Download train_hyperbolic_scene.py from lzhts1/ProWorld: direct link, hf CLI and curl.
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https://huggingface.co/datasets/lzhts1/ProWorld/resolve/main/train_hyperbolic_scene.py
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hf download hf://datasets/lzhts1/ProWorld/train_hyperbolic_scene.py
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curl -L -o train_hyperbolic_scene.py https://huggingface.co/datasets/lzhts1/ProWorld/resolve/main/train_hyperbolic_scene.py
919 Bytes
| """ | |
| Train the HyperbolicJEPA variant on OGBench Scene. | |
| Example: | |
| MUJOCO_GL=egl python train_hyperbolic_scene.py \ | |
| trainer.accelerator=gpu \ | |
| trainer.devices=1 \ | |
| trainer.max_epochs=10 \ | |
| loader.batch_size=128 \ | |
| subdir=ogbench_scene/hyperbolic_scene \ | |
| wandb.enabled=False | |
| If your local cache uses a different dataset layout, override the dataset name: | |
| data.dataset.name=/path/to/visual-scene-play-v0.h5 | |
| """ | |
| import faulthandler | |
| import hydra | |
| try: | |
| from torch.distributed.elastic.multiprocessing.errors import record | |
| except Exception: | |
| def record(fn): | |
| return fn | |
| from train_hyperbolic import run_hyperbolic_training | |
| def run(cfg): | |
| run_hyperbolic_training(cfg) | |
| def _main(): | |
| faulthandler.enable(all_threads=True) | |
| run() | |
| if __name__ == "__main__": | |
| _main() | |