Thanks Morgan,
It looks like TAO is running properly now. Just for my own information, and in case anyone else is reading this thread, there were a few additional steps I did.
First, when running performing the $docker run command, I mapped another volume using -v. This /home/porter/test location is where I saved my data and cloned the github repository to. I also renamed the container from webinar to taotest (this wasn’t necessary, I just did this for my own naming convention). So the command I ran was:
$ docker run --runtime=nvidia -it --rm --ipc=host --gpus all --name taotest -d -v /localhome/local-xxx:/localhome/local-xxx -p 8888:8888 -v /var/run/docker.sock:/var/run/docker.sock -v /home/porter/test:/home/porter/test -m 1100G --oom-kill-disable --ulimit memlock=-1 nvcr.io/nvidia/tao/tao-toolkit:5.5.0-pyt
After I ran:
$ docker exec -it taotest /bin/bash
Once I was in the container, I ran the other commands Morgan specified:
apt-get install sudo
sudo apt-get update
sudo apt install docker.io
sudo ls -la /var/run/docker.sock
$docker login nvcr.io
Username: $oauthtoken
Password: (specifying your own password)
I then navigated to my mapped volume of /home/porter/test, and cloned the github repository:
cd
cd /home/porter/test
git clone https://github.com/NVIDIA/tao_tutorials
I then triggered the notebook by running:
jupyter notebook --ip 0.0.0.0 --allow-root
Then opening the browser at http://127.0.0.1:8888
Then enter the token generated in your terminal:
http://hostname:8888/?token=a6e78bada2a5i203e5b63xaad5554fe39bxe9382f23b329a
Hope this helps anyone else who’s running into this issue!