RAPIDS is a suite of open-source GPU-accelerated data science and AI libraries that are well supported for scale-out with distributed engines like Spark and Dask. Ray is a popular open-source distributed Python framework commonly used to scale AI and machine learning (ML) applications. Ray particularly excels at simplifying and scaling training and inference pipelines and can easily target both…
]]>introduced in a previous post, is a GPU-accelerated library that accelerates pandas to deliver significant performance improvements—up to 50x faster—without requiring any changes to your existing code. As part of the NVIDIA RAPIDS ecosystem, acts as a proxy layer that executes operations on the GPU when possible, and falls back to the CPU (via pandas) when necessary.
]]>As we move towards a more dense computing infrastructure, with more compute, more GPUs, accelerated networking, and so forth—multi-gpu training and analysis grows in popularity. We need tools and also best practices as developers and practitioners move from CPU to GPU clusters. RAPIDS is a suite of open-source GPU-accelerated data science and AI libraries. These libraries can easily scale-out for…
]]>As consumer applications generate more data than ever before, enterprises are turning to causal inference methods for observational data to help shed light on how changes to individual components of their app impact key business metrics. Over the last decade, econometricians have developed a technique called double machine learning that brings the power of machine learning models to causal…
]]>Debugging is difficult. Debugging across multiple languages is especially challenging, and debugging across devices often requires a team with varying skill sets and expertise to reveal the underlying problem. Yet projects often require using multiple languages, to ensure high performance where necessary, a user-friendly experience, and compatibility where possible. Unfortunately…
]]>As PyData leverages much of the static language world for speed including CUDA, we need tools which not only profile and measure across languages but also devices, CPU, and GPU. While there are many great profiling tools within the Python ecosystem: line-profilers like cProfile and profilers which can observe code execution in C-extensions like PySpy/Viztracer. None of the Python profilers can…
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