CUDA-X Data Science

Verify system requirements

• Verify the system has CUDA 13 installed using nvcc --version or nvidia-smi
• Install conda using these instructions
• Create Kaggle API key using these instructions and place the kaggle.json file in the same folder as the notebook

Installing Data Science libraries

Use the following command to install the CUDA-X libraries (this will create a new conda environment)

  conda create -n rapids-test -c rapidsai-nightly -c conda-forge -c nvidia  \
  rapids=25.10 python=3.12 'cuda-version=13.0' \
  jupyter hdbscan umap-learn

Activate the conda environment

  conda activate rapids-test

Cloning the playbook repository

• Clone the github repository and go the assets folder place in cuda-x-data-science folder
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    git clone https://github.com/NVIDIA/dgx-spark-playbooks
  

• Place the kaggle.json created in Step 1 in the assets folder

Run the notebooks

There are two notebooks in the GitHub repository. One runs an example of a large strings data processing workflow with pandas code on GPU.

• Run the cudf_pandas_demo.ipynb notebook and use localhost:8888 in your browser to access the notebook
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    jupyter notebook cudf_pandas_demo.ipynb
  

The other goes over an example of machine learning algorithms including UMAP and HDBSCAN.

• Run the cuml_sklearn_demo.ipynb notebook and use localhost:8888 in your browser to access the notebook
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    jupyter notebook cuml_sklearn_demo.ipynb
  

If you are remotely accessing your DGX-Spark then make sure to forward the necesary port to access the notebook in your local browser. Use the below instruction for port fowarding

  ssh -N -L YYYY:localhost:XXXX username@remote_host 

• YYYY: The local port you want to use (e.g. 8888)
• XXXX: The port you specified when starting Jupyter Notebook on the remote machine (e.g. 8888)
• -N: Prevents SSH from executing a remote command
• -L: Spcifies local port forwarding