Appendix B - Using GPU Servers on Lambda Labs

Most of the development for the examples in this book were done on a leased server with a 48G VRAM Nvidia A6000 GPU.

You can sign up and use Lambda Labs using this link.

Here I assume that you are familiar with both Linux and setting up and using VPSs.

If you start from scratch configuring a GPU VPS it can take 15 or 20 minutes. I reduce this setup time by using Lambda Labs new persistent file system. For this discussion, I assume that you have a file system called myfiles.

I create a directory myfiles/myssh where I copy in my GitHub access SSH keys. These files are password protected. I named these files id_github_lambdalabs but you can use whatever naming convention that you like.

When I SSH to a newly started VPS, I do the following:

 1 cd myfiles
 2 cp myssh/* ~/.ssh/
 3 
 4 eval ssh-agent $SHELL
 5 ssh-add ~/.ssh/id_github_lambdalabs
 6 # (Requires password)
 7 
 8 git config --global user.email "markw@markwatson.com"
 9 git config --global user.name "Mark Watson"
10 
11 nvidia-smi # check GPU availability and stats

I keep the above text in an Apple Note and I simply copy and paste it when first running on a GPU VPS.

Also, when you are done working remember to rm -r -f ~/.ssh before logging out and deleting your VPS instance.

I do much of my development work on remote leased servers so I have my own way of working remotely using SSH and Emacs. I usually always keep an inexpensive VPS without a GPU running and I add Mosh and tmux to SSH and Emacs and I find that I have a fluid development setup. I particularly like always having an inexpensive VPS available because I can instantly access it from my iPad or Chromebook while traveling or sitting outside in my yard.