[D] What debugging info do you wish you had when training jobs fail?
Published:Dec 27, 2025 20:31
•1 min read
•r/MachineLearning
Analysis
This is a valuable post from a developer seeking feedback on pain points in PyTorch training debugging. The author identifies common issues like OOM errors, performance degradation, and distributed training errors. By directly engaging with the MachineLearning subreddit, they aim to gather real-world use cases and unmet needs to inform the development of an open-source observability tool. The post's strength lies in its specific questions, encouraging detailed responses about current debugging practices and desired improvements. This approach ensures the tool addresses genuine problems faced by practitioners, increasing its potential adoption and impact within the community. The offer to share aggregated findings further incentivizes participation and fosters a collaborative environment.
Key Takeaways
- •Debugging PyTorch training workflows is a significant challenge for practitioners.
- •Common failure modes include OOM errors, performance degradation, and distributed training issues.
- •Better tooling and observability are needed to improve the debugging experience.
Reference
“What types of failures do you encounter most often in your training workflows? What information do you currently collect to debug these? What's missing? What do you wish you could see when things break?”