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About

The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.

About

Scikit-learn provides simple and efficient tools for predictive data analysis. Scikit-learn is a robust, open source machine learning library for the Python programming language, designed to provide simple and efficient tools for data analysis and modeling. Built on the foundations of popular scientific libraries like NumPy, SciPy, and Matplotlib, scikit-learn offers a wide range of supervised and unsupervised learning algorithms, making it an essential toolkit for data scientists, machine learning engineers, and researchers. The library is organized into a consistent and flexible framework, where various components can be combined and customized to suit specific needs. This modularity makes it easy for users to build complex pipelines, automate repetitive tasks, and integrate scikit-learn into larger machine-learning workflows. Additionally, the library’s emphasis on interoperability ensures that it works seamlessly with other Python libraries, facilitating smooth data processing.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Enterprises in search of a solution to execute end-to-end data science and analytics pipelines entirely on GPUs

Audience

Engineers and data scientists requiring a solution to manage and improve their machine learning research

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/rapids

Company Information

scikit-learn
United States
scikit-learn.org/stable/

Alternatives

Alternatives

Gensim

Gensim

Radim Řehůřek
ML.NET

ML.NET

Microsoft
MLlib

MLlib

Apache Software Foundation
NVIDIA Brev

NVIDIA Brev

NVIDIA
Keepsake

Keepsake

Replicate

Categories

Categories

Integrations

Databricks Data Intelligence Platform
Anaconda
DagsHub
Flower
Gradient
HEAVY.AI
HPE Ezmeral Data Fabric
IBM Cloud
Keepsake
Kinetica
MLJAR Studio
Matplotlib
ModelOp
NVIDIA FLARE
NumPy
Plotly Dash
Python
Train in Data

Integrations

Databricks Data Intelligence Platform
Anaconda
DagsHub
Flower
Gradient
HEAVY.AI
HPE Ezmeral Data Fabric
IBM Cloud
Keepsake
Kinetica
MLJAR Studio
Matplotlib
ModelOp
NVIDIA FLARE
NumPy
Plotly Dash
Python
Train in Data
Claim NVIDIA RAPIDS and update features and information
Claim NVIDIA RAPIDS and update features and information
Claim scikit-learn and update features and information
Claim scikit-learn and update features and information