Deepobs Deepobs

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. You only have to code your new optimizer, we take care of the benchmarking for you! Automatically use competitive baselines! The same test problems for all optimizers! You can install the latest stable release of DeepOBS using pip

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: The code for the current implementation working with TensorFlow can be found on GitHub. 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.

This package is no longer maintained. It is superseded by the AlgoPerf benchmark suite DeepOBS is a benchmarking suite that drastically simplifies, automates and improves the evaluation of deep learning optimizers.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: 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). DeepOBS is a Python package to benchmark deep learning optimizers. It currently supports TensorFlow but a PyTorch version is currently in development. 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. You can install the latest stable release of DeepOBS using pip:

The package requires the following packages: TensorFlow is not a required package to allow for both the CPU and GPU version. Make sure that one of those is installed. There was an error while loading. Please reload this page. This tutorial will show you an example of how DeepOBS can be used to benchmark the performance of a new optimization method for deep learning.

This simple example aims to show you some basic functions of DeepOBS, by creating a run script for a new optimizer (we will use the Momentum optimizer as an example here) and running it... The easiest way to use DeepOBS with a new optimizer is to write a run script for it. This run script will import the optimizer and list its hyperparameters (other than the learning rate). For the Momentum optimizer this is simply You can download this example run script and use it as a template. The DeepOBS runner (Line 7) needs access to an optimizer class with the same API as the TensorFlow optimizers and a list of additional hyperparameters for this new optimizers.

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. DeepOBS provides modules and scripts for the full stack required to rapidly, reliably and reproducibly benchmark deep learning optimizers.

Here we briefly described the different levels of automation that DeepOBS provides. While, they are built hierarchically, they can be used separately. For example, one can use just the data loading capabilities of DeepOBS and built a new test problem on top of it. A more detailed description of the modules and scripts can be found in the API reference section. DeepOBS can automatically download and pre-process all necessary data sets. This includes

While ImageNet is part of DeepOBS, it is currently not part of the automatic data downloading pipeline mechanic. Downloading the ImageNet data set requires an account and can take a lot of time to download. Additonally, it requires quite a large amount of memory. The best way currently is to download and preprocess the ImageNet data set separately if needed and move it into the DeepOBS data folder.

People Also Search

• DeepOBS - DeepOBS
• Welcome to DeepOBS — DeepOBS 1.1.0 documentation
• DeepOBS - A Deep Learning Optimizer Benchmark Suite
• DeepOBS: A Deep Learning Optimizer Benchmark Suite
• Quick Start — DeepOBS 1.1.0 documentation - Read the Docs
• deepobs · PyPI
• DeepOBS/README.md at master · fsschneider/DeepOBS · GitHub
• Simple Example — DeepOBS 1.1.0 documentation
• Overview — DeepOBS 1.1.0 documentation