Sylabs, the company offering products, services, and solutions based on Singularity, the open source container solution for compute based workloads, is the latest new member to join the LF Deep Learning Foundation at the Linux Foundation.

Singularity was developed originally for high-performance computing (HPC) and is rooted in the open source community.

“We’re developing a solution for containerization that targets those with compute-driven workloads, so the fit with LF Deep Learning is highly relevant,” said Gregory Kurtzer, CEO and founder of Sylabs. “There’s a massive need to properly containerize and support workflows related to artificial intelligence and machine/deep learning.”

With a focus on compute-centric containerization, Sylabs also joined the Linux Foundation initiative behind Kubernetes, the Cloud Native Computing Foundation.

“AI in containerization is evolving rapidly and we are pleased to welcome forward-thinking new member companies like Sylabs,” said Ibrahim Haddad, executive director of LF Deep Learning Foundation. “Sylabs brings experience in both containers and AI to the LFDL and we are looking forward to working together to benefit the open source community.”    

Sylab’s integration between Singularity and Kubernetes leverages the Open Containers Initiative (OCI) image and runtime specifications as of the recent Singularity 3.1.0 release. Sylab’s recent blog shares a demonstration around this based upon a use case from Deep Learning.

Sylabs is excited to get more involved in the LF DL community and advance cloud native computing and AI innovation and efficiency around the compute-driven model. Sylabs will be attending KubeCon+CloudNativeCon North America later this year, while LF DL community members Huawei and Uber are taking part in KubeCon+CloudNativeCon+Open Source Summit China, June 24-26, 2019, in Shanghai, where the latest open source AI/DL/ML developments will be featured.

LF Deep Learning is building a sustainable ecosystem that makes it easy to create new AI products and services using open source technologies. Today, LF DL includes the following projects:

• • Acumos, a platform to build, share and deploy AI apps;

• • Angel ML, a flexible and Powerful Parameter Server for large-scale machine learning;

• • EDL, an Elastic Deep Learning framework designed to build cluster cloud services;

• • Horovod, a framework for distributed training across multiple machines; and
  • Pyro, a deep probabilistic programming framework that facilitates large-scale exploration of AI models.

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