TF Benchmarks

tf_cnn_benchmarks accessed via DEEPaaS API


Published by DEEP-Hybrid-DataCloud Consortium
Created: - Updated:

Module description

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 running on a single machine on a single GPU and multiple GPUs (please, note that running in distributed mode across multiple hosts is not supported by these Docker images). See the High-Performance models guide for more information.

References

[1] TF CNN Benchmarks: https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks

Test this module

You can test and execute this module in various ways.

Excecute 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

Execute on your computer 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 https://github.com/indigo-dc/udocker
$ 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

In either case, once the module is running, point your browser to http://127.0.0.1:5000/ 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.

Train this module

You can train this model using the DEEP framework. In order to execute this module in our pilot e-Infrastructure you would need to be registered in the DEEP IAM.

Once you are registedered, you can go to our training dashboard to configure and train it.

For more information, refer to the user documentation.

Categories

docker, tensorflow, cnn, trainable, api-v1

License

License: MIT

Configure and train

Get the code

Github Docker Hub