How NVIDIA Uses Polars
19 engineering articles about Polars from NVIDIA's engineering team
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The NVIDIA Grace CPU, launched in 2023, integrates Arm Neoverse cores with advanced memory and interconnect technologies to deliver high bandwidth and energy efficiency for modern data centers.
Praveen Menon
7 min read
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The article discusses the integration of XGBoost with Polars DataFrames, emphasizing the benefits of GPU acceleration for machine learning workflows.
Jiaming Yuan
7 min read
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The article presents a comprehensive playbook developed through extensive experience in Kaggle competitions, detailing seven effective modeling techniques for handling tabular data.
Brian Tepera
8 min read
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This article discusses five common performance bottlenecks in pandas workflows, providing insights on how to identify and resolve these issues using both CPU and GPU solutions.
This article discusses seven drop-in replacements for popular Python libraries that can significantly speed up data science workflows by leveraging GPU acceleration.
Jamil Semaan
8 min read
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The article discusses how GPU acceleration can significantly enhance the performance of common pandas workflows when dealing with large datasets.
Jamil Semaan
4 min read
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RAPIDS version 25.
Brian Tepera
6 min read
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This article discusses strategies for processing large datasets that exceed GPU VRAM using the Polars GPU engine, specifically focusing on Unified Virtual Memory (UVM) and multi-GPU streaming execu...
NVIDIA utilizes data science and machine learning to enhance chip manufacturing processes, focusing on optimizing workflows through the use of CUDA-X libraries like cuDF and cuML.
Divyansh Jain
8 min read
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The article discusses the latest enhancements in RAPIDS, including zero-code-change acceleration for Python machine learning, significant IO performance improvements, and out-of-core XGBoost capabi...
ApacheAzureAzure Blob StorageDaskGeminiGoogle CloudGoogle Cloud StorageLightGBMNetworkXPolarsPythonscikit-learnXGBoost
Nick Becker
9 min read
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The article discusses optimizing the Polars GPU Parquet Reader to handle large datasets efficiently.
Prem Sagar Gali
4 min read
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The article discusses the performance and energy efficiency of the NVIDIA Grace CPU Superchip for ETL workloads, comparing it with AMD and Intel CPUs.
Gregory Kimball
6 min read
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The article discusses the strategies employed by the winners of the NVIDIA hackathon at ODSC West, focusing on how they utilized RAPIDS Python APIs to enhance machine learning workflows.
The NVIDIA Deep Learning Institute has launched the Accelerated Data Science Teaching Kit, aimed at educators to enhance data science education.
Joe Bungo
3 min read
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The NVIDIA RAPIDS v24.
Nick Becker
8 min read
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NVIDIA has announced that its CUDA-X platform now accelerates the Polars Data Processing Library, enhancing its performance for data analytics.
Nick Becker
3 min read
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Polars has launched a new GPU engine powered by RAPIDS cuDF, which accelerates data processing workflows by up to 13x on NVIDIA GPUs.
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