NVIDIA has updated its GPU-accelerated deep learning software to make training more efficient. The new software will empower data scientists and researchers to enhance their deep learning projects and product development work by creating more accurate neural networks through faster model training and more sophisticated model design.

Nvidia says that the NVIDIA DIGITS Deep Learning GPU Training System version 2 (DIGITS 2) and NVIDIA CUDA Deep Neural Network library version 3 (cuDNN 3) provide significant performance enhancements and new capabilities.

For data scientists, DIGITS 2 now delivers automatic scaling of neural network training across multiple high-performance GPUs. This can double the speed of deep neural network training for image classification compared to a single GPU.

For deep learning researchers, cuDNN 3 features optimized data storage in GPU memory for the training of larger neural networks. cuDNN 3 also provides higher performance than cuDNN 2, enabling researchers to train neural networks up to two times faster on a single GPU.

The new cuDNN 3 library is expected to be integrated into forthcoming versions of the deep learning frameworks Caffe, Minerva, Theano and Torch, which are widely used to train deep neural networks.

"High-performance GPUs are the foundational technology powering deep learning research and product development at universities and major web-service companies," said Ian Buck, vice president of Accelerated Computing at NVIDIA. "We're working closely with data scientists, framework developers and the deep learning community to apply the most powerful GPU technologies and push the bounds of what's possible."

DIGITS 2 is the first all-in-one graphical system that guides users through the process of designing, training and validating deep neural networks for image classification.

The new automatic multi-GPU scaling capability in DIGITS 2 maximizes the available GPU resources by automatically distributing the deep learning training workload across all of the GPUs in the system.

cuDNN is a GPU-accelerated library of mathematical routines for deep neural networks that developers integrate into higher-level machine learning frameworks.

cuDNN 3 adds support for 16-bit floating point data storage in GPU memory, doubling the amount of data that can be stored and optimizing memory bandwidth. With this capability, cuDNN 3 enables researchers to train larger and more sophisticated neural networks.

cuDNN 3 also delivers significant performance speedups compared to cuDNN 2 for training neural networks on a single GPU.

The DIGITS 2 Preview release is available today as a free download for NVIDIA registered developers.