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nvidia/cuda-x-data-science/README.md
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@ -29,7 +29,7 @@ You will accelerate popular machine learning algorithms and data analytics opera
## Time & risk ## Time & risk
* **Duration:** 20-30 minutes setup time and 2-3 minutes to run each notebook. * **Duration:** 20-30 minutes setup time and 2-3 minutes to run each notebook.
* **Risk level:** * **Risks:**
* Data download slowness or failure due to network issues * Data download slowness or failure due to network issues
* Kaggle API generation failure requiring retries * Kaggle API generation failure requiring retries
* **Rollback:** No permanent system changes made during normal usage. * **Rollback:** No permanent system changes made during normal usage.
@ -42,19 +42,18 @@ You will accelerate popular machine learning algorithms and data analytics opera
- Create Kaggle API key using [these instructions](https://www.kaggle.com/discussions/general/74235) and place the **kaggle.json** file in the same folder as the notebook - Create Kaggle API key using [these instructions](https://www.kaggle.com/discussions/general/74235) and place the **kaggle.json** file in the same folder as the notebook
## Step 2. Installing Data Science libraries ## Step 2. Installing Data Science libraries
- Use the following command to install the CUDA-X libraries (this will create a new conda environment) Use the following command to install the CUDA-X libraries (this will create a new conda environment)
```bash ```bash
conda create -n rapids-test -c rapidsai-nightly -c conda-forge -c nvidia \ conda create -n rapids-test -c rapidsai-nightly -c conda-forge -c nvidia \
rapids=25.10 python=3.12 'cuda-version=13.0' \ rapids=25.10 python=3.12 'cuda-version=13.0' \
jupyter hdbscan umap-learn jupyter hdbscan umap-learn
``` ```
## Step 3. Activate the conda environment ## Step 3. Activate the conda environment
- Activate the conda environment
```bash ```bash
conda activate rapids-test conda activate rapids-test
``` ```
## Step 4. Cloning the playbook repository ## Step 4. Cloning the playbook repository
- Clone the github repository and go the assets folder place in cuda-x-data-science folder - Clone the github repository and go the assets folder place in **cuda-x-data-science** folder
```bash ```bash
git clone https://github.com/NVIDIA/dgx-spark-playbooks git clone https://github.com/NVIDIA/dgx-spark-playbooks
``` ```
@ -63,12 +62,12 @@ You will accelerate popular machine learning algorithms and data analytics opera
## Step 5. Run the notebooks ## Step 5. Run the notebooks
There are two notebooks in the GitHub repository. There are two notebooks in the GitHub repository.
One runs an example of a large strings data processing workflow with pandas code on GPU. 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 - Run the **cudf_pandas_demo.ipynb** notebook and use `localhost:8888` in your browser to access the notebook
```bash ```bash
jupyter notebook cudf_pandas_demo.ipynb jupyter notebook cudf_pandas_demo.ipynb
``` ```
The other goes over an example of machine learning algorithms including UMAP and HDBSCAN. 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 - Run the **cuml_sklearn_demo.ipynb** notebook and use `localhost:8888` in your browser to access the notebook
```bash ```bash
jupyter notebook cuml_sklearn_demo.ipynb jupyter notebook cuml_sklearn_demo.ipynb
``` ```