This list is meant to be a both a quick guide and reference for further research into these topics. It's basically a summary of that comp sci course you never took or forgot about, so there's no way it can cover everything in depth. It also will be available as a gist on Github for everyone to edit and add to.
The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.
I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).
So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht
| _complete_ssh_hosts () | |
| { | |
| COMPREPLY=() | |
| cur="${COMP_WORDS[COMP_CWORD]}" | |
| comp_ssh_hosts=`cat ~/.ssh/known_hosts | \ | |
| cut -f 1 -d ' ' | \ | |
| sed -e s/,.*//g | \ | |
| grep -v ^# | \ | |
| uniq | \ | |
| grep -v "\[" ; |