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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.
An Introduction to vSphere Bitfusion
November 11, 2020
This video details how vSphere Bitfusion helps to remote GPU devices for efficient use for AI/ML applications.
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Bitfusion Compatibility and Interoperability
December 08, 2020
Provides Bitfusion compatibility details on driver &library versions, ML frameworks versions, operating systems, and hardware.
Using Assignable Hardware with Dynamic DirectPath IO
November 16, 2020
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.
Running Multiple Applications on One GPU with vSphere Bitfusion
November 12, 2020
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.
Bitfusion Client Service – The Bash in the Rue Morgue
November 11, 2020
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
An Introduction to vSphere Bitfusion
November 11, 2020
This video details how vSphere Bitfusion helps to remote GPU devices for efficient use for AI/ML applications.
Bitfusion Jupyter Integration—It’s Full of Stars
November 10, 2020
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
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