A user on Reddit's r/MachineLearning subreddit is seeking guidance on how to automatically determine an optimal batch size for multi-GPU training using Accelerate with FSDP2. They are looking for a feature similar to Hugging Face's SFTTrainer, which can reduce the batch size to avoid CUDA Out Of Memory (OOM) errors. The user is inquiring if Accelerate can handle this automatically when FSDP2 encounters an OOM error, or if this functionality needs to be implemented externally. They are also open to alternative multi-GPU training approaches if automatic batch size detection is not well-supported with FSDP2. AI
IMPACT This query highlights a practical challenge in distributed AI training, indicating a need for more robust automatic resource management features in libraries like Accelerate and FSDP2.
RANK_REASON User question about implementing a specific feature in a software library.
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
