RAPIDS is the open-source suite libraries for executing data science & analytics pipelines totally on the graphics processing units. Nvidia rapids accelerates the data science pipelines for creating productive workflows.
RAPIDS is the library of an open source software, which runs totally on the GPUs. This works with the different ML algorithms to offer the faster processing speed with no serialization costs. The RAPIDS supports the multi-GPU deployments over the huge dataset sizes for data science pipelines.
How Does it Work?
RAPIDS makes use of GPU-accelerated ML to make the whole data science & analytics workflows to run faster with HR system HK. The GPU-optimized data frame helps to build the databases & machine learning apps. RAPIDS is made to look & feel like Python as well as offers the collection of libraries to run the data science pipeline totally through GPUs. RAPIDS includes the Dataframe API that integrates with the machine ML algorithms.
RAPIDS Improve Analytics Pipelines and Data Science
RAPIDS accelerates data science pipeline that includes data loading, model training, ETL, and inference that will allow for the interactive & exploratory workflows.
Advantages of the RAPIDS include:
- Scale — Get seamless scaling over any GPU, which includes GPU deployments & multi-node clusters.
- Integration — You can accelerate the Python data science chain with the minimal code changes.
- Speed — Quick training time for improving the data science productivity.
- Open source — The customizable open-source program supported by the NVIDIA & built on the Apache Arrow.
- Accuracy —Enables faster model deployment & iterations to improve machine learning accuracy.