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#!/bin/bash
export NCCL_BLOCKING_WAIT=0
export TOKENIZERS_PARALLELISM=false
export OMP_NUM_THREADS=8
export NCCL_IB_DISABLE=0
export NCCL_IB_GID_INDEX=3
export NCCL_SOCKET_IFNAME=eth0
export NCCL_DEBUG=INFO
# CONFIG Huggingface
# export HF_TOKEN="<PLACEHOLDER_HF_TOKEN_1>"
export HF_TOKEN="<PLACEHOLDER_HF_TOKEN_2>"
export HF_HOME="$HOME/.cache/huggingface"
export HF_HUB_ENABLE_HF_TRANSFER="1"
export NCCL_DEBUG=INFO
GPUS="0,1,2,3,4,5,6,7"
# 取 worker0 第一个 port
ports=($(echo $METIS_WORKER_0_PORT | tr ',' ' '))
port=${ports[0]}
port_in_cmd="$(echo "${METIS_WORKER_0_PORT:-2000}" | awk -F',' '{print $1}')"
echo "total workers: ${ARNOLD_WORKER_NUM}"
echo "cur worker id: ${ARNOLD_ID}"
echo "gpus per worker: ${ARNOLD_WORKER_GPU}"
echo "master ip: ${METIS_WORKER_0_HOST}"
echo "master port: ${port}"
echo "master port in cmd: ${port_in_cmd}"
# export WANDB_BASE_URL=https://api.wandb.ai
# export WANDB_API_KEY="<PLACEHOLDER_WANDB_KEY_1>"
# wandb login $WANDB_API_KEY
export WANDB_BASE_URL=https://api.wandb.ai
export WANDB_PROJECT=vision-reasoning
export WANDB_API_KEY="<PLACEHOLDER_WANDB_KEY_2>"
export WANDB_RUN_NAME=Qwen-VL-2B-GRPO-$(date +%Y-%m-%d-%H-%M-%S)
wandb login $WANDB_API_KEY
cd /home/tiger/multimodal-open-r1
# pip3 install vllm==0.6.6.post1
pip3 install -e ".[dev]"
pip3 install wandb==0.18.3
torchrun --nproc_per_node="${ARNOLD_WORKER_GPU}" \
--nnodes="${ARNOLD_WORKER_NUM}" \
--node_rank="${ARNOLD_ID}" \
--master_addr="${METIS_WORKER_0_HOST}" \
--master_port="${port_in_cmd}" \
src/open_r1/grpo.py \
--deepspeed scripts/zero3.json \
--output_dir Aria-GRPO-mini_cot_80k \
--model_name_or_path rhymes-ai/Aria \
--dataset_name luodian/mini_cot_80k \
--max_prompt_length 8192 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 1 \
--logging_steps 1 \
--bf16 \
--report_to wandb \
--gradient_checkpointing true \
--attn_implementation eager \
--save_total_limit 8 \
--num_train_epochs 1 \
--run_name $WANDB_RUN_NAME
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