Upload ProDiff/run_a40_background.sh with huggingface_hub

ProDiff/run_a40_background.sh

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+#!/bin/bash
+
+# ===================================
+# A40 GPU优化训练脚本 - 后台运行版本
+# ===================================
+
+# 配置参数
+EXP_NAME="trajectory_a40_temporal_optimized"
+DEVICE_ID=0
+SAMPLING_TYPE="ddpm"
+LOG_DIR="./training_logs"
+
+# 创建日志目录
+mkdir -p ${LOG_DIR}
+
+# 获取当前时间戳用于日志文件命名
+TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
+LOG_FILE="${LOG_DIR}/training_${EXP_NAME}_${TIMESTAMP}.log"
+
+# 显示启动信息
+echo "====================================="
+echo "ProDiff A40优化训练启动"
+echo "====================================="
+echo "实验名称: ${EXP_NAME}"
+echo "GPU设备: ${DEVICE_ID}"
+echo "采样类型: ${SAMPLING_TYPE}"
+echo "日志文件: ${LOG_FILE}"
+echo "启动时间: $(date)"
+echo "====================================="
+
+# 检查GPU状态
+echo "检查GPU状态..."
+nvidia-smi
+
+# 设置环境变量以优化A40性能
+export CUDA_VISIBLE_DEVICES=${DEVICE_ID}
+export PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128
+export OMP_NUM_THREADS=8
+
+# 使用nohup后台运行，同时输出到日志文件和终端
+echo "开始后台训练..."
+nohup python -u main.py \
+    --exp_name ${EXP_NAME} \
+    --device_id ${DEVICE_ID} \
+    --sampling_type ${SAMPLING_TYPE} \
+    --seed 42 \
+    > ${LOG_FILE} 2>&1 &
+
+# 获取进程ID
+PID=$!
+echo "训练进程PID: ${PID}"
+echo "日志文件: ${LOG_FILE}"
+
+# 保存PID到文件，方便后续管理
+echo ${PID} > "${LOG_DIR}/training_${EXP_NAME}.pid"
+
+echo ""
+echo "====================================="
+echo "训练已在后台启动！"
+echo "====================================="
+echo "监控命令:"
+echo "  查看日志: tail -f ${LOG_FILE}"
+echo "  查看进程: ps aux | grep ${PID}"
+echo "  停止训练: kill ${PID}"
+echo "  或者: kill \$(cat ${LOG_DIR}/training_${EXP_NAME}.pid)"
+echo ""
+echo "GPU监控:"
+echo "  nvidia-smi"
+echo "  watch -n 1 nvidia-smi"
+echo "====================================="