| # Rate-matched eval: eval_rep_aloha.sh <gpu> <job> <step> <repeat> <task> (episode_length = 400/repeat so sim budget stays 400 steps) | |
| GPU=$1; JOB=$2; ST=$3; N=$4; TASK=$5; LEN=${6:-$((400 / N))}; TAG=${7:-}; cd /workspace/isr && source .venv/bin/activate | |
| export HF_HOME=/workspace/.hf_home CUDA_VISIBLE_DEVICES=$GPU MUJOCO_GL=egl MUJOCO_EGL_DEVICE_ID=$GPU ISR_GYM_ALOHA=1 ISR_ALOHA_DEG=1 ISR_ACTION_REPEAT=$N PYTHONPATH=/workspace/isr | |
| OUT=results/${JOB}_${ST}_rep${N}${TAG}; python -c "import gym_aloha, runpy, sys; sys.argv[0]='lerobot_eval'; runpy.run_module('lerobot.scripts.lerobot_eval', run_name='__main__')" --policy.path=ckpts/$JOB/checkpoints/$ST/pretrained_model --policy.device=cuda --env.type=aloha --env.task=$TASK --env.episode_length=$LEN --eval.n_episodes=${EPS:-100} --eval.batch_size=25 --eval.use_async_envs=false --output_dir=$OUT > logs/eval_${JOB}_${ST}_rep${N}${TAG}.log 2>&1 | |
| echo "EVAL_DONE $JOB $ST rep$N$TAG $(python -c "import json;print(json.load(open('$OUT/eval_info.json'))['overall']['pc_success'])" 2>/dev/null || echo FAILED)" | |