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TensorFlow ( is a popular open-source library for symbolic math and linear algebra, with particular optimization for neural-networks-based machine learning workflow. Maintained by Google, it is widely used for research and production in both the academia and the industry. 

TensorFlow supports a wide variety of hardware platforms (CPUs, GPUs, TPUs), and can be scaled up to utilize multiple compute devices on a single or multiple compute nodes. The main objective of this benchmark is to profile the scaling behavior of TensorFlow on different hardware, and thereby provide a reference baseline of its performance for different sizes of applications.

There are many open-source datasets available for benchmarking TensorFlow, such as `mnist`, `fashion_mnist`, `cifar`, `imagenet`, and so on. This benchmark suite, however, would like to focus on a scientific research use case. `DeepGalaxy` is a code built with TensorFlow, which uses deep neural network to classify galaxy mergers in the Universe, observed by the Hubble Space Telescope and the Sloan Digital Sky Survey.  

- Website:
- Code download:
- [Prerequisites installation](#prerequisites-installation)
- [Test Case A](Testcase_A/
- [Test Case B](Testcase_B/
- [Test Case C](Testcase_C/

## Prerequisites Installation
The prerequsities consists of a list of python packages as shown below. It is recommended to create a python virtual environment (either with `pyenv` or `conda`). The following packages can be installed using the `pip` package management tool:
pip install tensorflow
pip install horovod
pip install scikit-learn
pip install scikit-image
pip install pandas
Note: there is no guarantee of optimal performance when `tensorflow` is installed using `pip`. It is better if `tensorflow` is compiled from source, in which case the compiler will likely be able to take advantage of the advanced instruction sets supported by the processor (e.g., AVX512). An official build instruction can be found at Sometimes, an HPC center may have a tensorflow module optimized for their hardware, in which case the `pip install tensorflow` line can be replaced with a line like `module load <name_of_the_tensorflow_module>`.