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State of RAPIDS: Bridging the GPU Data Science Ecosystem

We'll explore how RAPIDS, the end to end open-source data science accelerator from NVIDIA, and the open-source ecosystem are advancing data science. You'll learn how to start leveraging RAPIDS and the libraries it interacts with for faster performance and easier development on GPUs. We'll focus on the latest engineering work, current benchmarks, new release features, and the future road map. See how RAPIDS works with leading OSS tools like BlazingSQL, Dask, MLFlow, Optuna, Streamz, UCX, Xfeat, and others to deliver high-performance end-to-end data science on GPUs.
Recorded Feb 23 2021 51 mins
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Presented by
Joshua Patterson, NVIDIA
Presentation preview: State of RAPIDS: Bridging the GPU Data Science Ecosystem

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  • Accelerating Cybersecurity Log Pipelines and Analytics for Rapid Correlation Mar 2 2021 5:00 pm UTC 56 mins
    Bartley Richardson, NVIDIA & Craig Hills, Pondurance
    Today’s security teams deal with more data than ever before and continue to search for impactful methods to accelerate their log ingest and downstream analytics. In this session, we show how this end-to-end infrastructure is accelerated and augmented using RAPIDS and CLX (Cyber Log Accelerators). We present a use case that shows how raw data can be quickly parsed, enhanced with threat intelligence feeds, and then fed to GPU-based ML techniques for rapid correlation and enrichment. We walk through the architecture, explain how the GPUs are used, present benchmarks and results, and talk some about the larger picture of the value GPU compute brings to cybersecurity workflows.
  • State of RAPIDS: Bridging the GPU Data Science Ecosystem Recorded: Feb 23 2021 51 mins
    Joshua Patterson, NVIDIA
    We'll explore how RAPIDS, the end to end open-source data science accelerator from NVIDIA, and the open-source ecosystem are advancing data science. You'll learn how to start leveraging RAPIDS and the libraries it interacts with for faster performance and easier development on GPUs. We'll focus on the latest engineering work, current benchmarks, new release features, and the future road map. See how RAPIDS works with leading OSS tools like BlazingSQL, Dask, MLFlow, Optuna, Streamz, UCX, Xfeat, and others to deliver high-performance end-to-end data science on GPUs.
  • GPU Acceleration in Python Recorded: Feb 16 2021 59 mins
    Matthew Nicely, Math Libraries Product Manager, NVIDIA
    We'll step through the process of migrating code from native Python to Numba, and then to a CuPy Raw Kernel (CUDA C++). Basic workflow, best practices, lessons learned, and coding samples will be provided. NVIDIA Nsight Systems profilers will be used to demonstrate how minor optimizations can provide substantial performance benefits to custom developed code. The techniques discussed in this session can be used in any domain.
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  • Title: State of RAPIDS: Bridging the GPU Data Science Ecosystem
  • Live at: Feb 23 2021 5:00 pm
  • Presented by: Joshua Patterson, NVIDIA
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