GPU cloud tools take complexity out of machine learning infrastructure

Jack Vaughan | July 02, 2018

GPU cloud tools take complexity out of machine learning infrastructure
While talk of AI on GPUs is abuzz, actually building a machine learning infrastructure remains a dark art. A startup's PaaS is looking to automate parts of the process. Deep learning and machine learning are ushering in new waves of innovative hardware, but getting jobs running on such advanced infrastructure is not easy. Despite their high hopes, teams can burn up a lot of development time trying to move their jobs to the latest GPU or TPU machine learning infrastructure. This issue drives a lot of interest in data science platforms that can automate the effort to pack up libraries and other software elements in containers to successfully run on machine learning infrastructure in a GPU cloud. Cloud vendors, Hadoop distribution providers, machine learning specialists and others have been rolling out such software. Among the startups chasing the problem is PaaS specialist Paperspace Co. The Brooklyn, N.Y.-based company has been enhancing its software to enable a wider group of developers to implement highly iterative, neural network-based AI workloads on GPUs and other hardware. Job runner to the cloud. Paperspace launched its Gradient tool suite earlier this year to train neural networks and run GPU cloud jobs. The suite includes a job runner that enables developers to send local jobs to the cloud to be processed while supporting containers and Jupyter Notebooks.

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