sarman/dgx-spark-playbooks
1
0
Fork 0
mirror of https://github.com/NVIDIA/dgx-spark-playbooks.git synced 2026-04-22 18:13:52 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

chore: Regenerate all playbooks

Browse Source
This commit is contained in:
GitLab CI 2025-10-21 00:57:26 +00:00
parent 752eada0cb
commit 11f2a77ea7
1 changed files with 5 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
nvidia/cuda-x-data-science/README.md
View File

@ -29,7 +29,7 @@ You will accelerate popular machine learning algorithms and data analytics opera
## Time & risk
* **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
* Kaggle API generation failure requiring retries
* **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
## 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
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
```
## Step 3. Activate the conda environment
- Activate the conda environment
```bash
conda activate rapids-test
```
## 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
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
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
- Run the **cudf_pandas_demo.ipynb** notebook and use `localhost:8888` in your browser to access the notebook
```bash
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
- Run the **cuml_sklearn_demo.ipynb** notebook and use `localhost:8888` in your browser to access the notebook
```bash
jupyter notebook cuml_sklearn_demo.ipynb
```