Greg's House of Treats

clang-format

https://clang.llvm.org/docs/ClangFormat.html

autocomplete & more in emacs

https://github.com/Sarcasm/irony-mode

Check out all these different links to Stanford’s CS229 course. They seem to be different versions of the same course.

http://cs229.stanford.edu/syllabus.html

https://online.stanford.edu/courses/cs229-machine-learning

https://www.coursera.org/learn/machine-learning

http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=MachineLearning

https://see.stanford.edu/Course/CS229

MIT stuff

http://introtodeeplearning.com/

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/syllabus/

https://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/

MIT course references

https://docs.google.com/spreadsheets/d/1jtdtJHXZPbVSIT2xxF18OkQmwjfZrraAKai9e90mfeU/edit#gid=0

Discourse discussion

https://discourse.slicer.org/t/python-wrapping-of-plastimatch/6722/2

ITK module template

https://github.com/InsightSoftwareConsortium/ITKModuleTemplate

https://blog.kitware.com/python-packages-for-itk-modules/

https://itkpythonpackage.readthedocs.io/en/latest/Build_ITK_Module_Python_packages.html

Relevant section of the ITK software guide

https://itk.org/ITKSoftwareGuide/html/Book1/ITKSoftwareGuide-Book1ch3.html#x39-440003.7

https://itk.org/ITKSoftwareGuide/html/Book1/ITKSoftwareGuide-Book1ch9.html#x55-1520009.5

Consider also

https://github.com/pybind/pybind11

https://scikit-build.readthedocs.io/en/latest/

The hardware on sherbert is a Titan XP, compute capability 6.1

https://developer.nvidia.com/cuda-gpus

Currently we have package: nvidia-driver (390.116-1)

Backports has a package nvidia-driver (418.56-2~bpo9+1).  This could be installed to support CUDA 10.1.

https://packages.debian.org/stretch-backports/nvidia-driver

Debian stable/backport has only package nvidia-cuda-dev (9.1.85-8~bpo9+1)

This is not acceptable, because cuDNN does not support this version

Instead, install NVIDIA CUDA 9.0.176 for compatibility with cuDNN

https://docs.nvidia.com/deploy/cuda-compatibility/

cuDNN 7.6.0

https://docs.nvidia.com/deeplearning/sdk/cudnn-support-matrix/index.html

We therefore want Tensorflow 1.12.x

https://www.tensorflow.org/install/source

Which can be installed from package

pip install tensorflow-gpu==1.12.2

To test, run the following

import tensorflow as tf
print (tf.test.is_built_with_cuda())
print (tf.test.is_gpu_available(False,6.1))

There will be an exception for non-cuda available GPUs, so limit the available device as this:

CUDA_VISIBLE_DEVICES=0 python tftest.py