CUDA Deep Neural Network (cuDNN) is a library from NVIDIA that provides the GPU-accelerated primitives for deep learning such as convolution, pooling, normalization, activation layers, tensor transformation. With cuDNN, a machine learning researcher or developer can spend less time writing the implementation for low-level GPU performance tuning. The cuDNN library powers major deep learning frameworks such as Caffe, Caffe 2, Tensor Flow, Cognitive Toolkit and PyTorch.
This post summarizes the steps to install cuDNN 7 for Cuda Toolkit 9.1 on Ubuntu 16.04. Installation for different version of cuDNN and Cuda Toolkit may require additional tweak or different steps that are not covered in this post. Continue reading
With advances in GPU technologies, performing complex computation is not an exclusive feat of multicore CPUs anymore. It is not uncommon to perform computation for linear algebra, image and video processing, machine learning (especially deep learning), graph analytics, and so forth on GPU.
NVIDIA graphic cards have gained popularity among machine learning researchers and practitioners as the base hardware for GPU computing. To harness the GPU power, NVIDIA develops and provides CUDA toolkit that can be used as the development environment and libraries for GPU-accelerated applications.
If you are using Ubuntu 16.04 (Xenial) and want to install the recent release of CUDA toolkit (version 9.1), this post may help. The official installation guide is available at the NVIDIA website and can be referenced when following the steps outlined in this post. Continue reading
Docker is a popular option for “virtual appliances” nowadays. Docker website defines the “Docker” moniker as a software containerization platform that is more flexible than traditional virtual machines since it virtualizes the operating system instead of the hardware. This means that Docker can further isolate the resources by enabling multiple Docker containers running on an operating system. If the operating system itself is running on a virtual machine either via full virtualization (bare metal) or paravirtualization, we will have a good example of IT infrastructure transformation from asset-ownership model into cloud-lease model (my related paper about such transformation can be read here). Continue reading