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2bea32f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | #!/bin/bash
#PJM -L rscgrp=b-batch
#PJM -L gpu=1
#PJM -L elapse=4:00:00
#PJM -N precompute_sparse
#PJM -j
#PJM -o logs/precompute_sparse_%j.out
module load cuda/12.2.2
module load cudnn/8.9.7
module load gcc-toolset/12
source /home/pj24002027/ku50002536/Takoai/lfj/lfj/stack_env/bin/activate
# 在 grn_ccfm 目录运行（precompute 脚本依赖 grn_ccfm 的 src/）
cd /home/pj24002027/ku50002536/Takoai/lfj/lfj/GRN/grn_ccfm
export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256
echo "=========================================="
echo "Job ID: $PJM_JOBID"
echo "Job Name: $PJM_JOBNAME"
echo "Start: $(date)"
echo "Node: $(hostname)"
echo "GPU: $(nvidia-smi --query-gpu=name,memory.total --format=csv,noheader 2>/dev/null || echo 'N/A')"
echo "=========================================="
# 输出到 grn_ccfm/cache/ (pca_emb 的两个 shell 脚本引用此路径)
mkdir -p cache
python scripts/precompute_sparse_attn.py \
--data-name norman \
--n-top-genes 5000 \
--fold 1 \
--split-method additive \
--topk 30 \
--use-negative-edge \
--scgpt-model-dir transfer/data/scGPT_pretrained \
--max-seq-len 5000 \
--attn-layer 11 \
--attn-use-rank-norm \
--batch-size 2 \
--top-k 300 \
--n-pca-pairs 1000 \
--max-pca-components 64 \
--output cache/norman_attn_L11_sparse.h5 \
--device cuda
echo "=========================================="
echo "Finished: $(date)"
echo "Output: cache/norman_attn_L11_sparse.h5"
echo "=========================================="
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