Both Google and Microsoft have made open-source deep-learning toolkits available for download. TechEnablement.com also has a github repository containing our machine-learning teaching code that achieved 13 PF/s average sustained performance on the ORNL Titan supercomputer.
Google’s TensorFlow™ is an open source software library for numerical computation using data flow graphs one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. It only runs on a single machine. Originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for machine learning and deep neural networks research, but Google claims the system is general enough to be applicable in a wide variety of other domains as well.
Microsoft has released a similar project called DMLT (Distributed Machine Learning Toolkit). DMLT’s core is a C++ SDK for a client-server architecture, but the goal is to make it easier for data scientists to perform model training across multiple machine nodes. Both MPI and ZMQ distributed communications APIs are supported.