Deep Learning Frameworks:
Caffe2, Cognitive toolkit, MXNet, PyTorch, TensorFlow and others rely on GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training
We have finished building Deep Learning Dev Box Stack v8.0 which now supports Ubuntu 16.04. We can ship all new deep learning machines with Ubuntu 16.04. The new install also includes both python 2.7 and python 3 side by side with all packages installed for both python 2.7 and python 3. Tensorflow is also setup so it is easy to upgrade.
So lots of improvements for
CUDA v8.0, v9.0, v9.1, v9.2 & 10.0
NVIDIA Driver v410.79
Google Tensor Flow v1.2.1
NVIDIA Digits 6.0
Deep learning is part of the machine learning methods that use one of a set of algorithms to learn high-level representations of data. Such algorithms have been successfully applied to a large variety of problems ranging from image classification, to natural language processing and speech recognition.
Graphics processing units (GPU) have provided groundbreaking performance to accelerate deep learning research with thousands of computational cores and up to 100x application throughout when compared to central processing units (CPU) alone. LinuxVixion, S.L., in collaboration with Amber-MD development team has developed the Deep Learning Workstation, featuring NVIDIA GPU technology, for developers to get started with deep learning research now.
The NVIDIA Deep Learning GPU Training System (DIGITS) is an interactive deep learning development tool for scientists and researchers to quickly design deep neural networks (DNN) using real-time network behavior visualization. DIGITS is a complete system for researchers to get started with developing an optimized neural network for a single data set or training multiple networks on many data sets.
With pre-installed NVIDIA DIGITS, as well as other leading deep machine learning software packages, LinuxVixion Deep Learning GPU Solutions are fully turn-key and designed for rapid development and deployment of optimized deep neural networks with multiple GPUs
All NVidia GeForce systems are fully validated for numerical correctness using custom validation suites.