In addition to announcing the formation of the GPU Open Analytics Initiative with H2O and MapD, today, we are pleased to announce an exciting collaboration with NVIDIAH2O and MapD, with a goal of democratizing machine learning to increase performance gains of data science workloads. Using NVIDIA’s Graphics Processing Unit (GPU) technology, Anaconda is mobilizing the Open Data Science movement by helping teams avoid the data transfer process between Central Processing Units (CPUs) and GPUs and move toward their larger business goals. 

In addition to announcing the formation of the GPU Open Analytics Initiative with H2O and MapD, today, we are pleased to announce an exciting collaboration with NVIDIA, H2O and MapD, with a goal of democratizing machine learning to increase performance gains of data science workloads. Using NVIDIA’s Graphics Processing Unit (GPU) technology, Anaconda is mobilizing the Open Data Science movement by helping teams avoid the data transfer process between Central Processing Units (CPUs) and GPUs and move toward their larger business goals. 

The new GPU Data Frame (GDF) will augment the Anaconda platform as the foundational fabric to bring data science technologies together allowing it to take full advantage of GPU performance gains. In most workflows using GPUs, data is first manipulated with the CPU and then loaded to the GPU for analytics. This creates a data transfer “tax” on the overall workflow.   With the new GDF initiative, data scientists will be able to move data easily onto the GPU and do all their manipulation and analytics at the same time without the extra transfer of data. With this collaboration, we are opening the door to an era where innovative AI applications can be deployed into production at an unprecedented pace and often with just a single click.

In a nutshell, this collaboration provides these key benefits:

  • Python Democratization. GPU Data Frame makes it easy to create new optimized data science models and iterate on ideas using the most innovative GPU and AI technologies.

  • Python Acceleration. The standard empowers data scientists with unparalleled acceleration within Python on GPUs for data science workloads, enabling Open Data Science to proliferate across the enterprise.

  • Python Production. Data science teams can move beyond ad-hoc analysis to unearthing game-changing results within production-deployed data science applications that drive measurable business impact.

Anaconda aims to bring the performance, insights and intelligence enterprises need to compete in today’s data-driven economy. We’re excited to be working with NVIDIA, mapD, and H2O as GPU Data Frame pushes the door to Open Data Science wide open by further empowering the data scientist community with unparalleled innovation, enabling Open Data Science to proliferate across the enterprise.


About the Author

Travis Oliphant

President, Chief Data Scientist & Co-Founder

Travis holds a PhD from the Mayo Clinic and BS and MS degrees in mathematics and electrical engineering from Brigham Young University. Since 1997, he has worked extensively with Python for numerical and scientific programming, most notably …

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