AMD unveils Helios rack and MI455X accelerator in bid to close the gap with Nvidia

Introduction

In this tutorial, you'll learn how to set up and run AI workloads using AMD's MI455X accelerator technology. This hands-on guide will walk you through installing the necessary software, configuring your environment, and running a simple AI inference task using the AMD GPU. By the end, you'll understand how to leverage AMD's AI hardware for machine learning applications.

Prerequisites

• Basic understanding of Linux command line
• Access to a computer with an AMD GPU (MI455X or compatible)
• Internet connection for downloading packages
• Root or sudo access for system installations

Step-by-Step Instructions

Step 1: Check Your Hardware Compatibility

Verify AMD GPU Detection

Before installing any software, we need to confirm your system recognizes the AMD GPU. Run this command to check:

lspci | grep -i amd

This command lists all AMD-related hardware in your system. You should see output mentioning your GPU model, which should be compatible with the MI455X architecture.

Step 2: Install AMD ROCm Software Stack

Install ROCm (Radeon Open Compute)

ROCm is AMD's open software platform for GPU computing. It provides the foundation for running AI workloads on AMD hardware:

sudo apt update
sudo apt install rocm-dkms rocm-utils

The ROCm stack includes drivers, libraries, and tools needed to run GPU-accelerated applications. This installation prepares your system to work with AMD GPUs for AI computing.

Step 3: Install AI Frameworks and Dependencies

Set Up Python Environment

Install Python and required libraries for AI workloads:

sudo apt install python3 python3-pip
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2

PyTorch is a popular machine learning framework that works with ROCm. Installing it with the ROCm index ensures compatibility with your AMD GPU.

Step 4: Verify GPU Access

Test Your Setup

Create a simple Python script to verify your GPU is accessible:

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU count: {torch.cuda.device_count()}")
if torch.cuda.is_available():
    print(f"GPU name: {torch.cuda.get_device_name(0)}")

Run this script with: python3 test_gpu.py

This script confirms that PyTorch can access your AMD GPU and displays its specifications.

Step 5: Run a Simple AI Inference Task

Download and Test a Pre-trained Model

Now let's run a basic image classification task using a pre-trained model:

import torch
import torchvision.models as models
import torchvision.transforms as transforms
from PIL import Image

# Load pre-trained ResNet model
model = models.resnet50(pretrained=True)
model.eval()

# Define image transformations
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

# Load sample image
image = Image.open('sample.jpg')
image_t = transform(image).unsqueeze(0)

# Run inference
with torch.no_grad():
    output = model(image_t)
    _, predicted = torch.max(output, 1)
    print(f"Predicted class: {predicted.item()}")

This code loads a ResNet50 model, processes an image, and performs inference. The model will run on your AMD GPU thanks to the ROCm setup.

Step 6: Monitor GPU Performance

Use ROCm Tools for Monitoring

Monitor your GPU's performance during AI workloads:

rocm-smi --showuse

This command displays GPU utilization, memory usage, and other performance metrics. Understanding these metrics helps optimize your AI workloads.

Summary

In this tutorial, you've learned how to set up an AMD MI455X-compatible environment for AI computing. You installed the ROCm software stack, verified GPU access, and ran a simple AI inference task. This foundation enables you to explore more complex AI applications on AMD hardware. As AMD continues to develop its AI ecosystem with products like the Helios rack, these skills will become increasingly valuable for leveraging next-generation AI infrastructure.