Support accelerate training and add test configs for SmolVLA

- 2-GPU SLURM job (distributed training)
- 1-GPU local accelerate and direct training scripts
- Accelerate configs for 1-GPU and 2-GPU setups

test_accelerate_2gpu.slurm

@@ -0,0 +1,67 @@
+#!/bin/bash
+#SBATCH --job-name=test_accelerate
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=16
+#SBATCH --gres=gpu:2
+#SBATCH --time=1:00:00
+#SBATCH --partition=hopper-prod
+#SBATCH --output=/fsx/dana_aubakirova/vla/logs/test_accelerate_%j.out
+#SBATCH --error=/fsx/dana_aubakirova/vla/logs/test_accelerate_%j.err
+
+# Create logs directory if it doesn't exist
+mkdir -p /fsx/dana_aubakirova/vla/pr/lerobot/logs
+
+# Activate conda environment
+source /fsx/dana_aubakirova/miniconda3/etc/profile.d/conda.sh
+conda activate multi
+
+# 2-GPU Test CUDA environment
+export CUDA_VISIBLE_DEVICES=0,1
+export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128,expandable_segments:True
+export TORCH_DISTRIBUTED_DEBUG=OFF
+export NCCL_DEBUG=INFO
+export CUDA_LAUNCH_BLOCKING=0
+export ACCELERATE_USE_FSDP=false
+export ACCELERATE_USE_DEEPSPEED=false
+export HF_ACCELERATE_DEVICE_MAP=false
+export TRANSFORMERS_NO_ADVISORY_WARNINGS=1
+export SAFETENSORS_FAST_GPU=1
+export HF_HUB_ENABLE_HF_TRANSFER=1
+export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
+export ACCELERATE_TORCH_DEVICE_MAP_AUTO=false
+
+# Change to working directory
+cd /fsx/dana_aubakirova/vla/pr/lerobot
+
+echo "=== Testing Accelerate Multi-GPU Training with SmolVLA ==="
+echo "Dataset: lerobot/svla_so100_sorting"
+echo "GPUs: 2"
+echo "Steps: 100 (for quick test)"
+echo "Job ID: $SLURM_JOB_ID"
+echo ""
+
+# Set output directory with job ID
+export OUTPUT_DIR="outputs/test_accelerate_2gpu_job_${SLURM_JOB_ID}"
+
+echo "Output directory: $OUTPUT_DIR"
+echo ""
+
+# Test accelerate training
+accelerate launch --config_file accelerate_configs/2gpu_config_safe.yaml -m lerobot.scripts.train \
+    --policy.type=smolvla \
+    --policy.push_to_hub=false \
+    --dataset.repo_id=lerobot/svla_so100_sorting \
+    --dataset.video_backend=pyav \
+    --steps=100 \
+    --save_freq=50 \
+    --log_freq=5 \
+    --batch_size=2 \
+    --num_workers=0 \
+    --output_dir=$OUTPUT_DIR \
+    --wandb.enable=false
+
+echo ""
+echo "=== Training completed! ==="
+echo "Check logs and outputs in: $OUTPUT_DIR"
+echo "Job ID: $SLURM_JOB_ID"