Faster training time to improve data science productivity. Intuitive GUI.
January 23, 2022
My experience has been phenomenal I am a fully satisfied customer. The RAPIDS suite of software libraries, built on CUDA-X AI, gives you …
NVIDIA RAPIDS
NVIDIA RAPIDS
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What is NVIDIA RAPIDS?
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs.
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- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
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Product Details
What is NVIDIA RAPIDS?
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs.
NVIDIA RAPIDS Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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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.
- 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.
- Its not flexible and cost effective for all sizes of organizations.
- I appreciate it has hassle-free integration.
December 12, 2021
NVIDIA RAPIDS AI
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.
- Visualization
- Deep learning pipeline
- State of the art libraries
- GPU restart failure is a tricky bug