Accelerate Your Machine Learning Workloads on AWS with Neuron SDK
In the era of artificial intelligence, cutting-edge models are reshaping industries and revolutionizing our daily lives. Behind these advancements are powerful AI accelerators like NVIDIA H100 GPUs, Google Cloud TPUs, and AWS’s Trainium and Inferentia chips, among others. Selecting the optimal platform for our machine learning (ML) workloads is crucial due to the high costs associated with AI computation. To fully leverage the capabilities of these AI accelerators, it is essential to optimize our ML workloads.
In this blog post, we will explore various techniques for optimizing ML workloads on AWS’s custom-built AI chips using the AWS Neuron SDK. This is part of our series focused on analyzing and optimizing ML model performance across different platforms and environments. While we will mainly discuss ML training workloads and AWS Inferentia, the techniques can also be applied to AWS Trainium. Performance optimization is a continuous process that involves identifying bottlenecks and underutilized resources. In this post, we will delve into specific optimization techniques without delving into performance analysis, which will be covered in a future post.
Disclaimer: The code shared in this post is for demonstration purposes only. We recommend consulting the official Neuron SDK documentation for accurate and robust information. The experiments were conducted on an Amazon EC2 inf2.xlarge instance with the latest version of the Deep Learning AMI for Neuron. Keep in mind that the Neuron SDK is constantly evolving, so it’s essential to stay updated with the latest SDK and documentation.
To illustrate these optimization techniques, we utilized a simple Vision Transformer (ViT)-backed classification model. We implemented various optimizations in our experiments and measured their impact on the training speed. Let’s delve into some of the strategies we explored:
1. Multi-process data loading: Overlapping data loading and training to increase system utilization.
2. Batch size optimization: Adjusting the batch size to improve system performance.
3. PyTorch Automatic Mixed Precision: Using lower precision floats to accelerate performance.
4. OpenXLA optimizations: Leveraging optimizations offered by the PyTorch/XLA API.
5. Neuron-specific optimizations: Harnessing the power of the Neuron compiler for optimizations.
Each optimization technique yielded varying results in terms of training speed improvement. By implementing a combination of these strategies, we achieved a 435% performance boost compared to our baseline experiment. While these optimizations significantly enhanced training speed, it is crucial to assess their impact on model convergence and accuracy in a real-world scenario.
In conclusion, optimizing ML workloads on AWS Inferentia with the Neuron SDK offers tremendous potential for accelerating AI computations. By utilizing these optimization techniques and staying informed about the latest advancements in AI accelerators, we can maximize the capabilities of our chosen platform. Stay tuned for more optimization strategies across different AI accelerators in our future posts.