Welcome to DeepOBS¶
This DeepOBS version is under continious development and a beta of DeepOBS 1.2.0.
Many thanks to Aaron Bahde for spearheading the developement of DeepOBS 1.2.0.
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.
- Automatic tuning of optimizer hyperparameters.
- Reporting and visualization the results of the optimizer benchmark.
The code for the current implementation working with TensorFlow and PyTorch can be found on GitHub.
- Quick Start
- Simple Example
- Suggested Protocol
- How to Write Customized Runner
- Tuning Automation