TensorFlow Engineering with CUDA GPU for Deep Learning

See also:

• 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.Manually download “cuDNN v6.0 Library for Linux”.
2.Bash auto Install CUDA in Ubuntu 16.04.2, can combine (&& \) with code below.
3.Install cuDNN, PIP:

CUDA Install

see also:
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.