Upload train/CCFM/pca_emb/run_precompute_sparse.sh with huggingface_hub

train/CCFM/pca_emb/run_precompute_sparse.sh

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+#!/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 "=========================================="