RAPIDS (Using GPU for Fast Computations)
Explore RAPIDS, an open source suite leveraging GPUs for fast data science and machine learning tasks. Understand its key libraries like cuDF for DataFrames and cuML for GPU-accelerated modeling. Discover how RAPIDS enhances speed, scalability, and accuracy in large-scale computations.
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RAPIDS
RAPIDS is an open source suite of software libraries and APIs that enable users to execute their pipeline entirely on the GPU.
RAPIDS has the following characteristics:
- Low-level compute optimization
- Exposes GPU parallelism
- High-bandwidth memory speed
Features of RAPIDS
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Scale: It easily scales with the number of GPUs increasing in the underlying system. It supports multi-node, multi-GPU deployments enabling faster training on much larger datasets.
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Accuracy: It allows for faster iterations of algorithms used for building the models to make model building faster and increase the accuracy of the models. The training time for models decreases.
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Open Source: It is open source and supported by NVIDIA and it is built on Apache Arrow.
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Speed: It has a faster training time for increasing the productivity in the life of a Data Scientist.