Get Started with RAPIDS
Unlock new insights and make data more productive by leveraging RAPIDS to accelerate your python based data science and analytics pipelines. Learn how to use our libraries, accelerate common data science use cases, and access hands-on labs to develop business-critical applications.
Explore Resources to Get Started With RAPIDS Libraries
cuDF is a GPU DataFrame library that provides a pandas-like API for loading, filtering, and manipulating data. With pandas accelerator mode, it can accelerate pandas workflows on GPUs with zero code changes.
The RAPIDS Accelerator for Apache Spark provides a set of plug-ins for Apache Spark that leverage GPUs to accelerate processing via the RAPIDS libraries.
cuGraph is a GPU-accelerated graph analytics library that includes support for property graphs, remote (graph as a service) operations, and graph neural networks.
- Overview of Graph Types & Algorithms on cuGraph
- Cheatsheet
- NetwokX GPU backend powered by cuGraph
- Graph Analytics With cuGraph
- Beginner’s Guide to GPU-Accelerated Graph Analytics in Python
- Accelerating GNNs with DGL, PyG, and cuGraph Webinar
- Accelerating End-to-End Data Science Workflows Online Course
cuML is a unified CPU/GPU library for executing machine learning algorithms with an API that closely follows the Scikit-Learn API
- Overview of Popular Machine Learning Estimators on cuML
- Cheatsheet
- cuML Interoperability with CPU and GPU
- Getting Started With cuML
- Machine Learning with GPU Accelerated Pandas and Sci-kit Learn
- Deep Dive into GPU-Accelerated Technologies Webinar
- Fundamentals of Accelerated Data Science Online Course
Use Case Tutorials
Learn how to accelerate data science in a few short hours in simple technical walk-throughs that pair step-by-step tutorials with notebooks, sample datasets, and benchmarks in example exercises that demonstrate how RAPIDS helps data scientists save time and better leverage their data in ubiquitous data science and machine learning use cases.
Accelerated Data Analysis
Learn how to use RAPIDS cuDF to accelerate data analytics workflows.
This starter kit covers how to use RAPIDS for:
- Exploratory Data Analysis
- Time Series Analysis
- Visualization
- DataFrame to ML
Get Started
Data Processing at Scale
Explore how to accelerate Apache Spark 3.0 workloads with the RAPIDS Accelerator for Apache Spark.
This starter kit walks through an example use case in retail:
- Data Processing, including ETL and manipulation functions
Get Started
Time Series Forecasting
Build time series forecasting models with RAPIDS cuDF and cuML, which mimic pandas and sci-kit learn.
This starter kit includes two examples of time series workflows:
- Time Series Analysis
Get Started
Learn How Top-Performing Kaggle Grandmasters Win Data Science Competitions
The Kaggle Grandmasters of NVIDIA place in the top 10% of Kaggle Grandmasters worldwide. Learn their tricks of the trade with our Kaggle Grandmaster series that explain how our teams use accelerated data science to create winning recommender systems and predictive models.
Mastering Multilingual Recommender Systems
The KGMoN leveraged transfer learning, model embedding, and other innovative techniques to construct a two-stage pipeline solution for e-commerce product suggestions. This solution won the 2023 KDD Cup.
View Video Read Blog
Mastering Recommender Systems
KGMoN tackled an e-commerce use case by training high-functioning recommendation systems with a dataset that included millions of anonymized user interactions. These strategies placed top 3 in Kaggle’s Otto competition.
View Video
Predicting Credit Defaults on a Massive Dataset
Jiwei Liu, KGMON, walks through GPU-acceleration in a default prediction use case (top 10 in Kaggle’s American Express competition) that resulted in significantly faster inference times.
Read BlogEnterprise Support
With Enterprise Support, you can accelerate your data science workload with the security of guaranteed response times, priority security notifications, regular updates, and access to NVIDIA AI experts. Get your workload working for you faster with NVIDIA AI Enterprise.
| Support Options for RAPIDS Find the right license to deploy, run, and scale RAPIDS for any application on any platform |
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|---|---|---|
| Features | Open Source
For individuals looking to get access to RAPIDS open-source libraries for development |
NVIDIA AI Enterprise
For enterprises looking to purchase RAPIDS for deployment. |
| Install | Contact Sales | |
| Access to All RAPIDS Libraries, with easy to use APIs | × | × |
| Large-scale graph analytics with 35+ accelerated graph algorithms | × | × |
| Machine learning toolkit with 30+ GPU-accelerated algorithms | × | × |
| Scalable with Dask or Apache Spark | × | × |
| Accessible via docker, PIP, Conda (version dependencies: CUDA®, framework backend) | × | × |
| AI Workflows for Cybersecurity, VRP, and more | × | |
| Workload and infrastructure management features | × | |
| Business-standard support, including: | × | |
- Unlimited technical support cases accepted via the customer portal and phone 24/7 |
× | |
- Escalation support during local business hours (9:00 a.m. - 5:00 p.m., Monday-Friday) |
× | |
- Timely resolution provided by NVIDIA experts and engineers |
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- Security fixes and priority notifications |
× | |
- Production branches that ensure API stability |
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- Three years of long-term support |
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| Hands-on NVIDIA LaunchPad labs | × | |
Experience Enterprise Hardware and Software on NVIDIA Launchpad
Work with NVIDIA experts to try out live use cases powered by RAPIDS, in NVIDIA provisioned infrastructure with Launchpad.
Predict Prices with Accelerated Data Processing
Train an XGBoost model and predict taxi ride fares with RAPIDS.
Learn MoreData Processing, Tokenization, and Sentiment Analysis
Develop and deploy a sentiment analysis model.
Learn MoreAccelerating Apache Spark With Zero Code Changes
Run GPU-accelerated Spark SQL queries to run an ETL workflow.
Learn MoreSynthetic Data Generator for Tabular Data
Explore tabular tokenization and preprocess data to train a GPT model.
Learn MoreScale Data Science with Domino Data Lab
Develop and deploy a data science project on Domino Data Lab.
Learn MoreDeploy a Fraud Detection XGBoost Model
Use cuML tools to visualize a fraud detection dataset.
Learn MoreDevelop an AI-based Cybersecurity Solution
Run cybersecurity use cases to analyze the behavior of every user and detect anomalies.
Learn MoreNext Item Prediction Using Recommendation Systems
Achieve better accuracy for recommendation models.
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RAPIDS libraries are open source, written in Python, and built on Apache Arrow. The software is being developed in partnership with enterprises globally. Download RAPIDS to dramatically accelerate machine learning and data science.
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