This package is no longer maintained. It is superseded by the AlgoPerf benchmark suite
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 visualizing the results of the optimizer benchmark.
The code for the current implementation working with TensorFlow can be found on Github. A PyTorch version is currently developed and can be accessed via the pre-release or the develop branch (see News section below).
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
If you find any bugs in DeepOBS, or find it hard to use, please let us know. We are always interested in feedback and ways to improve DeepOBS.
We are currently working on a new and improved version of DeepOBS, version 1.2.0. It will support PyTorch in addition to TensorFlow, has an easier interface, and many bugs ironed out. You can find the latest version of it in this branch.
A pre-release is available now. The full release is expected in a few weeks.
Many thanks to Aaron Bahde for spearheading the development of DeepOBS 1.2.0.
pip install deepobs
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.
If you want to create a local and modifiable version of DeepOBS, you can do this directly from this repo via
pip install -e git+https://github.com/fsschneider/DeepOBS.git#egg=DeepOBS
for the stable version, or
pip install -e git+https://github.com/fsschneider/DeepOBS.git@develop#egg=DeepOBS
for the latest development version.
Further tutorials and a suggested protocol for benchmarking deep learning optimizers can be found on https://deepobs.readthedocs.io/