras,
        see the Jupyter Notebook in the `examples` folder.
        
        Documentation
        -------------
        
        API documentation is available `here <https://maggy.readthedocs.io/en/latest/>`_.
        
Keywords: Hyperparameter,Optimization,Auto-ML,Hops,Hadoop,TensorFlow,Spark
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Programming Language :: Python :: 3
