rapids Archives

rapids

Watch: RAPIDS: The Platform Inside and Outside

Conferences rapidsposted by ODSC Team March 16, 2020

This session, “RAPIDS: The Platform Inside and Outside,” presented by Joshua Patterson, Data Science Director at NVIDIA, for ODSC East 2019, looks at the programming platform RAPIDS. Python has seen terrific progress as... Read more

Regression Blog 2: We’re Practically Giving These Regressions Away

Modeling Open Source rapids regression XGBoostposted by RAPIDS October 11, 2019

When I heard that they would be releasing Pumpkin Spice Spam, I thought of regression. This might seem like a leap, but bear with me. In the U.S. in the last few years, hearing news reports of unusual Pumpkin Spice flavored products means the unofficial end of... Read more

cuSpatial Accelerates Geospatial and Spatiotemporal Processing

Machine Learning Modeling geospatial IoT Machine Learning rapidsposted by RAPIDS October 1, 2019

The Internet of Things (IOT) has spawned explosive growth in sensor data. Location is some of the most important information generated by sensors, and dynamic location is vital in the case of mobile sensors. Examples include: mobile phones (GPS), vehicles, robots, and cameras. [Related Article: The... Read more

Accelerating Deep Learning Recommender Systems by Over 15x Using RAPIDS, PyTorch, and fast.ai

Deep Learning Modeling Tools & Languages Deep Learning rapids Recommender Systemsposted by RAPIDS September 25, 2019

This June, the RAPIDS Deep Learning team took part in the RecSys 2019 Challenge, where we placed 15th out of 1534 teams despite joining the competition in its final weeks. The competition centered around recommending hotel listings to users of the website Trivago, who was the host of the... Read more

Financial Data Modeling with RAPIDS.

Business + Management Financial Services Tools & Languages financial data modeling rapidsposted by RAPIDS August 15, 2019

A financial dataset is challenging in many ways. The data is usually anonymized to protect customers’ privacies. Sometimes even the column name of the tabular data is encoded, which can prevent feature engineering using domain knowledge. As required by financial regulation and laws, oftentimes the models must be... Read more

gQuant — GPU-Accelerated examples for Quantitative Analyst Tasks

Modeling Tools & Languages gquant Quantitative Analyst Tasks rapidsposted by RAPIDS August 14, 2019

gQuant Background: Our prior blog gave a high-level overview of examples in the gQuant repository using GPU accelerated Python. Here we will dive more deeply into the technical details. The examples in gQuant are built on top of NVIDIA’s RAPIDS framework and feature fast data access provided by cuDF dataframes... Read more

RAPIDS 0.8: Same Community New Freedoms

Machine Learning Modeling Tools & Languages Machine Learning PyTorch rapidsposted by RAPIDS August 13, 2019

RAPIDS released 0.8 a few weeks back. And afterwards, like most Americans, we took off for the 4th of July holiday. Over that break, I reflected on the purpose of RAPIDS. Speed is great, building a strong community is awesome, but the true power of RAPIDS is... Read more

Nightly News: CI produces latest packages

Guest contributor rapidsposted by RAPIDS July 30, 2019

“Release code early and often” is a software engineering philosophy that RAPIDS takes to heart. We try to release about every six weeks or so, partly to keep up the pace of feature development, but also so RAPIDS users don’t get stuck on older versions of our software... Read more

When Less is More: A Brief Story About Feature Engineering with XGBoost

Machine Learning Machine Learning rapidsposted by RAPIDS July 25, 2019

I played a minor role launching RAPIDS on Google Dataproc by refining a model that predicts taxi fare in New York City. Geographic location of passenger pick-ups and drops-offs were columns in the data. These are recorded as longitude and latitude measurements, with precision to many decimal places.... Read more

RAPIDS cuGraph

Modeling Predictive Analytics graph analytics Open Source rapidsposted by RAPIDS July 8, 2019

The Data Scientist has a collection of techniques within their proverbial toolbox. Data engineering, statistical analysis, and machine learning are among the most commonly known. However, there are numerous cases where the focus of the analysis is on the relationship between data elements. In those cases,... Read more