Metadata-Version: 1.1
Name: deepobs
Version: 1.1.0
Summary: Deep Learning Optimizer Benchmark Suite
Home-page: UNKNOWN
Author: Frank Schneider, Lukas Balles and Philipp Hennig,
Author-email: frank.schneider@tue.mpg.de
License: MIT
Description: # DeepOBS - A Deep Learning Optimizer Benchmark Suite
        
        ![DeepOBS](docs/deepobs_banner.png "DeepOBS")
        
        [![Documentation Status](https://readthedocs.org/projects/deepobs/badge/?version=latest)](https://deepobs.readthedocs.io/en/latest/?badge=latest)
        [![Build Status](https://travis-ci.com/fsschneider/deepobs.svg?branch=master)](https://travis-ci.com/username/projectname)
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        
        **DeepOBS** is a benchmarking suite that drastically simplifies, automates and
        improves the evaluation of deep learning optimizers.
        
        It can evaluate the performance of new optimizers on a variety of
        **real-world test problems** and automatically compare them with
        **realistic baselines**.
        
        DeepOBS automates several steps when benchmarking deep learning optimizers:
        
          - Downloading and preparing data sets.
          - Setting up test problems consisting of contemporary data sets and realistic
            deep learning architectures.
          - Running the optimizers on multiple test problems and logging relevant
            metrics.
          - Reporting and visualization the results of the optimizer benchmark.
        
        ![DeepOBS Output](docs/deepobs.jpg "DeepOBS_output")
        
        The code for the current implementation working with **TensorFlow** can be found
        on [Github](https://github.com/fsschneider/DeepOBS).
        
        The full documentation is available on readthedocs:
        https://deepobs.readthedocs.io/
        
        The paper describing DeepOBS has been accepted for ICLR 2019 and can be found
        here:
        https://openreview.net/forum?id=rJg6ssC5Y7
        
        We are actively working on a **PyTorch** version and will be releasing it in the
        next months. In the meantime, PyTorch users can still use parts of DeepOBS such
        as the data preprocessing scripts or the visualization features.
        
        
        ## Installation
        
        	pip install git+https://github.com/fsschneider/DeepOBS.git
        
        Note, that the installation process can take a while as it will also
        automatically download all baseline results.
        
        We tested the package with Python 3.6 and TensorFlow version 1.12. Other
        versions of Python and TensorFlow (>= 1.4.0) might work, and we plan to expand
        compatibility in the future.
        
        Further tutorials and a suggested protocol for benchmarking deep learning
        optimizers can be found on https://deepobs.readthedocs.io/
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
