Graphics Memory Optimization Solutions
The following measures can be taken to address the problem of insufficient explicit memory for model training:
- Data batches: Decrease batch_size (recommended to start from 2)
- Mixing accuracy: Use torch.bfloat16 to reduce video memory usage
- gradient accumulation: Accumulation of gradients by multiple forward propagation
- Model streamlining::
- Try a smaller version of Janus-4o
- Remove unnecessary model components
- alternative::
- Free GPU Resources with Google Colab
- Consider model parallelism or data parallelism strategies
Note: Use torch.cuda.empty_cache() periodically to clean the cache and monitor the graphics memory usage
This answer comes from the articleShareGPT-4o-Image: an open source multimodal image generation datasetThe
May not be reproduced without permission:AI productivity tools " How to avoid the problem of insufficient memory in multimodal model training?
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