How NVIDIA Uses PySpark
8 engineering articles about PySpark from NVIDIA's engineering team
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The article discusses how the NVIDIA RAPIDS Accelerator for Apache Spark enables zero code change for GPU-accelerated data processing, enhancing the performance of Apache Spark ML applications.
The article discusses the Spark RAPIDS ML library, an open-source Python package that accelerates Apache Spark ML applications using NVIDIA GPU technology.
Erik Ordentlich
8 min read
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The article discusses the integration of distributed deep learning with Apache Spark 3. 4, highlighting new built-in APIs for both distributed model training and inference.
Lee Yang
6 min read
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The article discusses the introduction of Spark RAPIDS ML, a new GPU-accelerated library for Apache Spark ML that enhances the performance and cost-effectiveness of machine learning applications.
The article discusses how retailers can enhance their data analytics capabilities using GPU-accelerated Apache Spark workloads on Google Cloud Dataproc.
Saurav Agarwal
12 min read
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The article discusses how Volkswagen is leveraging NVIDIA RAPIDS to accelerate connected car data pipelines by 100x, addressing challenges such as geospatial indexing and K-Nearest Neighbors.
The article discusses how Dask, an open-source library, enhances Python's capabilities for data science and machine learning, making it suitable for enterprise-level applications.
The article discusses the challenges and solutions involved in merging telemetry and logs from microservices at scale using Apache Spark.
Niranjan Nataraja
11 min read
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