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    Democratize GPU Access with MLOps

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    Domino, in partnership with NVIDIA®, supports open, collaborative, reproducible model development and training free of DevOps constraints - powered by efficient, end-to-end compute. Democratize GPU access by granting data science teams access powerful NVIDIA-Accelerated Compute – both on-premise and in the cloud.

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    Provide Self-Serve Access to GPUs

    Domino manages the time-consuming DevOps chores that are typically required to provide access to GPU resources. The unmatched power of NVIDIA DGX compute is easily accessible via Domino so data scientists can focus on critical work, and IT teams eliminate infrastructure configuration and debugging tasks.

    “Before we had Domino, it took an intern eight weeks to just get access to a GPU. Now it's just a push button and it's those savings added up across thousands of employees at scale that really make the difference.”

    Greg Forrest

      Greg Forrest
     Senior Manager of AI  and AiMLabs,  Lockheed Martin

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    Improve Data Science Team Productivity

    Time is money, and that’s critically important when members of your data science team are forced for wait on experiments involving huge datasets and computationally intensive frameworks.

    The combination of Domino and the NVIDIA Accelerated Computing gives data scientists immediate access to parallel computing and massive floating point processing. With this amount of power just a few clicks away, important research such as deep learning can be completed in a fraction of the time.

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    Scale Across Multi-node GPUs & Compute Frameworks

    Many deep learning and AI training jobs require more than a single GPU or single multi-GPU machine. But setting up a multi-node cluster is so hard that many teams decide to leave these resources in place, even if that means they’ll often sit idle when there are no complex experimentation to be run.

    Dedicated resources and low utilization are eliminated with Domino. The platform automatically creates and manages multi-node clusters, and releases clusters when training is done. Domino supports ephemeral clusters using Spark, Ray, and Dask.

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    Govern Usage of Valuable GPU Resources

    Domino gives IT visibility into who is accessing GPU hardware and how it’s being used. Permissions can be set to ensure that employees without proper entitlements are not burning through valuable resources, and power users have full access to maximize the use of the hardware. Usage information and tracking can inform a centralized IT team to easily allocate resources and chargebacks, while also measuring the business value that the GPU-enabled model is generating.

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    Drive Utilization of Compute by Role

    Domino administrators can easily divide a single DGX system into several different hardware tiers to support a variety of different users and their use cases. For example, 1 and 2-GPU partitions can be allocated for basic research, while 4 and 8-GPU partitions are set aside for training workloads. By providing different compute options, more data scientists can use the system at the same time, and companies get the maximum benefit from their GPU investment.

    With NVIDIA Multi-Instance GPU (MIG) technology on the NVIDIA A100 Tensor Core GPU, admins can take this even further, allowing up to 56 concurrent notebooks or hosted models, each with an independent GPU instance.

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    Support Your Chosen GPU Infrastructure

    Domino's close partnership with NVIDIA means our Enterprise MLOps Platform runs across a broad range of NVIDIA Accelerated Computing solutions. 

    Coming in 2021, Domino will support NVIDIA AI Enterprise™ via NVIDIA-Certified Systems™, built on NVIDIA EGX™ platform. 

    Trusted by Customers Across Industries

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    Healthcare giant Johnson & Johnson is injecting data science across its business to improve its manufacturing, clinical trial enrollment, forecasting and more. Using an MLOps strategy to centralize data science efforts, taking into consideration people, process, and technology with NVIDIA GPU-accelerated Domino Data Lab, Johnson & Johnson has embedded over 1,000 data scientists in its business. 

    NVIDIA GTC J&J & Domino PanelPanel Discussion: How Johnson & Johnson is embedding data science across their business

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    When Lockheed Martin wanted to centralize access to data science tooling, streamline collaboration and knowledge sharing, and automate DevOps tasks, they turned to Domino Data Lab. Today, Lockheed attributes $20 million in annual cost savings to their use of Domino, which includes a 10x increase in data scientist productivity thanks to self-serve access to resources that include NVIDIA GPUs.

    Lockheed 20m 10xCase Study: Lockheed Martin attributes $20m in cost savings to data science

    "So many companies, tragically, waste precious engineering resources trying to build this tooling themselves, and it’s a lot harder. Your engineers are going to be creating much more value when focused on problems that are competitively differentiated and unique to your business."

    Jim Swanson CIO, Johnson & Johnson

    Domino makes it easy for our data scientists to rapidly access NVIDIA GPUs so we can support complex use cases like training deep neural networks.

    Mike Johnson Lead Data Scientist, Lockheed Martin

    Solution Briefs & Reference Architecture

    Learn how Domino helps democratize GPU access

    Domino’s growing partner ecosystem helps our customers accelerate the development and delivery of models with key capabilities of infrastructure automation, seamless collaboration, and automated reproducibility. This greatly increases the productivity of data scientists and removes bottlenecks in the data science lifecycle.