Paperspace, a leading provider of AI cloud computing, today announced Paperspace Gradient is certified under the new NVIDIA DGX-Ready Software program. The program offers proven solutions that complement NVIDIA DGX™ systems, including the new NVIDIA DGX A100, with certified software that supports the full lifecycle of AI model development.
Paperspace Gradient accelerates and scales the development and deployment of production-ready machine learning and deep learning models. The platform runs on the industry’s first comprehensive CI/CD engine for building, training, and deploying deep learning models. Paperspace’s best-in-class machine learning tooling and methodology supports multi-cloud, on-premise, and hybrid environments for today’s modern enterprises. It also works with NVIDIA NGC — hub for AI software — and is optimized for NVIDIA DGX systems.
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Gradient’s development environment supports building and training models from research and development to production-scale. Gradient offers Jupyter notebooks, the Gradient CLI with GitHub integration, and the Gradient web interface. It provides cloud-agnostic support for end-to-end model development, management, and deployment.
Paperspace Gradient was developed to make building machine learning infrastructure more accessible, painless, cost-effective, and efficient. The platform’s web-based development environment obviates the need for any setup and ongoing management of infrastructure.
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“AI has made its way into every aspect of business, yet developers at most organizations still find current frameworks costly and difficult to implement,” said Dillon Erb, CEO & co-founder, Paperspace. “Paperspace aims to give every developer the tools needed to be an ML developer, and to unleash the full potential of AI for every enterprise.”
“We developed our NVIDIA DGX-Ready Software program to accelerate AI development in the enterprise,” said John Barco, senior director of DGX software product management at NVIDIA. “Paperspace has developed a unique CI/CD approach to building machine learning models that simplifies the process and takes advantage of the power of NVIDIA DGX systems.”
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