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Are you struggling to use CUDA in Jupyter Notebook/Lab because of module loading issues? Learn how to properly load environment modules without sudo access.
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How to Load Environment Modules into Your Jupyter Notebook/Lab for CUDA Use

When working with Jupyter Notebook or JupyterLab on a remote server—especially in a cluster-computing environment—you might run into challenges when trying to use certain packages. One prevalent issue is loading the CUDA toolkit using environment modules. If you're like many users who don't have sudo access, you may find yourself in a situation where the terminal commands you rely on simply don't function within your Jupyter environment. Fear not, as we’re here to guide you through resolving these issues effectively.

Understanding the Problem

You’re running JupyterLab and need to use CUDA, but to do so, you must load it as a module with the module load command. However, attempts to execute this command directly in Jupyter using !module load cuda fails. This is primarily because Jupyter does not recognize the module command you would normally run in a bash terminal.

Additionally, the error message you encountered indicates that the necessary CUDA libraries (e.g., libcudart.so.9.2) cannot be found. This means that the paths to the CUDA installation directories must be correctly set in your environment variables to use the CUDA functions seamlessly in your Python scripts.

Step-by-Step Solution

Follow these steps to properly load your CUDA module into Jupyter Notebook/Lab.

1. Define the Shell Function

The first step is to initialize the module command by sourcing the necessary initialization script. This script is usually located in the following directory on Red Hat-like systems:

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In order to make the module command available in your Jupyter environment, you can run the following command in the Jupyter Notebook cell using the magical % syntax for shell commands:

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2. Verify Available Modules

Once you’ve sourced the module initialization script, you can check which modules are available for loading. This can be done by running:

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This command will list all the modules currently available for you to load. If cuda appears in this list, you’re one step closer to utilizing it in your Jupyter environment.

3. Load the CUDA Module

If you can see the cuda module listed from the previous step, you can then proceed to load it by executing the following command:

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4. Set Environment Variables (If Necessary)

In some cases, you may also need to set some specific environment variables manually if they’re not automatically configured through loading the module. You can use the %env magic command in Jupyter to set environment variables:

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Make sure to replace /path/to/cuda/bin and /path/to/cuda/lib64 with the correct paths for your setup. This will help your Python scripts locate the required CUDA libraries.

Conclusion

With these steps, you should now be able to load the CUDA environment module within your Jupyter Notebook/Lab successfully. This approach allows you to configure your environment without needing administrative privileges. If you encounter further issues, double-check the paths or consult with your server administrator to ensure the modules are correctly set up on your system. Happy coding!