The Bright for Deep Learning solution, available on Bright Cluster Manager Version 7.3, provides a choice of machine learning frameworks to simplify deep learning projects, including Caffe, Torch, TensorFlow, and Theano. In addition, the Bright solution includes several of the most popular machine learning libraries to help access datasets, such as MLPython, NVIDIA CUDA Deep Neural Network library (cuDNN), Deep Learning GPU Training System (DIGITS), and CaffeOnSpark, the open sourced solution for distributed deep learning on big data clusters.
We have enhanced Bright Cluster Manager 7.3 so our customers can quickly and easily deploy new deep learning techniques to create predictive applications for fraud detection, demand forecasting, click prediction, and other data-intensive analyses,” said Martijn de Vries, Chief Technology Officer of Bright Computing. “Going forward, customers using Bright to deploy and manage clusters for deep learning will not have to worry about finding, configuring, and deploying all of the dependent software components needed to run deep learning libraries and frameworks.”
Bright Cluster Manager Version 7.3 includes Python modules that support machine learning, plus the NVIDIA hardware drivers, CUDA (parallel computing platform API) library, CUB (CUDA building blocks), and NCCL (library of standard collective communication routines). Bright also provides and installs environment modules that make it easy to dynamically modify the user environment to use all of the provided machine learning components. Deep learning applications can also be scaled beyond a single machine with the CaffeOnSpark package, spreading the processing across an entire cluster for better performance.
Many Fortune 500 and Forbes 2000 firms will need to apply deep learning to their big data workflows,” said Steve Conway, IDC research vice president for high performance computing. “Enterprises that do this ahead of their competitors stand to gain a major competitive advantage.”
If users need more capacity, Bright’s cloud bursting capacity lets them extend GPU-enabled instances into the cloud. Bright also makes it easy to run deep learning applications in containers or in a private OpenStack cloud. Users can even take advantage of the performance benefits of modern clusters with RDMA-capable interconnects by running a deep learning application using RDMA-Spark.
To read more about Bright’s deep learning solution, go to www.brightcomputing.com/deep-learning.