Difference between revisions of "RAPIDS"
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* [http://rapids.ai/community.html RAPIDS Community] | * [http://rapids.ai/community.html RAPIDS Community] | ||
| − | The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end [[Data Science|data science]] and [[analytics]] pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for [[analytics]] and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. [http://rapids.ai/ RAPIDS Getting Started | NVIDIA] | + | The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end [[Data Science|data science]] and [[analytics]] pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth [[memory]] speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for [[analytics]] and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. [http://rapids.ai/ RAPIDS Getting Started | NVIDIA] |
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<youtube>G1kx_7NJJGA</youtube> | <youtube>G1kx_7NJJGA</youtube> | ||
Latest revision as of 06:22, 2 March 2024
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The RAPIDS data science framework includes a collection of libraries for executing end-to-end data science pipelines completely in the GPU. It is designed to have a familiar look and feel to data scientists working in Python. The RAPIDS suite of open source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. RAPIDS Getting Started | NVIDIA
