cuDF

A close-up of a person typing on a laptop.
Aug 30, 2023

Workshop: Enhancing Data Science Outcomes with Efficient Workflows

Learn to create an end-to-end machine learning pipeline for large datasets with this virtual, hands-on workshop.
1 MIN READ
An illustration representing the RAPIDS workflow.
Aug 23, 2023

ICYMI: Utilizing GPUs for Machine Learning with RAPIDS

Delve into how TMA Solutions is accelerating original ML and AI workflows with RAPIDS.
1 MIN READ
An illustration with 3 different colored squares labeled GPUs in a row.
Aug 04, 2023

ICYMI: Unlocking the Power of GPU-Accelerated DataFrames in Python

Read this tutorial on how to tap into GPUs by importing cuDF instead of pandas–with only a few code changes.
1 MIN READ
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Jul 17, 2023

New Video: Visualizing Census Data with RAPIDS cuDF and Plotly Dash

Gathering business insights can be a pain, especially when you're dealing with countless data points.  It’s no secret that GPUs can be a time-saver for...
2 MIN READ
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Jun 28, 2023

ICYMI: Exploring Challenges Posed by Biased Datasets Using RAPIDS cuDF

Read about an innovative GPU solution that solves limitations using small biased datasets with RAPIDS cuDF.
1 MIN READ
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Mar 22, 2023

Reusable Computational Patterns for Machine Learning and Data Analytics with RAPIDS RAFT

RAPIDS is a suite of accelerated libraries for data science and machine learning on GPUs: cuDF for pandas-like data structures, cuGraph for graph data, and cuML...
11 MIN READ
Mar 14, 2023

Accelerated Data Analytics: Speed Up Data Exploration with RAPIDS cuDF

This post is part of a series on accelerated data analytics. Digital advancements in climate modeling, healthcare, finance, and retail are generating...
11 MIN READ
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Mar 14, 2023

Accelerated Data Analytics: Faster Time Series Analysis with RAPIDS cuDF

This post is part of a series on accelerated data analytics. Because it is generally constrained to a single core, a standard exploratory data analysis (EDA)...
9 MIN READ
Oct 17, 2022

Mastering String Transformations in RAPIDS libcudf

Efficient processing of string data is vital for many data science applications. To extract valuable information from string data, RAPIDS libcudf provides...
17 MIN READ
May 27, 2022

Boosting Data Ingest Throughput with GPUDirect Storage and RAPIDS cuDF

If you work in data analytics, you know that data ingest is often the bottleneck of data preprocessing workflows. Getting data from storage and decoding it can...
14 MIN READ
May 27, 2022

Prototyping Faster with the Newest UDF Enhancements in the NVIDIA cuDF API

Over the past few releases, the NVIDIA cuDF team has added several new features to user-defined functions (UDFs) that can streamline the development process...
8 MIN READ
May 05, 2022

Optimizing Access to Parquet Data with fsspec

As datasets continue to grow in size, the adoption of cloud-storage platforms like Amazon S3 and Google Cloud Storage (GCS) are becoming more popular. Although...
12 MIN READ
Jan 13, 2022

Accelerating Trustworthy AI for Credit Risk Management

On April 21, 2021, the EU Commission of the European Union issued a proposal for a regulation to harmonize the rules governing the design and marketing of AI...
12 MIN READ
Dec 08, 2021

Deep Learning vs Machine Learning Challenger Models for Default Risk with Explainability

Data Scientists and Machine Learning Engineers often face the dilemma of “machine learning compared to deep learning” classifier usage for their business...
18 MIN READ
Jul 30, 2021

RAPIDS Accelerator for Apache Spark v21.06 Release

Introduction RAPIDS Accelerator for Apache Spark v21.06 is here! You may notice right away that we’ve had a huge leap in version number since we announced our...
4 MIN READ
Mar 19, 2021

10 Minutes to Data Science: Transitioning Between RAPIDS cuDF and CuPy Libraries

RAPIDS is about creating bridges, connections, and clean handoffs between GPU PyData libraries. Interoperability with functionality is our goal. For example, if...
2 MIN READ