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        "description": "# DeepOBS - A Deep Learning Optimizer Benchmark Suite\n\n![DeepOBS](docs/deepobs_banner.png \"DeepOBS\")\n\n[![PyPI version](https://badge.fury.io/py/deepobs.svg)](https://badge.fury.io/py/deepobs)\n[![Documentation Status](https://readthedocs.org/projects/deepobs/badge/?version=stable)](https://deepobs.readthedocs.io/en/latest/?badge=stable)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n\n**DeepOBS** is a benchmarking suite that drastically simplifies, automates and\nimproves the evaluation of deep learning optimizers.\n\nIt can evaluate the performance of new optimizers on a variety of\n**real-world test problems** and automatically compare them with\n**realistic baselines**.\n\nDeepOBS automates several steps when benchmarking deep learning optimizers:\n\n  - Downloading and preparing data sets.\n  - Setting up test problems consisting of contemporary data sets and realistic\n    deep learning architectures.\n  - Running the optimizers on multiple test problems and logging relevant\n    metrics.\n  - Reporting and visualization the results of the optimizer benchmark.\n\n![DeepOBS Output](docs/deepobs.jpg \"DeepOBS_output\")\n\nThe code for the current implementation working with **TensorFlow** can be found\non [Github](https://github.com/fsschneider/DeepOBS).\nA PyTorch version is currently developed (see News section below).\n\nThe full documentation is available on readthedocs:\nhttps://deepobs.readthedocs.io/\n\nThe paper describing DeepOBS has been accepted for ICLR 2019 and can be found\nhere:\nhttps://openreview.net/forum?id=rJg6ssC5Y7\n\n**If you find any bugs in DeepOBS, or find it hard to use, please let us know.\nWe are always interested in feedback and ways to improve DeepOBS.**\n\n## News\n\nWe are currently working on a new and improved version of DeepOBS, version 1.2.0.\nIt will support **PyTorch** in addition to TensorFlow, has an easier interface, and\nmany bugs ironed out. You can find the latest version of it in [this branch](https://github.com/fsschneider/DeepOBS/tree/v1.2.0-beta0).\n\nA pre-release, version 1.2.0-beta0, will be available shortly and a full release is expected in a few weeks.\n\nMany thanks to [Aaron Bahde](https://github.com/abahde) for spearheading the developement of DeepOBS 1.2.0.\n\n## Installation\n\n\tpip install deepobs\n\nWe tested the package with Python 3.6 and TensorFlow version 1.12. Other\nversions of Python and TensorFlow (>= 1.4.0) might work, and we plan to expand\ncompatibility in the future.\n\nIf you want to create a local and modifiable version of DeepOBS, you can do this directly from this repo via\n\n\tpip install -e git+https://github.com/fsschneider/DeepOBS.git#egg=DeepOBS\n\nfor the latest stable version, or \n\n\tpip install -e git+https://github.com/fsschneider/DeepOBS.git@v1.2.0-beta0#egg=DeepOBS\n\nto get the preview of DeepOBS 1.2.0.\n\n\nFurther tutorials and a suggested protocol for benchmarking deep learning\noptimizers can be found on https://deepobs.readthedocs.io/\n\n\n",
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