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Hello! I encountered an OOM issue during training. I am using 4×4090 (24GB). Does this fail to meet the minimum memory requirement?
Here are my training settings:
torchrun --nproc_per_node="4" --nnodes="1" --node_rank="0" --master_addr="127.0.0.1" --master_port="12346" src/open_r1/grpo_rec.py --deepspeed local_scripts/zero3.json --output_dir output/$RUN_NAME --model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct --dataset_name --image_root --max_prompt_length 1024 --num_generations 4 --per_device_train_batch_size 1 --gradient_accumulation_steps 2 --logging_steps 1 --bf16 --torch_dtype bfloat16 --data_seed 42 --report_to none --gradient_checkpointing true --attn_implementation flash_attention_2 --num_train_epochs 2 --run_name $RUN_NAME --save_steps 100 --save_only_model true
The text was updated successfully, but these errors were encountered:
不够用的,需要6*24G。但GPU够用了,还是出现了内核问题,你的内核是多少;uname -r命令可以查看
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6.8.0-51-generic 是这个
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Hello! I encountered an OOM issue during training. I am using 4×4090 (24GB). Does this fail to meet the minimum memory requirement?
Here are my training settings:
torchrun --nproc_per_node="4"
--nnodes="1"
--node_rank="0"
--master_addr="127.0.0.1"
--master_port="12346"
src/open_r1/grpo_rec.py
--deepspeed local_scripts/zero3.json
--output_dir output/$RUN_NAME
--model_name_or_path Qwen/Qwen2.5-VL-3B-Instruct
--dataset_name
--image_root
--max_prompt_length 1024
--num_generations 4
--per_device_train_batch_size 1
--gradient_accumulation_steps 2
--logging_steps 1
--bf16
--torch_dtype bfloat16
--data_seed 42
--report_to none
--gradient_checkpointing true
--attn_implementation flash_attention_2
--num_train_epochs 2
--run_name $RUN_NAME
--save_steps 100
--save_only_model true
The text was updated successfully, but these errors were encountered: