add train

examples/7_train_libero_smolvla.sh

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+#!/bin/bash
+# smolvla training
+
+set -euo pipefail
+
+# repo/env
+cd ~/lerobot || exit 1
+# conda activate lerobot
+export LC_ALL=C
+
+
+rm -f core-*
+
+# storage / caches (use RAID to avoid filling $HOME)
+RAID=/raid/jade
+export TRANSFORMERS_CACHE=$RAID/.cache/huggingface/transformers
+export HF_HOME=$RAID/.cache/huggingface
+export HF_DATASETS_CACHE=$RAID/.cache/huggingface/datasets
+export HF_LEROBOT_HOME=$RAID/.cache/huggingface/lerobot
+export WANDB_CACHE_DIR=$RAID/.cache/wandb
+export TMPDIR=$RAID/.cache/tmp
+mkdir -p $TMPDIR
+export WANDB_MODE=offline
+export HF_DATASETS_OFFLINE=1
+export HF_HUB_OFFLINE=1
+export TOKENIZERS_PARALLELISM=false
+export MUJOCO_GL=egl
+
+# will only use if accelerate is used
+PORT=29522
+
+# =================== CONFIG ===================
+ENV=libero
+TASK=libero_spatial
+REPO_ID=physical-intelligence/libero
+
+POLICY=smolvla
+VLM=HuggingFaceTB/SmolVLM2-2.2B-Instruct
+
+# Optim / scheduling
+LR=1e-4
+DECAY_LR=2.5e-6
+DECAY_STEPS=30000
+USE_AMP=false
+TRAIN_EXPERT_ONLY=true
+N_ACTION_STEPS=1
+SEED=1000
+
+# Training loop
+OFFLINE_STEPS=100000
+BATCH_SIZE=32
+EVAL_FREQ=0
+SAVE_FREQ=300000
+EVAL_BATCH_SIZE=1
+NUM_EPISODES=1
+
+# GPU selection 0, 1, 2, 3
+export CUDA_VISIBLE_DEVICES=1
+
+# naming/output dir
+TRAIN_DIR=$RAID/logs/lerobot/lerobot_${REPO_ID//\//_}_${POLICY}_lr${LR}bs${BATCH_SIZE}steps${OFFLINE_STEPS}
+echo "Training dir: $TRAIN_DIR"
+
+# train
+rm -rf "$TRAIN_DIR"
+
+python src/lerobot/scripts/train.py \
+  --policy.type=$POLICY \
+  --policy.vlm_model_name=$VLM \
+  --dataset.repo_id=$REPO_ID \
+  --dataset.root=$HF_DATASETS_CACHE \
+  --env.type=$ENV \
+  --env.task=$TASK \
+  --output_dir=$TRAIN_DIR \
+  --batch_size=$BATCH_SIZE \
+  --steps=$OFFLINE_STEPS \
+  --eval_freq=$EVAL_FREQ \
+  --save_freq=$SAVE_FREQ \
+  --eval.batch_size=$EVAL_BATCH_SIZE \
+  --eval.n_episodes=$NUM_EPISODES \
+  --policy.use_amp=$USE_AMP \
+  --policy.optimizer_lr=$LR \
+  --policy.repo_id=None \
+  --policy.scheduler_decay_lr=$DECAY_LR \
+  --policy.scheduler_decay_steps=$DECAY_STEPS \
+  --policy.n_action_steps=$N_ACTION_STEPS \
+  --policy.train_expert_only=$TRAIN_EXPERT_ONLY \
+  --policy.vlm_model_name=/raid/jade/.cache/huggingface/models/SmolVLM2-2.2B-Instruct \
+  --seed=$SEED \
+  --wandb.enable=false