Metadata-Version: 1.1
Name: tensorflow-qnd
Version: 0.0.4
Summary: Quick and Distributed TensorFlow command framework
Home-page: http://github.com/raviqqe/tensorflow-qnd/
Author: Yota Toyama
Author-email: raviqqe@gmail.com
License: Public Domain
Description: # tensorflow-qnd
        
        [![PyPI version](https://badge.fury.io/py/tensorflow-qnd.svg)](https://badge.fury.io/py/tensorflow-qnd)
        [![Build Status](https://travis-ci.org/raviqqe/tensorflow-qnd.svg?branch=master)](https://travis-ci.org/raviqqe/tensorflow-qnd)
        [![License](https://img.shields.io/badge/license-unlicense-lightgray.svg)](https://unlicense.org)
        
        Quick and Distributed TensorFlow command framework
        
        tensorflow-qnd is a TensorFlow framework to create commands to experiment with
        models on multiple computers.
        While made to be used on multiple computers in a cluster, this library is also
        useful to exploit multiple GPUs on a single machine.
        
        
        ## Installation
        
        Python 3.5+ and TensorFlow 0.12+ are required.
        
        ```
        $ pip3 install --user --upgrade tensorflow-qnd
        ```
        
        
        ## Usage
        
        ```
        def_run(batch_inputs=True, prepare_filename_queues=True)
            Define `run()` function.
        
            See also `help(def_run())`.
        
            Args:
                batch_inputs: If `True`, create batches from Tensors
                    returned from `train_input_fn()` and `train_input_fn()` and feed
                    them to a model.
                prepare_filename_queues: If `True`, create filename queues for train
                    and eval data based on file paths specified by command line
                    arguments.
        
            Returns:
                `run()` function.
        
        
        run(model_fn, train_input_fn, eval_input_fn=None)
            Run `tf.contrib.learn.python.learn.learn_runner.run()`.
        
            Args:
                model_fn: A function to construct a model.
                    Types of its arguments must be one of the following:
                        `Tensor, ...`,
                        `Tensor, ..., mode=ModeKeys`.
                    Types of its return values must be one of the following:
                        `Tensor, Tensor, Operation, eval_metrics=dict<str, Tensor>`
                        (predictions, loss, train_op, and eval_metrics (if any)),
                        `ModelFnOps`.
                train_input_fn, eval_input_fn: Functions to serve input Tensors
                    fed into the model. If `eval_input_fn` is `None`,
                    `train_input_fn` will be used instead.
                    Types of its arguments must be one of the following:
                        `QueueBase` (a filename queue),
                        `None` (No argument).
                    Types of its return values must be one of the following:
                        `Tensor, Tensor` (x and y),
                        `dict<str, Tensor>, dict<str, Tensor>`
                        (features and labels).
                    The keys of `dict` arguments must match with names of
                    `model_fn` arguments.
        
            Returns:
                Return value of `tf.contrib.learn.python.learn.learn_runner.run()`.
        
        
        add_flag(name, *args, **kwargs)
            Add a flag.
        
            Args:
                name: Flag name. Real flag name will be `"--{}".format(name)`.
                *args, **kwargs: The rest arguments are the same as
                    `argparse.add_argument()`.
        
        
        add_required_flag(name, *args, **kwargs)
            Add a required flag.
        
            Its interface is the same as `add_flag()` but `required=True` is set by
            default.
        ```
        
        
        ## Examples
        
        See [examples](examples) directory.
        
        
        ## License
        
        [The Unlicense](https://unlicense.org)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: Public Domain
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: System :: Networking
