TF Benchmarks

By DEEP-Hybrid-DataCloud Consortium | Created: - Updated:

docker, tensorflow, cnn, trainable, api-v1

License: MIT

Build Status

tf_cnn_benchmarks from TensorFlow team accessed via DEEPaaS API

tf_cnn_benchmarks contains implementations of several popular convolutional models (e.g. Googlenet, Inception, Overfeat, Resnet, VGG), and is designed to be as fast as possible. tf_cnn_benchmarks supports both running on a single machine or running in distributed mode across multiple hosts. See the High-Performance models guide for more information.


[1] TF CNN Benchmarks:

Run locally on your computer

Using Docker

You can run this module directly on your computer, assuming that you have Docker installed, by following these steps:

$ docker pull deephdc/deep-oc-benchmarks_cnn
$ docker run -ti -p 5000:5000 deephdc/deep-oc-benchmarks_cnn

Using udocker

If you do not have Docker available or you do not want to install it, you can use udocker within a Python virtualenv:

$ virtualenv udocker
$ source udocker/bin/activate
$ git clone
$ cd udocker
$ pip install .
$ udocker pull deephdc/deep-oc-benchmarks_cnn
$ udocker create deephdc/deep-oc-benchmarks_cnn
$ udocker run -p 5000:5000  deephdc/deep-oc-benchmarks_cnn

Once running, point your browser to and you will see the API documentation, where you can test the module functionality, as well as perform other actions (such as training).

For more information, refer to the user documentation.