– Many AI/ML workloads require high performance computing systems
– Google Cloud’s Cloud HPC Toolkit simplifies the creation and management of HPC systems for AI/ML workloads
– The ML blueprint within the Cloud HPC Toolkit helps deploy HPC systems optimized for training large language models and other AI/ML workloads
The Cloud HPC Toolkit by Google Cloud simplifies the creation and management of HPC systems for AI/ML workloads, including training large language models. It includes features such as quickly provisioning and configuring HPC clusters, installing and managing AI/ML software stacks, optimizing HPC clusters for AI/ML workloads, and monitoring HPC clusters.
A new Cloud HPC Toolkit blueprint for ML workloads allows users to easily spin up a HPC system running on A3 VMs with NVIDIA H100 Tensor Core GPUs, ideal for training large language models and other AI/ML workloads. The blueprint incorporates Google Cloud best practices to ensure high training performance.
Deploying large-scale HPC and AI/ML clusters with NVIDIA GPUs involves careful coordination of infrastructure components, including networking configuration. The Cloud HPC Toolkit simplifies this process through an easy-to-use blueprint with best practices already in place, providing features such as configuring high-speed networking via five NICs, shared storage using Filestore, the Slurm scheduler, management VMs, and pre-configured user environments with popular AI/ML libraries and tools. This blueprint helps optimize HPC systems for demanding AI/ML workloads and makes it easier for users familiar with traditional HPC systems and schedulers to adapt to AI/ML needs on Google Cloud.