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export NCCL_IB_HCA=mlx5_0:1,mlx5_1:1,mlx5_2:1,mlx5_3:1,mlx5_4:1,mlx5_7:1,mlx5_8:1,mlx5_9:1
export NCCL_IB_DISABLE=0
export NCCL_SOCKET_IFNAME=bond0
export NCCL_DEBUG=INFO
export NCCL_NVLS_ENABLE=0
export TEXT_ENCODER_NAME="google/t5-v1_1-xxl"
export VISION_ENCODER_NAME="google/siglip-so400m-patch14-384"
export OUTPUT_DIR="./checkpoints/rdt-finetune-1b-sim"
export CFLAGS="-I/usr/include"
export LDFLAGS="-L/usr/lib/x86_64-linux-gnu"
export CUTLASS_PATH="/data/lingxuan/cutlass"
export WANDB_PROJECT="robotic_diffusion_transformer"
if [ ! -d "$OUTPUT_DIR" ]; then
mkdir "$OUTPUT_DIR"
echo "Folder '$OUTPUT_DIR' created"
else
echo "Folder '$OUTPUT_DIR' already exists"
fi
# For run in a single node/machine
# accelerate launch main.py \
# --deepspeed="./configs/zero2.json" \
# ...
accelerate launch main.py \
--deepspeed="./configs/zero2.json" \
--pretrained_model_name_or_path="robotics-diffusion-transformer/rdt-1b" \
--pretrained_text_encoder_name_or_path=$TEXT_ENCODER_NAME \
--pretrained_vision_encoder_name_or_path=$VISION_ENCODER_NAME \
--output_dir=$OUTPUT_DIR \
--train_batch_size=24 \
--sample_batch_size=32 \
--max_train_steps=400000 \
--checkpointing_period=10000 \
--sample_period=500 \
--checkpoints_total_limit=40 \
--lr_scheduler="constant" \
--learning_rate=1e-4 \
--mixed_precision="bf16" \
--dataloader_num_workers=8 \
--image_aug \
--dataset_type="finetune" \
--state_noise_snr=40 \
--load_from_hdf5 \
--report_to=wandb
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