Jupyter Hub

Jupyter Status

Released and accessible to all HPC Users at the correct URLs.

Jupyter Overview

The Jupyter Enterprise Gateway is a multi-user environment for Jupyter Notebooks. DeepThought has integrated the Jupyter Gateway to allow users to run jobs on the cluster via the native Web Interface.

If you have access to the HPC, you automatically have access to the Jupyter Lab. You can the JupyterLab Instance via the following Jupyter URL or manually via https://deepweb.flinders.edu.au/jupyter. Your credentials are the the same as the HPC, your FAN and password.

If you are a student with access to the HPC, the above URLs may work - the URL http://deepteachweb.flinders.edu.au/jupyter is guaranteed to work correctly.

Using Conda Environments in Jupyter Hub

You can use your own custom environment in a Jupyter Kernel. There are some restrictions on python versions that must be adhered to for this integration to work correctly. You will also need to install some specific packages. This process of enabling a Conda environment to work with the HPC, SLURM and Jupyter Hub is detailed below.

Python Version Restrictions

  • Python Version <= 3.9.

Python 3.10 will not work, due to dependency incompatibility. Specifically, there was a change in the PyCrypto and HTML packages.

Conda Environment Preparation

  1. If you have not performed an initial conda init bash, issue module load Miniconda3 followed by conda init bash, the log-out and log-in the HPC.
  2. Ensure you have a named Conda environment that is loadable by conda activate <ENVIRONMENT_NAME>
  3. Ensure that the python version used by the environment is Python 3.9 or less.’
  4. Log onto the HPC via SSH and activate your Conda environment conda activate <ENVIRONMENT_NAME> (You can also use the JupyterHub ‘Terminal’ to do this)
  5. Execute the following commands:
    1. python3 -m pip install cm-jupyter-eg-kernel-wlm bash-kernel
    2. conda install ipython ipython_genutils
  6. Log into Jupyter Hub with your FAN and password at the Jupyter URL
  7. Using the bar on the Left-hand side, select small green symbol
  8. Create a new Kernel based on the ‘CONDA via SLURM’ template. a. Ensure you select any additional modules you need, like GDAL b. Ensure that you select the correct Conda environment to initialise
  9. Use the Kernel in your Jupyter Notebooks

For Reference, the below image shows the Kernel Template Screen.

Jupyter Hub Kernel Template Screen

Tensorflow / GPU Usage via Jupyter Hub

Conda Environment & Tensorflow Install

It possible to use the GPU nodes and access the GPU’s via the JupyterHub Interface. In this example, Tensorflow is used as the GPU-Enabled package of choice. To access the GPUs from your Conda environment and use Tensorflow, perform the following steps:

  1. Follow the ‘Conda Environment Preparation’ steps up to step 4.
  2. Run the following commands to install Tensorflow. DO NOT use conda install tensorflow, as it has issues with GPU detection. You must use pip.
    1. python3 -m pip install tensorflow

There are a few known issues that can occur here, however the main on is solved below:

  1. An error about HTML has not attribute ‘parser’ or similar. This is usually resolved by upgrading pip.
    1. python3 -m pip install --upgrade pip

Try and re-install Tensorflow as above. If it still fails, upgrade the rest of the installation tooling for python:

  1. python3 -m pip install --upgrade setuptools
  2. python3 -m pip install --upgrade distlib

And once again attempt to Install Tensorflow. If you are still having issues, then reach out to deepthought@flinders.edu.au

Now that the environment is up and running, create a new Kernel Templace from the JupyterHub GUI. For CUDA access you must load the following modules. Alter the version numbers as needed.

  • cuda1X.X/toolkit
  • cuDNN

If you do not add these modules to the modules list, then you will not be able to use CUDA processing, even if you allocate yourself GPUs on the cluster.

All done! You should now be able to run the following commands from your Jupyter notebook and have Tensorflow return back GPU’s in its visible devices list and return a Tensor.

  • python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
  • python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"