Deploying an AI-ready enterprise platform capable of handling multi-node learning involves setting up a distributed computing environment where multiple nodes collaborate to train large-scale AI models. This approach accelerates computation and enables the processing of extensive datasets that surpass the capabilities of a single node. Leveraging VMware vSphere 7 Update 2 in conjunction with NVIDIA’s GPU technologies facilitates the creation of such an infrastructure.

Understanding Multi-Node Learning

Multi-node learning, also known as distributed learning, partitions the training process of AI models across several nodes. Each node processes a subset of the data, and periodic synchronization ensures the model parameters are updated coherently. This methodology significantly reduces training time and allows for the handling of datasets that are too large for a single machine’s memory.

Key Components for Multi-Node Learning Deployment

1. Hardware Requirements:
   • Servers: Multiple servers equipped with NVIDIA GPUs, preferably from the Ampere architecture (e.g., A100 GPUs), which support features like Multi-Instance GPU (MIG) for efficient resource utilization.
   • Networking: High-speed network interfaces that support Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) to facilitate low-latency, high-throughput communication between nodes.
2. Software Requirements:
   • VMware vSphere 7 Update 2: This version introduces enhancements that support AI workloads, including improved GPU virtualization and support for NVIDIA GPUDirect RDMA.
   • NVIDIA vGPU Software: Enables GPU sharing among multiple virtual machines, optimizing resource utilization.
   • NVIDIA AI Enterprise Suite: A comprehensive suite of AI tools and frameworks optimized for VMware environments.

Deployment Steps

1. Hardware Configuration:
   • BIOS Settings: Enable virtualization technologies (e.g., VT-x, AMD-V) and SR-IOV (Single Root I/O Virtualization) in the server BIOS to allow direct access to GPU resources.
   • Network Configuration: Ensure that network adapters support RoCE and are configured for RDMA to reduce latency in inter-node communications.
2. vSphere Configuration:
   • Enable vGPU:
     • Access the vSphere Client and navigate to the desired ESXi host.
     • Under “Configure,” select “Hardware” > “Graphics” > “Graphics Devices.”
     • Click “Edit,” assign the GPU to the virtual machine, and select the appropriate vGPU profile.
   • Enable Multi-Instance GPU (MIG) (Optional):
     • SSH into the ESXi host.

  Execute the command:

#nvidia-smi mig -cgi 19,14,9,5,5,2 -C

• • • Reboot the host to apply changes.

1. Virtual Machine (VM) Configuration:
   • Prerequisites: Ensure VMs are compatible with ESXi 7.0 U2 and have the latest VMware Tools installed.
   • Assign vGPU to VM:
     • Power off the VM.
     • Edit VM settings to add a new PCI device.
     • Select the NVIDIA vGPU and choose the desired profile.
     • Save changes and power on the VM.
2. Enabling Multi-Node Communication:
   • Using RoCE:
     • Ensure RDMA over Converged Ethernet is supported and enabled on network adapters.
     • Configure network switches to prioritize RDMA traffic, ensuring low-latency communication between nodes.
   • Using PVRDMA (Paravirtual RDMA):
     • In the vSphere Client, navigate to the VM settings.
     • Add a new network adapter and select “PVRDMA” as the adapter type.
     • Connect the adapter to a distributed port group that supports RDMA.