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DHLAB and LSHT student projects - Technical specifications 🛠️

👉 For information on generic organisation of projects, see these slides (accessible with EPFL gdrive login).

Sections:

GitHub repository:

Naming:

  • use lower case;
  • use hyphens to separate tokens;
  • if related to a larger project, start with the name of this project, followed by the name of your project (e.g. impresso-image-classification);
  • in case of doubt, ask your supervisors;

Structure:
You are free to structure your repository as you wish, but we advise to have:

  • a notebooks folder, for your working notebook;
  • a lib folder, in case you convert your notebook in scripts with a command line interface;
  • a report folder, where you put the PDF and latex sources of your report;
  • a README, with the information specified below.

What should be in your README:

  • Basic information

    • your name
    • the names of supervisors
    • the academic year

  • About: include a brief introduction of your project.

  • Research summary: include a brief summary of your approaches/implementations and an illlustration of your results.

  • Installation and Usage

    • dependencies: platform, libraries (for Python include a requirements.txt file)
    • compilation (if necessary)
    • usage: how to run your code

  • License
    We encourage you to choose an open license (e.g. AGPL, GPL, LGPL or MIT).
    License files are already available in GH (add new file, start typing license, choices will appear).
    You can also add the following at the end of your README:

      project_name - Jean Dupont    
  Copyright (c) Year EPFL    
  This program is licensed under the terms of the [license].

Working with a remote EPFL server

If necessary your lab (DHLAB or LHST) can grant you access to a machine on the IC cluster:

  • ask your supervisor to gain access;
  • you need to be on campus or use VPN to access the machine;
  • login with gaspar credentials: ssh [gasparname]@iccluster0XX.iccluster.epfl.ch where 'XX' is the machine number
  • the node usually has
    • 256GB of RAM,
    • 2 GPUs
    • 200GB of disk space on /
    • 12TB of disk space under scratch
  • ⚠️ important: the machine is shared and given the small size of /, do not store your data (i.e. the data you work with, intermediary results, models, various resources, etc.) in your home but under scratch/students where you can create your own folder.

Attention! When first connecting to the cluster node with ssh, you will get the follwing message:

The authenticity of host 'iccluster0XX.iccluster.epfl.ch (XX.XX.XX.XX)' can't be established.
ECDSA key fingerprint is SHA256:XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX.
Are you sure you want to continue connecting (yes/no/[fingerprint])?

Write yes and press ENTER to establish a first connection.

For people using Python on a cluster node

  • create a local python environment using conda, virtualenv or pipenv.
  • Note: Before creating environments, you need to create a $USER folder on /scratch/students/$USER (mkdir /scratch/students/$USER and replace $USER with your username). Alternatively use /scratch/$USER/ (mkdir /scratch/$USER) if you have the folder permission.
  • to easily code locally and run things remotely, configure your IDE to save your code on the remote server as you code (e.g. with PyCharm, Visual Studio Code).

conda

Create a user folder in /scratch/students/ or /scratch/ (see note above) and configure conda so that environments (and packages) are stored there instead of /home/:

conda config --add envs_dirs /scratch/students/$USER/.conda/envs
conda config --add pkgs_dirs /scratch/students/$USER/.conda/pkgs

Moreover, it is good practice to regularly clean your conda packages: conda clean --all

To create an environment:

conda create -n ENV_NAME python=X.XX anaconda

For example, to create an environment with python 3.10:

conda create -n py310 python=3.10 anaconda

In this example, the name of the environment is the same as the python version, but it may have any name (depending on your needs).

To activate an environemnt: source activate ENV_NAME
To deactivate an environemnt: source deactivate ENV_NAME

virtualenv

virtualenv is currently configured for Python 3.8. See in the optional section how to configure it for Python 3.11.

Let's create and activate a new environment inside a new folder (e.g., testenv) in your /scratch/students/ or /scratch/ user folder (see note above):

virtualenv /scratch/students/$USER/testenv
source /scratch/students/$USER/testenv/bin/activate

Inside the environment, you can install packages (e.g., torch) via pip. To avoid getting an out of memory error, you can create and provide a temporary folder (e.g., tmp):

mkdir /scratch/students/$USER/tmp
export TMPDIR=/scratch/students/$USER/tmp/ 

pip install torch

Optional

If you like to configure virtualenv for Python 3.11 (i.e., available via python/python3), you need to install it first in another directory (e.g., packages):

mkdir /scratch/students/$USER/packages
python -m pip install virtualenv --target /scratch/students/$USER/packages

export PYTHONPATH=/scratch/students/$USER/packages:$PYTHONPATH
alias virtualenv=/scratch/students/$USER/packages/bin/virtualenv

Note that you need to re-enter the last two commands every time you start a new session.

pipenv

Note: the following procedure for pipenv has not been thoroughly tested, if you test it and there are other steps, please update this document via a pull request

To ensure you are not using /home, there are two things to do: 1) ensure your pipenv environments are not installed in /home, 2) ensure pip's temporary directory (where pip downloads the files before loading them into the environment) is not on /home.

  1. Create the temporary directory mkdir /scratch/students/$USER/.pipenv_tmpdir
  2. Add the following lines to your /home/<user>/.bashrc/file:
export PIPENV_VENV_IN_PROJECT=1 # tells pip to create the environment in the folder where you're creating it.
export TMPDIR="/scratch/students/$USER/.pipenv_tmpdir" # tells pip to use this folder as the temporary directory

Some sources: temporary directory, create pipenv in current directory

How to access a notebook on a remote server

In order to access an instance of Jupyter notebook or Jupyter Lab running on a remote server, you can either configure Jupyter accordingly or use a SSH tunnel.

Configuring Jupyter notebook for remote access

Have a look at the official Jupyter documentation.

In summary, you have to:

  • Run jupyter notebook --generate-config. This will create a .jupyter folder in your home (hidden, use ls -a).
  • Use jupyter notebook password to set a password.
  • Edit the jupyter_notebook_config.py file in order to set the port where jupyter will broadcast. The following three lines are needed, all the rest can be commented:
c = get_config()
c.NotebookApp.ip = '0.0.0.0'
c.NotebookApp.port = XXX <= change this port; use the last four digits of your SCIPER number (to avoid colliding with other people on the same port).

You can ignore SSL certificates.

If you run jupyter notebook the notebook will start and be accessible at http://iccluster0XX.iccluster.epfl.ch:XXXX (you need to enter your password)

In order to leave it open while you are executing things, you can run the notebook in screen (see below).

Using a SSH tunnel

You can also use a SSH tunnel, which is easier, but somewhat more brittle (you will need to reconnect the SSH tunnel when you lose the connection, e.g. because you suspended your machine).

  1. Connect to the remote server setting up a SSH tunnel. Run the following command from your local machine. As the port number XXXX, use the last four digits of your SCIPER number (to avoid colliding with other people on the same port):
    ssh -L XXXX:localhost:XXXX [gasparname]@iccluster0NN.iccluster.epfl.ch
  2. Launch Jupyter notebook or lab on the node (again replacing XXXX with the same port number):
    jupyter notebook --no-browser --port=XXXX

Your notebook is now accessible at https://localhost:XXXX. You may need a token, so look at the message given by Jupyter notebook / lab when you run it.

In order to leave it open while you are executing things, you should run the notebook in a screen (see below).

How to create another shell session and detach from it: use screen

Main commands:

  • create a session: screen -S name_of_the_session => you are in
  • "detach" from screen session: Ctrl-A D => session still running, you are out
  • reconnect (reattach) to a session: screen -r name_of_the_session
  • kill a session: from within the session, Ctrl-A K
  • in case you are reconnecting from inside (by mistake): screen -rd name_of_the_session
  • to list all active screens: screen -ls

Documentation:

Steps to work with a notebook in a screen:

  • cd [your repo]
  • screen -S work => you are in a screen named "work" where you will launch the notebook
  • activate your env
  • start Jupyter notebook (jupyter notebook, or jupyter notebook --no-browser --port=XXXX if you use a SSH tunnel)
  • open the URL in your web browser
    • http://iccluster0XX.iccluster.epfl.ch:XXXX if you configured remote access
    • http://localhost:XXXX if you use a SSH tunnel
  • if everything is ok then detach the screen (Ctr-a d). You can now work in the notebook, open and close your browser as you want, it will keep running.

Troubleshooting:

  • Half-point floating precision (FP16, dtype=torch.bfloat16) is not supported by the GPU. Please use normal floating precision (FP32) instead.