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Rapids cuML (Compute Unified Device Architecture) is one of the widely adopted hardware accelerators that offers an efficient and fast way to process complex data for a variety of scientific, business, and engineering applications. The CuML library offers various capabilities to users, including Python bindings for Python 3.7 and beyond. Additionally, this library comes with many efficient algorithms and frameworks to simplify the analysis of large datasets. One can also use this accelerated package through RAPIDS APIs to accelerate the execution time of ML, DL, and NLP tasks on GPU
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RAPIDS (Relational and Parallel Store for Data Science), cuML (CUDA and OpenCL based on CUDA and C++), is the latest open-source implementation of the CUDA Toolkit and an updated and enhanced version of cuDNN, cuBLAS, and cuDF libraries. It provides high-performance computing capabilities and GPU-accelerated machine learning. RAPIDS cuML helps in modeling and machine learning by providing an easy-to-use library that enables users to run large-scale models and deep