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Train AI for less: Improve ML Goodput with elastic training and optimized checkpointing
- Improving ML Goodput is crucial for cost savings and efficiency in model training, but faces challenges like frequent interruptions and slow checkpointing.
- To enhance ML Goodput, strategies like elastic training, asynchronous checkpointing, and multi-tier checkpointing can be employed.
- Elastic training involves automatic scaling and remediation actions to minimize disruptions, ensuring resilient and adaptable training.
- Optimized checkpointing focuses on reducing overhead and improving recovery times by using multi-tier storage and tuning checkpoint frequency.
- These techniques leverage NVIDIA Resiliency Extension and PyTorch's distributed checkpointing to optimize ML Goodput.
- In a case study with 1,024 A3 Mega GPU instances, ML Goodput improved significantly through a combination of these techniques.
- Elastic training and optimized checkpointing, coupled with easy deployment options, are essential for maximizing ML Goodput in PyTorch workloads.
- These strategies are customizable through Python scripts and offer efficiency savings for large-scale training workloads on Google Cloud.
- The project involved contributions from various teams and individuals at Google Cloud, aiming to optimize ML Goodput and achieve cost savings.
- The improvements in ML Goodput can result in significant efficiency gains, as demonstrated in the case study with A3 Ultra pricing.
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