The Tufts High Performance Compute (HPC) cluster delivers 35,845,920 cpu hours and 59,427,840 gpu hours of free compute time per year to the user community.

Teraflops: 60+ (60+ trillion floating point operations per second) cpu: 4000 cores gpu: 6784 cores Interconnect: 40GB low latency ethernet

For additional information, please contact Research Technology Services at tts-research@tufts.edu

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"TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well." 

TensorFlow site has tutorials and other resources for using this application.  TensorFlow has an API for python and C++.
There are two modules for tensorflow on the cluster for python and both  are gpu aware. Use the gpu partition to take advantage of the speedup that the GPUs provide.
tensorflow/11-python2.7
tensorflow/11-python3.5

You must use python3 instead of python if using the tensorflow/11-python3.5 module.

$module load tensorflow/11-python3.5
$python3
Python 3.5.0 (default, Nov  4 2015, 11:43:11)  
[GCC 4.4.7 20120313 (Red Hat 4.4.7-4)] on linux 
Type "help", "copyright", "credits" or "license" for more information. 
>>>import tensorflow as tf
>>> ( more code)
 
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For additional information, please contact Research Technology Services at tts-research@tufts.edu