Jupyter at OLCF

Jupyter is a powerful tool enabling reproducible research and teaching. Use it to create notebooks that contain both computer code and rich text elements (paragraphs, equations, figures, widgets, links). This allows you to create human-readable documents containing executable data analytics components, results, and descriptions.


JupyterLab is a web-based interactive development environment for Jupyter. It provide a way to use notebooks, text editors, terminals, and custom components together. You can configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning.

The OLCF JupyterHub implementation will spawn you into a single-user JupyterLab environment.

Jupyter Hub

JupyterHub is the best way to serve Jupyter Labs for multiple users within a project. It is a multi-user Hub that spawns, manages, and proxies multiple selectable instances of the single-user JupyterLab server.

JupyterHub within OLCF works by first authenticating each user using NCCS LDAP authentication. The Hub will offer each user a selection of Lab images to launch, which will then be spun up automatically. Upon successfull authentication, your UID/GID/Groups are queried and built into the deployment of your personal JupyterLab. In addition, you will have access to your standard OLCF storage space(s) on NFS and Lustre.


All members of currently enabled OLCF projects have access to the OLCF JupyterHub and JupyterLab.

OLCF Moderate JupyterHub (for Moderate Security Enclave projects): https://jupyter.olcf.ornl.gov/

OLCF Open JupyterHub (for Open Security Enclave projects): https://jupyter-open.olcf.ornl.gov/

The above links will present you with a page similar to below.

  • When using the Moderate JupyterHub, please login with your OLCF Moderate account and PASSCODE.

  • When using the Open JupyterHub, please login with either your OLCF Open account credentials (accepts both UCAMS and XCAMS accounts).


After succesfull authentication you will be presented with a choice of JupyterLab images (similar to the image below):

  • CPU-Only Lab

  • GPU-Lab (currently only available to Moderate Security Enclave projects)


These GPU-enabled labs are limited. You may get get a message saying the resource is not schedulable, if all GPUs in the pool are occupied.


Select the lab you would like and click “Start”.

The CPU Lab provides a single default notebook/environment in the “launcher” page of the Jupyterlab interface. The GPU Lab provides options for CUDA 10 and CUDA 11 environments.

You can read about the environments more, below, in the “Conda Environments” section. In addtion, that section will show you how to create your own Conda Environments.

Once inside the JupyterLab, please take a moment to explore the interface.

To read more about the JupyterLab interface, in general, please check out this wonderful documentation: JupyterLab Docs

CPU vs. GPU JupyterLab (Available Resources)

Hardware Resources

Each CPU Lab spawned by OLCF’s Moderate JupyterHub gets these default resources:

  • 32 CPUs

  • 32GB Memory

  • NCCS filesystem access (Lustre and NFS)

Each GPU Lab gets the following resources:

  • 16 CPUs

  • 32GB Memory

  • Nvidia V100 GPU

  • NCCS filesystem access (Lustre and NFS)


You have the same filesystem access as if you were on Frontier, to both NFS and Lustre, as you will be working under your standard OLCF UID.

Each CPU Lab spawned by OLCF’s Open JupyterHub gets these default resources:

  • 8 CPUs

  • 24GB Memory

  • NCCS Open filesystem access (GPFS and NFS)

Each GPU Lab gets the following resources:

  • Currently unavailable


You have the same filesystem access as if you were on Ascent, to both NFS and GPFS.

Software and Libraries

Both CPU and GPU labs have the standard analysis and ML libraries: PyTorch, TensorFlow, Pandas, NumPy; and visualization libraries: Bokeh, Jax, Matplotlib, OpenCV. To see the full list of installed libraries, open a Console from the Launcher page and type in conda list. These libraries should cover most use cases. You can also find instructions for setting up a custom conda environment for use with JupyterLab further down.

The GPU lab provides two different environments, CUDA10 and CUDA11. Both the CUDA10 and CUDA11 environments provide GPU support for PyTorch, CuPy, and CudNN. GPU support for Tensorflow is currently only available in the CUDA10 environment. Tensorflow only has CPU support in the CUDA11 environments. The image below shows the CUDA options in the Launcher page.


Working within Lustre and NFS (Launching a notebook)

To see the root of your filesystem access, within your JupyterLab interface, click this circled folder (you can traverse to your users spaces from there):


You should see lustre and ccs - the “top” of Lustre and NFS respectively.

Then, you can start a notebook in the directory of your choosing (relative to your user access).

To do so, traverse the filesystem to your desired path and then click the “Python 3” notebook in the launcher page. This will launch the default notebook environment and store the notebook in your current path.


Any notebooks saved in the root location won’t be persisted across sessions. Make sure you are saving your work in a location in /ccs or /lustre where you have write access.

In the example image below, I have launched a notebook in my /ccs/proj/<proj>/<uid>/ directory (the notebook filename is “Untitled.ipynb” - you can rename yours):


Another method of getting to the filesystem path of your choosing is selecting File->”Open from Path” and typing the desired path in the text box:


Conda environments and custom notebooks

From the Console of a particular environment, you can install additional libraries with a simple conda install to use in that particular environment in the current session. But these installed libraries won’t persist across sessions if your server is restarted.

Setting up your own custom Conda environment is useful when the base environment doesn’t provide what you need and you want your installed libraries to persist across sessions. These custom Conda environments need to be saved in a /ccs or /lustre/orion directory.


Please note that GPFS and Lustre are purged.

Example: Creating a Conda environment on Jupyter


Conda environments created using the below method are only usable in JupyterLab. You can’t create an environment within JupyterLab and use these environments on other machines like Frontier or Andes to run jobs. You will need to recreate the environment separately on those machines. Alternatively, See our Jupyter Visibility Guide for details on how to make your Fronter/Andes environments visible to Jupyter – which we highly recommend doing instead.

In this example, we will create a simple environment that installs NumPy. Although this example installs NumPy, the instructions below can still be followed to create a desired environment on Jupyter:

  1. Launch a Jupyter Lab from https://jupyter.olcf.ornl.gov

  2. From the JupyterLab Launcher page, click on the Terminal option.

  3. Next, create your conda env:

    conda create -p /path/to/your/test_env python=3.10 numpy
  4. Activate your new environment:

    source activate /path/to/your/test_env
  5. Install ipykernel (will let Jupyter “see” your environment):

    conda install ipykernel
  6. Use ipykernel to expose your new environment to Jupyter:

    python -m ipykernel install --user --name test_env --display-name test_env

    If successful, you should see something like Installed kernelspec test_env in ~/.local/share/jupyter/kernels/test_env

After following the above steps, you should now be able to use your environment “kernel” in a Jupyter Notebook. When you refresh the page (or open a new Jupyter Launcher tab), you will see new options labeled as your Conda environment name you passed to ipykernel.

To use your custom environment in a Notebook, you can either click on the new options to launch a new Jupyter Notebook with that custom kernel, or you can switch kernels in existing Notebooks by clicking on the “Kernel” menu option and then select “Change Kernel” while in an existing Notebook.


Note that you can always install more libraries into your Conda environment as needed by opening the Terminal in JupyterLab again, activating your environment with source activate and doing conda install or pip install.

To delete your environment, you will need to delete it from the path where the environment was created, as well as delete the corresponding directory from ~/.local/share/jupyter/kernels.

Manually stopping your JupyterLab session

All JupyterLab sessions expire and termninate if they are left idle for an 1-hour.

If you would like to terminate your session manually though, to select a different JupyterLab image for instance (going from CPU to GPU-enabled, or vice versa), you can do so by going to File -> Hub Control Panel -> Stop Server.

This will take a few seconds to shutdown, then you can restart and reselect a provided JupyterLab image from the menu.

Things to be aware of

  • All notebooks have an idle time limit of 1-hour. After 1-hour of idle time, your JupyterLab session will terminate. You may restart your session though.

  • To persist a notebook and conda environment, it is highly recommended to use your NFS project space (/ccs/proj/<project-id>/<uid>/).

  • The GPU-labs are limited resources. There is no guarantee of a GPU being readily available for JupyterLab. Please use the more readily accessible CPU-Labs, unless you absolutely need a GPU.

Example Jupyter Notebooks

Please check out our OLCF Jupyter-Examples repository on GitHub.