Train and benchmark AI workloads with DeepSpeed on Google Cloud C4A Axion VMs
Introduction
Understand DeepSpeed and Google Axion C4A for AI training
Create a Google Axion C4A virtual machine for DeepSpeed
Set up PyTorch and DeepSpeed on a Google Axion C4A virtual machine
Train and benchmark AI workloads on an Arm-based Google Axion virtual machine
Next Steps
Train and benchmark AI workloads with DeepSpeed on Google Cloud C4A Axion VMs
Who is this for?
This is an introductory topic for DevOps engineers, ML engineers, and software developers who want to run AI training and benchmarking workloads using PyTorch and DeepSpeed on SUSE Linux Enterprise Server (SLES) Arm64, validate CPU-based neural network execution, and benchmark AI performance on Arm processors.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Install and configure PyTorch and DeepSpeed on Arm-based Google Cloud C4A Axion VMs
- Create and execute neural network training workloads using PyTorch
- Benchmark CPU-based AI workloads on Arm64 processors
- Validate scalable AI execution and workload performance on Google Axion Arm VMs
Prerequisites
Before starting, you will need the following:
- A Google Cloud Platform (GCP) account with billing enabled
- Basic familiarity with Python and machine learning concepts