Sure! Here’s the translation into American English:
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Organizations expanding their artificial intelligence infrastructure to encompass models with trillions of parameters face a challenging dilemma: finding a balance between reducing training time at a lower cost or accelerating the process while incurring higher expenses. The technique of “checkpointing” has become crucial in this context, as it allows for faster recovery times and minimizes losses during training. However, its implementation can lead to high storage costs. Choosing to perform checkpointing infrequently reduces costs but increases the risk of losing significant training progress, especially in the face of common failures in distributed environments that utilize thousands of graphic processors.
During the training process of the Meta Llama 3 model, failures were reported every three hours, 60% of which were attributable to GPU issues. The remaining failures were due to problems related to networks, CPUs, and disks. This instability can result in the loss of days of progress, which not only increases costs and time to market but also complicates project planning.
Recognizing these challenges, AWS has introduced managed tiered checkpointing through Amazon SageMaker HyperPod, an infrastructure designed to scale and accelerate the development of generative AI models. This innovative solution leverages CPU memory for high-performance checkpoint storage, automatically replicating data across adjacent compute nodes, thereby enhancing system reliability.
SageMaker HyperPod not only automatically identifies issues in the nodes and replaces affected ones to resume the training process but also aids in implementing optimal checkpointing strategies, maximizing training performance. This feature has been tested in large distributed training clusters, with configurations ranging from hundreds to over 15,000 GPUs, successfully saving checkpoints in a matter of seconds.
The implementation of this feature does not require advanced technical expertise and can be easily incorporated into PyTorch training scripts. Additionally, managed tiered checkpointing allows organizations to set both the frequency and retention policies for in-memory and persistent storage, using Amazon S3 as an additional option. This technology significantly optimizes recovery times and checkpoint management compared to traditional approaches that rely on remote persistent storage.
The best results are achieved by configuring checkpoint writing in memory frequently, while copies to Amazon S3 can be performed less frequently. With these capabilities, the combination of managed tiered checkpointing and SageMaker HyperPod promises to maintain high training performance even in large-scale environments prone to failures.
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Let me know if you need anything else!
via: MiMub in Spanish
