This repository contains demo notebooks for the GPU DataFrame (GDF).

In this demo, we will train 4000 regularized linear regression models on the U.S. Census dataset, with the goal to predict the income of a person, given approximately 447 data points (such as age, occupation, zip code, etc.)

By using multiple GPUs, we are able to speed up this process significantly, and can train about 40 models per second (on a DGX-1 with 8 GPUs)

Notebook: mapd_to_pygdf_to_h2oaiglm.ipynb. Uses: mapd, pymapd, pygdf, h2oaiglm

This is a simple example that demonstrates the use of PyMapD to create a table, populate it and fetch query result as a GDF. Then, we show some common PyGDF dataframe operations on the GDF; for example, groupby, join, and transform columns with custom Python code that is just-in-time compiled into GPU code.

Notebook: nytaxi-pymapd-pygdf.ipynb Uses: mapd, pymapd, pygdf

To build the docker image, go into the ./notebook-demo-docker and run:

docker build -t goai/base:latest ./base
docker build -t goai/demo:latest ./demo

nvidia-docker run -p 8888:8888 -ti goai/demo:latest

This launches the mapd, and the notebook automatically.

Login to the notebook with your browser by following the URL printed on the terminal.

Open mapd_to_pygdf_to_h2oaiglm.ipynb and hit "Run All" to test. This notebook should run to the end without error.

To run on specific GPUs, use NV_GPU.

For example:

NV_GPU=0 nvidia-docker run -p 8888:8888 -ti goai/demo:latest