Administrator Guide

Environment set up
28 RAPIDS Scaling on Dell EMC PowerEdge Servers
F Environment set up
In this section we explain the steps on how to install RAPIDS through the docker from NVIDIA GPU Cloud
(NGC), download the NYC-taxi dataset, and pull the notebooks repo [13]
1. Review the below prerequisites before running the tests:
a. NVIDIA Pascal™ GPU architecture or better
b. CUDA 9.2 or 10.0+ compatible NVIDIA driver
c. Ubuntu 16.04/18.04 or CentOS 7
d. Docker CE v19.03+ for Linux distribution
2. Download and the NYC-taxi dataset to the folder of your choice at the local host, for example:
$ mkdir rapids cd rapids
$ mkdir data cd data
$ wget --no-check-certificate https://storage.googleapis.com/anaconda-public-
data/nyc-taxi/csv/2014/yellow_tripdata_2014-{01..12}.csv
$ wget --no-check-certificate https://storage.googleapis.com/anaconda-public-
data/nyc-taxi/csv/2015/yellow_tripdata_2015-{01..12}.csv
$ wget --no-check-certificate https://storage.googleapis.com/anaconda-public-
data/nyc-taxi/csv/2016/yellow_tripdata_2016-{01..12}.csv
3. Pull the notebooks-contrib repo inside the container using wget or to the local host and use a docker
volume mount to /rapids/contrib/:
$ cd rapids
$ git clone https://github.com/rapidsai/notebooks-contrib
Note: Use the dataset and notebooks-contrib paths as the mounted volumes, as per the instructions
running docker images. This will map folders from the host operating system to the container OS in the
/rapids/ directories
4. Pull the selected docker image from NGC. To explore the full tag list for all available images visit:
$ docker pull nvcr.io/nvidia/rapidsai/rapidsai:0.10-cuda10.1-runtime-ubuntu18.04
5. Start the Container:
$ docker run --gpus all
--rm -it \
--net=host \
-p 8888:8888 \
-p 8787:8787 \
-p 8786:8786 \
-v /rapids/notebooks-contrib/:/rapids/notebooks/contrib/ \
-v /rapids/data/:/rapids/data/ \
nvcr.io/nvidia/rapidsai/rapidsai:0.10-cuda10.1-runtime-ubuntu18.04