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Learn how to efficiently use GPU acceleration for AI/ML
VMware vSphere Bitfusion virtualizes Graphics processing units (GPU) to provide a pool of shared, network-accessible GPU resources that support Artificial Intelligence (AI) and Machine Learning (ML) applications. Check out this demo-video to get an understanding about Bitfusion and how it helps to efficiently utilize GPU devices.
This video covers the new Assignable Hardware framework with Dynamic DirectPath I/O in vSphere 7. A new, flexible, way of utilizing hardware accelerators like GPU, FPGA, or NICs with virtual workloads. We'll demo how to configure virtual machines with a GPU using Dynamic DirectPath I/O.
Describes how Bitfusion can help you achieve the most efficient use of GPUs by partitioning its memory and running multiple concurrent applications, one in each partition. VMware vSphere Bitfusion also allows you do this while accessing the GPUs from remote servers.
Create a Bitfusion Client service; run ML applications on VMs with pre-allocated remote GPUs for acceleration; eliminate need to invoke Bitfusion on the command line
Instructions and example to create a Bitfusion kernel for Jupyter Notebooks. Run your AI/ML application with remote access to GPUs for acceleration using Bitfusion technology inside Jupyter