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RAPIDS Integration

cnvrg allows you to instantly connect RAPIDS to automate your work and accelerate your development

Flexible ML Platform


Use any language, AI framework, and compute environment. Integrate and
 version any kind of data to reuse in any project, experiment, and/or notebook 

Interactive ML Platform


Use any development environment like JupyterLab, RStudio, and more with pre-installed dependencies and version control

Unified ML Platform


One unified environment to manage, build and deploy your machine learning with all your favorite data science tools

Learn more about the RAPIDS integration

RAPIDS is a data science framework and open source software library for executing end-to-end data science on graphics processing units (GPUs). RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. The suite also focuses on common data preparation tasks for data science including a Pandas-esque dataframe API which integrates with a variety of machine learning algorithms to hedge typical serialization costs.

Connect cnvrg & RAPIDS in minutes

Accelerate your AI development easily connecting cnvrg & RAPIDS

One-click development environment

  • Get started quickly with out-of-the-box RAPIDS integration with pre-built containers
  • Speed up development with container-based ML platform
  • Leverage advanced algorithms with an easy to use ML workspace and the AI library
Machine Learning Tracking
Machine Learning Pipelines

Production ML at Scale

  • Scale your usage of RAPIDS across hundreds of nodes
  • Effortlessly go from small to big data with an auto-managed and scalable cluster infrastructure
  • Flexibility to run locally or in your own compute resources
Machine Learning Pipelines

Extendable Machine Learning Platform

  • Build and attach your own docker containers with a fully containerized data science platform
  • Connect private or third-party container registries (Docker Hub, NGC) in a few clicks
  • Access the industry’s best ML containers released periodically by the solutions team
Dataset Management