22dad1df73
The start route's precision preflight folded bf16/int8/fp8 into the CUDA requirement but omitted mxfp8, so an mxfp8 request on a GPU-less host (or an older CUDA GPU without Blackwell) passed the preflight, evicted resident image and chat models, then raised only in the spawned trainer child. Mirror _resolve_base_precision: require CUDA for mxfp8 and re-check the Blackwell (sm100+) capability up front, so a doomed run is rejected before teardown. |
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| .. | ||
| __init__.py | ||
| diffusion_dit_trainer.py | ||
| diffusion_lora_trainer.py | ||
| diffusion_train_common.py | ||
| diffusion_training_service.py | ||
| resume.py | ||
| s3_dataset.py | ||
| trainer.py | ||
| training.py | ||
| worker.py | ||