| # export NCCL_SOCKET_IFNAME=bond0 | |
| # export NCCL_IB_HCA=mlx5_2,mlx5_3 | |
| # export NCCL_DEBUG=INFO # 输出调试信息，帮助查找问题 | |
| # export NCCL_IB_DISABLE=1 # 禁用 InfiniBand，防止某些网络设备问题 | |
| # export NCCL_SOCKET_IFNAME=eth0 # 设置网络接口 | |
| # # used for check save when communication | |
| # export NCCL_BLOCKING_WAIT=1 | |
| # export NCCL_ASYNC_ERROR_HANDLING=1 | |
| # export NCCL_TIMEOUT=10000 # timeout set to 1 hour (unit: seconds) | |
| # export NCCL_SOCKET_TIMEOUT_MS=360000 | |
| ########################################################################################### | |
| # === Please modify the following paths according to hf_iukkofmmRdUqCdqdqclmFjSOktKYvSrOjMyour environment === | |
| ##Gr00t是125cubelr | |
| #oft是125cubeoftlibero | |
| Framework_name=QwenOFT | |
| freeze_module_list='' | |
| base_vlm=/inspire/qb-ilm/project/embodied-basic-model/zhangjianing-253108140206/model/cubev0-200000-Qwen3-VL | |
| config_yaml=./examples/LIBERO/train_files/starvla_cotrain_libero.yaml | |
| libero_data_root=/inspire/ssd/project/embodied-basic-model/zhangjianing-253108140206/experiment/starVLA/playground/Datasets/LEROBOT_LIBERO_DATA/libero | |
| data_mix=libero_all | |
| run_root_dir=./results/Checkpoints | |
| run_id=125_cube_oft_gr00t | |
| # === End of environment variable configuration === | |
| ########################################################################################### | |
| export WANDB_MODE=offline | |
| # export WANDB_MODE=disabled | |
| #examples/LIBERO/train_files/run_libero_train.sh | |
| output_dir=${run_root_dir}/${run_id} | |
| mkdir -p ${output_dir} | |
| # mv this script to the output dir | |
| cp $0 ${output_dir}/ | |
| accelerate launch \ | |
| --config_file starVLA/config/deepseeds/deepspeed_zero2.yaml \ | |
| --num_processes 4 \ | |
| starVLA/training/train_starvla.py \ | |
| --config_yaml ${config_yaml} \ | |
| --framework.name ${Framework_name} \ | |
| --framework.qwenvl.base_vlm ${base_vlm} \ | |
| --datasets.vla_data.data_root_dir ${libero_data_root}\ | |
| --datasets.vla_data.data_mix ${data_mix} \ | |
| --datasets.vla_data.per_device_batch_size 8 \ | |
| --trainer.vla_data.video_backend torchvision_av \ | |
| --trainer.freeze_modules ${freeze_module_list} \ | |
| --trainer.max_train_steps 30000 \ | |
| --trainer.save_interval 5000 \ | |
| --trainer.logging_frequency 10 \ | |
| --trainer.eval_interval 1000 \ | |
| --run_root_dir ${run_root_dir} \ | |
| --run_id ${run_id} \ | |
| --wandb_project wallx4libero \ | |
| --wandb_entity 1732949190-tongji-university \ | |
| # --is_debug True | |
| # #### Multi-Server Multi-GPU training script ##### | |
| # accelerate launch \ | |
| # --config_file starVLA/config/deepseeds/deepspeed_zero2.yaml \ | |
| # --main_process_ip $MASTER_ADDR \ | |
| # --main_process_port $MASTER_PORT \ | |
| # --machine_rank $SLURM_PROCID \ | |
| # --num_machines $SLURM_NNODES \ | |
| # --num_processes=${TOTAL_GPUS} \ | |
| # starVLA/training/train_starvla.py \ | |
| # --config_yaml ${config_yaml} \ | |
| # --framework.name ${Framework_name} \ | |
| # --framework.qwenvl.base_vlm ${base_vlm} \ | |
| # --run_root_dir ${run_root_dir} \ | |
| # --run_id ${run_id} \ | |
| # --wandb_project your_project \ | |
| # --wandb_entity your_name | |
| # ##### Multi-Server Multi-GPU training script ##### | |