AWS Researchers Introduce Gemini: Pioneering Rapid Failure Recovery in Large-Scale Deep Learning Training

A team of researchers from Rice University and Amazon Web Services has developed a distributed training system called GEMINI, which aims to improve crash recovery in training large machine learning models. The system addresses challenges associated with using CPU memory for checkpointing, ensuring higher availability and minimizing interference with training traffic. GEMINI has shown significant improvement over existing solutions, making it a promising advancement in training large-scale deep learning models.

GEMINI has introduced a distributed training system to improve the recovery process in training large models. Previous solutions were limited by bandwidth and storage constraints, affecting checkpoint frequency and model accuracy even though deep learning frameworks such as PyTorch and TensorFlow offered checkpointing interfaces. GEMINI’s approach optimizes checkpoint placement and traffic scheduling, making it a valuable advancement in this field.

Deep learning models, especially large ones, have been recognized for their impressive performance. However, training large models often requires improvement due to its complexity and time consumption. Current solutions for crash recovery in large model training are hampered by limited bandwidth in remote storage, resulting in significant recovery costs. GEMINI has introduced innovative CPU memory techniques that enable rapid crash recovery. GEMINI’s strategies for optimal checkpoint placement and traffic scheduling have led to significantly faster failure recovery than existing solutions. He has made notable contributions to the field of deep learning.

GEMINI is based on Deep-Speed ​​and uses the ZeRO-3 configuration for distributed training. Amazon EC2 Auto Scaling groups are used to manage GPU model states. Checkpoints are stored in both CPU memory and remote storage, with a checkpoint frequency of three hours. GEMINI employs a near-optimal checkpoint placement strategy to maximize recovery probability and a traffic scheduling algorithm to reduce interference. The evaluation is performed on NVIDIA GPUs but is applied to other accelerators such as AWS Trainium.

GEMINI significantly improves failover, outperforming existing solutions by more than 13 times. The evaluation results confirm its effectiveness in reducing wasted time without compromising training performance. The scalability of GEMINI is evident across different failure frequencies and training scales, showing its potential for large-scale distributed training. The traffic interleaving algorithm in GEMINI positively influences training performance, further improving system efficiency.

Existing solutions for crash recovery in large model training are limited by the bandwidth of remote storage, which prevents high checkpoint frequencies and results in significant time loss. The study focuses on static and synchronous training with fixed computing resources, omitting consideration of elastic and asynchronous training methods. The issue of CPU memory size to store checkpoint history for purposes other than crash recovery is not addressed in the current research.

In conclusion, GEMINI is an efficient and scalable distributed training system that offers fast and reliable crash recovery using CPU memory checkpointing and an advanced placement strategy. Its high checkpoint frequencies help reduce wasted time without impacting training performance, making it an excellent solution for large-scale distributed training on GPU clusters.

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