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NVIDIA RAPIDS

Score9.1 out of 10

4 Reviews and Ratings

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What is NVIDIA RAPIDS?

NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs. Users can run data science workflows with high-speed GPU compute and parallelize data loading, data manipulation, and machine learning for 50X faster end-to-end data science pipelines.

Categories & Use Cases

Key Features

Learn about the best (and worst!) features NVIDIA RAPIDS has to offer, as determined by TrustRadius' reviewers.
Based on 4 ratings of NVIDIA RAPIDS's features
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Top Performing Features

  • Connect to Multiple Data Sources

    Ability to connect to a wide variety of data sources including data lakes or data warehouses for data ingestion

    Category average: 8.8

  • Visualization

    The product’s support and tooling for analysis and visualization of data.

    Category average: 8.3

  • Interactive Data Analysis

    Ability to analyze data interactively using Python or R Notebooks

    Category average: 8.8

Areas for Improvement

  • Security, Governance, and Cost Controls

    Built-in controls to mitigate compliance and audit risk with user activity tracking

    Category average: 8.6

  • Extend Existing Data Sources

    Use R or Python to create custom connectors for any APIs or databases

    Category average: 8.9

  • Interactive Data Cleaning and Enrichment

    Access to visual processors for data wrangling

    Category average: 9

Reviews

What users are saying about NVIDIA RAPIDS.
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Faster training time to improve data science productivity. Intuitive GUI.

Incentivized
Douglas CarterView profile
Technical Manager in Information Technology at BairesDev (1001-5000 employees employees)

Use Cases and Deployment Scope

My experience has been phenomenal I am a fully satisfied customer. The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analysis pipelines on GPUs. This tool also focuses on common data preparation tasks for analytics and data science which accelerate our Python data science toolchain with minimal code changes and no new tools to learn. The support is great in all aspects.

Pros

  • Increases machine learning model accuracy by iterating on models faster and easy deployment frequently.
  • Easy to use and maintain.
  • Great support team.
  • Improves our productivity with near-interactive data science.

Cons

  • Its not flexible and cost effective for all sizes of organizations.
  • I appreciate it has hassle-free integration.

Return on Investment

  • Hassle free integration.
  • Top model accuracy.
  • Reduce training time.

Alternatives Considered

MATLAB

NVIDIA RAPIDS AI

Incentivized
Mauro Bennici
CTO (Chief Technical Officer) in Information Technology at Ghostwriter.AI (1-10 employees employees)

Use Cases and Deployment Scope

NVIDIA RAPIDS helps us create an integrated pipeline process to monitor, design, and deploy deep learning models internally and externally. It is also easy to deploy our services on-premises in our customer's datacenters. The integrated visualization "area" is excellent for instantly running and using detailed data. NVIDIA RAPIDS allows us to be focused on creating value for our customers, avoiding reinventing the wheel.

Pros

  • Visualization
  • Deep learning pipeline
  • State of the art libraries

Cons

  • GPU restart failure is a tricky bug

Return on Investment

  • Efficient way to complete tasks
  • De-facto GPUs standard