mirror of
https://github.com/RYDE-WORK/lnp_ml.git
synced 2026-07-22 05:49:55 +08:00
39 lines
1.3 KiB
Bash
39 lines
1.3 KiB
Bash
#!/usr/bin/env bash
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set -uo pipefail
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# ===== 可调参数（用环境变量覆盖）=====
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GPU=${GPU:-1}
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SEED=${SEED:-42}
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BATCH=${BATCH:-8} # OOM 就设 4
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N_OUTER=${N_OUTER:-5}
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N_INNER=${N_INNER:-3}
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N_TRIALS=${N_TRIALS:-20}
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EPOCHS=${EPOCHS:-20} # 每个 trial 最大 epoch
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PATIENCE=${PATIENCE:-5} # 内层早停耐心值（配合 min_delta 生效）
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# 固定运行目录（按 seed 区分）→ 断点续跑的关键
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RUN_DIR="models/abl_qwen_softrag/seed${SEED}"
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export TRANSFORMERS_OFFLINE=1
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export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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export TOKENIZERS_PARALLELISM=false
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mkdir -p "${RUN_DIR}"
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echo "[$(date)] START seed=${SEED} gpu=${GPU} batch=${BATCH} dir=${RUN_DIR}"
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CUDA_VISIBLE_DEVICES=${GPU} python -m lnp_ml.modeling.nested_cv_optuna \
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--input-path data/interim/internal.csv \
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--output-dir models/abl_qwen_softrag \
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--resume-dir "${RUN_DIR}" \
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--seed ${SEED} \
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--use-llm --use-rag --rag-top-k 4 \
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--use-soft-prompt --llm-use-qlora \
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--llm-model-path models/qwen2.5-7b-instruct \
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--no-llm-freeze \
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--n-outer-folds ${N_OUTER} --n-inner-folds ${N_INNER} \
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--n-trials ${N_TRIALS} --epochs-per-trial ${EPOCHS} \
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--inner-patience ${PATIENCE} \
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--batch-size ${BATCH} --device cuda \
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2>&1 | tee -a "${RUN_DIR}/run.log"
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echo "[$(date)] DONE seed=${SEED} (exit=${PIPESTATUS[0]})" |