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    Increasing model velocity for complex models by leveraging hybrid pipelines, parallelization and GPU acceleration

    Data science is facing an overwhelming demand for CPU cycles as scientists try to work with datasets that are growing in complexity faster than Moore’s Law can keep up....

    Powering Up Machine Learning with GPUs

    Whether you are a machine learning enthusiast, or a ninja data scientist training models for all sorts of applications, you may have heard of the...

    Spark, Dask, and Ray: Choosing the Right Framework

    Apache Spark, Dask, and Ray are three of the most popular frameworks for distributed computing. In this blog post we look at their history, intended...

    On-Demand Spark clusters with GPU acceleration

    Apache Spark has become the de facto standard for processing large amounts of stationary and streaming data in a distributed fashion. The addition...

    Benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST

    In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, writes about benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using...

    Deep Learning on GPUs without the Environment Setup

    We have seen an explosion of interest among data scientists who want to use GPUs for training deep learning models. While the libraries to support...

    Making Data Science Fast: Survey of GPU Accelerated Tools

    This talk took place at the Domino Data Science Pop-up in Austin, TX on April 13, 2016 In this talk, Mazhar Memon, CEO and Co-founder at...

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