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MLOps.community

The Art and Science of Training LLMs // Bandish Shah and Davis Blalock // #219

Mar 22, 2024
01:15:11
Snipd AI
Exploring the challenges of training large language models, including debugging issues and evaluating machine learning models effectively. The discussion covers the importance of data quality, efficient computation techniques, and optimizing machine learning model training and deployment for successful outcomes.
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Quick takeaways

  • Training large language models faces challenges of hardware failures, software instability, and complex debugging processes.
  • Ensuring model quality demands diverse evaluation metrics and meticulous debugging to address unexpected challenges.

Deep dives

The Challenges of Training Large Language Models

Training large language models involves numerous challenges, including hardware failures, software instability due to frequent breaking changes in libraries like PyTorch, and complex debugging processes. With thousands of GPUs crunching numbers, hardware failures can occur at any time, leading to issues like Nickel timeouts. Additionally, software stacks are not yet mature, causing disruptions with breaking changes and the need for constant maintenance to adjust to evolving environments.

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