Note: At the AWS re:Invent Machine Learning Keynote we announced performance records for T5-3B and Mask-RCNN. This blog post includes updated numbers with additional optimizations since the keynote aired live on 12/8.
At re:Invent 2019, we demonstrated the fastest training times on the cloud for Mask R-CNN, a popular instance segmentation model, and BERT, a popular natural language processing (NLP) model. Over the past several months, we have worked in collaboration with NVIDIA to significantly improve the underlying infrastructure, network, machine learning (ML) framework, and model code to once again achieve the best training times for state-of-the-art models used by our customers. Today, we’re excited to share with you the fastest training times for Mask R-CNN on TensorFlow and PyTorch and T5-3B (NLP) on PyTorch, and dive deep into the technology stack, our optimizations, and how you can leverage these capabilities to train large models quickly with Amazon SageMaker.
Our customers training deep neural network models in PyTorch and TensorFlow have asked for help with problems they face with training speed and model size. First, customers told us they wanted to train models faster without waiting days or weeks for results. Data scientists need to iterate daily to get ML applications to market faster. Second, customers told us they struggled to apply the latest research in NLP because these model architectures didn’t fit in a single NVIDIA GPU’s memory during training. Customers knew they could get higher accuracy from these larger models with billions of parameters. But there was no easy way to automatically and efficiently split a model across multiple NVIDIA GPUs.
To solve these problems, AWS released new SageMaker distributed training libraries, which provide the easiest and fastest way to train deep learning models. The SageMaker data parallelism library provides better scaling efficiency than Horovod or PyTorch’s Distributed Data Parallel (DDP), and its model parallelism library automatically splits large models across multiple GPUs. In this post, we describe how this underlying technology was used to achieve record training times for Mask R-CNN and T5-3B.
Object detection algorithms form the backbone of many deep learning applications. Self-driving cars, security systems, and image processing all incorporate object detection. In particular, Mask R-CNN is ubiquitous in this field. Mask R-CNN takes in an image and then isolates and identifies objects within that image, providing both a bounding box and object mask. Since it was first proposed in 2017, training Mask R-CNN on the COCO dataset has become the standard benchmark for object detection models, and many of our customers use this as their baseline to buil
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