| agent="vadv2_4096_pdm_rel_extra" | |
| # cache="navtrain_vadv2_4f_cache" | |
| cache="navtrain_vadv2+map_img256x1024_cache" | |
| bs=32 | |
| lr=0.0001 | |
| ngc batch run \ | |
| -in dgx1v.32g.8.norm \ | |
| --ace nv-us-west-2 \ | |
| --label _wl___computer_vision \ | |
| -n ml-model.lzx_train._wl___computer_vision \ | |
| --result /result \ | |
| -i nvcr.io/nvidian/swaiinf/lzx-navsim \ | |
| --workspace q-2TlPKESo62ktTxOc8rYg:/zhenxinl_nuplan \ | |
| --port 6007 \ | |
| --commandline " | |
| git pull; | |
| python \${NAVSIM_DEVKIT_ROOT}/navsim/planning/script/run_training.py \ | |
| agent=$agent \ | |
| experiment_name=${agent}_ckpt \ | |
| cache_path=\${NAVSIM_EXP_ROOT}/$cache \ | |
| agent.config.ckpt_path=${agent}_ckpt \ | |
| split=trainval \ | |
| dataloader.params.batch_size=$bs \ | |
| agent.lr=$lr \ | |
| scene_filter=navtrain" |