The Data Exchange with Ben Lorica Building An Experiment Tracker for Foundation Model Training
Sep 26, 2024
Aurimas Griciūnas, Chief Product Officer at Neptune.AI, dives into the complexities of training large language models and the critical need for effective experiment tracking. He discusses the transition from MLOps to LLMOps and how traditional tools struggle with the data demands of foundation models. Griciūnas highlights the challenges of operating massive GPU clusters and the importance of checkpoints for fault tolerance. The episode also covers breakthroughs in AI reasoning and the fine-tuning approaches essential for enterprises navigating this evolving landscape.
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LLMOps Scaling Challenges
- LLMOps scaling challenges arise from the sheer size of models and data.
- Massive clusters, long training times, and vast metric logging create bottlenecks for traditional tools.
Scale of Frontier Model Training
- Frontier model training clusters now utilize 100,000+ GPUs across multiple data centers.
- Training times extend to months, making fault tolerance crucial due to the immense computational cost.
Visualizations Saving the Day
- Visualizations within Neptune.ai have helped LLM training teams identify restart procedure errors.
- By comparing overlapping metric curves from restarted runs, discrepancies reveal potential issues in code or configurations.