import cudf
gdf = cudf.read_csv('path/to/file.csv')
for column in gdf.columns:
print(gdf[column].mean())
RAPIDS uses optimized NVIDIA® CUDA® primitives and high-bandwidth GPU memory to accelerate data preparation and machine learning. The goal of RAPIDS is not only to accelerate the individual parts of the typical data science workflow, but to accelerate the complete end-to-end workflow.
We suggest that you take a look at the sample workflow in our Docker container (described below), which illustrates just how straightforward a basic XGBoost model training and testing looks in RAPIDS.
You can get a minimal conda installation with Miniconda or get the full installation with Anaconda.
For instructions on how to build a development conda environment, see the cuDF README for more information. Also refer to the cuML README for conda install instructions for cuML.
Run the following command within the Docker container started from the command above to launch the notebook server:
(rapids) root@container:/rapids/notebooks# bash utils/start-jupyter.sh
NOTE: This will run JupyterLab on your host machine at port 8888.
Notebooks can be found in two directories within the container:
/rapids/notebooks/cuml - cuML demo notebooks
/rapids/notebooks/mortgage - cuDF, Dask, XGBoost demo notebook
E2E.ipynb for more detailsSee the RAPIDS Container README page for more information about using custom datasets. Docker Hub and NVIDIA GPU Cloud host RAPIDS containers with full list of available tags.
NOTE: Ubuntu 16.04’s python3 package is Python 3.5 by default. Follow the python install instructions to upgrade.
Refer to the cuDF README or cuML README for pip install instructions.
Checkout the cuDF README or cuML README for from source build instructions.
By default, Ubuntu 16.04’s python3 package is Python 3.5, so you need to install Python 3.6 or 3.7 with the following steps:
For Python 3.6
apt-get install software-properties-common python-software-properties
add-apt-repository ppa:deadsnakes/ppa
apt update && apt install python3.6-dev
For Python 3.7
apt-get install software-properties-common python-software-properties
add-apt-repository ppa:deadsnakes/ppa
apt update && apt install python3.7-dev
Check out the cuDF, cuML, and XGBoost API docs.
Learn how to setup a mult-node cuDF and XGBoost data preparation and distributed training environment by following the mortgage data example notebook and scripts.