TF practical part in do deep learning How to setup Docker and Nvidia-Docker 2.0 on Ubuntu 18.04 Install # Summary: install CUDA first, then TF.
ref TF 1.0 doc
ref nvidia doc, until step 3
requirements # 64-bit Linux Python 2.7 or 3.3+ (3.5 used in this blog) NVIDIA CUDA 7.5 (8.0 if Pascal GPU) NVIDIA cuDNN >v4.0 (v5.1 recommended) NVIDIA GPU with compute capability >3.0 steps # 1.
ways of parallel computing in R
//TODO: study & summarize Map, Gather, Scatter etc.
//TODO: see also the notes for campus’ students assignments.
Coursera Course Notes: _GPU notes.docx
Code: github, or desktop search “gpu assignments”.
Compared with “knn-r-project.Rproj”, “knn_cuda.vcxproj” is at least 6k times faster for almost exactly the same job. (diff: data cleaned; windowSize added.) One reason is the R project was using data.frame instead of tada.table. See here for more R performance info.