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GPU server for machine learning

Install necessary packages

sudo apt update
sudo apt -y upgrade
sudo apt -y install build-essential gcc g++ make binutils
sudo apt -y install software-properties-common git
sudo apt -y install cmake pkg-config

Allow SSH UDP

Replace with your actual SSH port sudo ufw allow 22/udp comment "ML" and sudo ufw reload

Nvidia and Cuda

This is just in case you need gpu accelerated encoders or decoders for video server. Remove the nouveau kernel then download and install cuda and the driver. Check version nvcc --version.

sudo apt install nvidia-driver-455
sudo apt install cuda

Add the following to the end of .bashrc and source .bashrc

export CUDA_PATH=/usr
export PATH=$PATH:/usr/local/cuda-11.1/bin

Python

sudo apt update
sudo apt install python3-pip python3-dev python-is-python3
sudo -H pip3 install virtualenv

Create ML user

sudo adduser mlgpu
sudo usermod -a -G sudo mlgpu
mkdir .ssh
chmod 700 .ssh/
touch .ssh/authorized_keys
chmod 600 .ssh/authorized_keys
ssh-import-id gh:thebeachlab

If you want to disable 2FA for this user, edit sudo nano /etc/pam.d/sshd and add

auth [success=done default=ignore] pam_succeed_if.so user ingroup mlgpu

before auth required pam_google_authenticator.so. Make sure you reload the ssh daemon sudo service sshd restart

Check the connection ssh -p 22 [email protected]

Install jupyterlab and pytorch

su mlgpu
pip3 install torch torchvision

Check that pytorch with cuda is accessible

mlgpu@thebeachlab:~$ python
Python 3.8.5 (default, Jul 28 2020, 12:59:40)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print (torch.rand(5,3))
tensor([[0.8937, 0.2411, 0.1159],
        [0.9376, 0.5696, 0.0137],
        [0.7617, 0.7618, 0.3687],
        [0.1805, 0.9064, 0.2470],
        [0.9646, 0.5219, 0.2525]])
>>> torch.cuda.is_available()
True

Install jupyterlab pip3 install jupyterlab ipywidgets

Note: Multiple warnings about /home/ml/.local/bin not in your lab.

Open a remote jupyterlab session

From your laptop ssh -p 22 -CL 8899:localhost:8899 [email protected] or add an ml alias to connect to the server and then start jupyter lab

  • -C for data compression
  • -L listen-port:host:port for port forwarding

jupyter lab --no-browser --port=8899 or jl if you create an alias jl="jupyter lab --no-browser --port=8899 in the mlgpu .bash_aliases

Then in your laptop browser open the notebook with the provided token:

http://localhost:8899/?token=LOTS-OF-NUMBERS-AND-LETTERS

To access without token generate a config file jupyter lab --generate-config and set a password jupyter notebook password. Then modify nano ~/.jupyter/jupyter_notebook_config.py to set an empty token c.NotebookApp.token = ''

Mount remote folder via SSHFS

In your laptop install sshfs, then add a fuse group and add yourself to that group

[unix ~]$ sudo groupadd fuse
[unix ~]$ sudo usermod -a -G fuse unix

Logout and login for the changes to apply. Now you can create the mount point and mount the mlgpu home folder

sudo mkdir /mnt/mlgpu
sudo sshfs -p 22 -o allow_other,workaround=rename,noexec,idmap=user,uid=$(id -u),gid=$(id -g),default_permissions,IdentityFile=/home/unix/.ssh/id_rsa [email protected]:/home/mlgpu /mnt/mlgpu

And you will see that the files are mounted as if you were the owner

[unix /mnt/mlgpu]$ ls -l
total 4.0K
drwxrwxr-x 1 unix users 4.0K Nov  6 10:54 data

And in the remote server

mlgpu@thebeachlab:~$ ls -l
total 4
drwxrwxr-x 3 ml ml 4096 Nov  6 09:54 data

Unmount when not needed sudo umount /mnt/mlgpu/