Upload sbatch.sh

sbatch.sh

@@ -0,0 +1,130 @@
+#!/bin/bash
+# ==========================================================
+# 用法：
+#   sbatch submit_job.sh <gpu_num> <job_name> [debug|long]
+#
+# 参数说明：
+#   <gpu_num>    : GPU 数量（必填，如 0 / 1 / 2）
+#   <job_name>   : 任务名称（必填）
+#   [debug|long] : 可选，debug=2小时，long=24小时，默认12小时
+#
+# 资源规则：
+#   每块 GPU 分配 16 个 CPU 核
+#   每个 CPU 核分配 8G 内存
+#   若 GPU=0，则默认申请 16C + 128G
+#
+# 示例：
+#   sbatch submit_job.sh 1 train-job
+#   sbatch submit_job.sh 2 bigjob long
+#   sbatch submit_job.sh 1 debug-test debug
+#   sbatch submit_job.sh 0 cpuonly
+# ==========================================================
+
+GPU_NUM="$1"
+JOB_NAME="$2"
+MODE="$3"
+
+# 参数检查
+if [[ -z "$GPU_NUM" || -z "$JOB_NAME" ]]; then
+    echo "❌ 参数错误！"
+    echo "用法: sbatch submit_job.sh <gpu_num> <job_name> [debug|long]"
+    exit 1
+fi
+
+if ! [[ "$GPU_NUM" =~ ^[0-9]+$ ]]; then
+    echo "❌ 错误：gpu_num 必须是非负整数"
+    exit 1
+fi
+
+# 默认参数（12小时）
+PARTITION="q-dyvm6xra"
+QOS="qos_g5_12hrs"
+TIME_LIMIT="12:00:00"
+
+if [[ "$MODE" == "debug" ]]; then
+    PARTITION="q-g7hdnrvb"
+    QOS=""
+    TIME_LIMIT="2:00:00"
+elif [[ "$MODE" == "long" ]]; then
+    PARTITION="q-dyvm6xra"
+    QOS="qos_g5_24hrs"
+    TIME_LIMIT="24:00:00"
+elif [[ -n "$MODE" ]]; then
+    echo "❌ 错误：第三个参数只能是 debug 或 long"
+    exit 1
+fi
+
+# 资源配置
+CPU_PER_GPU=16
+MEM_PER_CPU=8G
+
+if [[ "$GPU_NUM" -eq 0 ]]; then
+    TOTAL_CPU=16
+    TOTAL_MEM="128G"
+    GPU_DIRECTIVE=""
+else
+    TOTAL_CPU=$(( GPU_NUM * CPU_PER_GPU ))
+    TOTAL_MEM="$(( TOTAL_CPU * 8 ))G"
+    GPU_DIRECTIVE="#SBATCH --gres=gpu:${GPU_NUM}"
+fi
+
+# 输出信息
+echo "=========================================================="
+echo "✅ 提交 sbatch 参数："
+echo "GPU 数量     : ${GPU_NUM}"
+echo "CPU 数量     : ${TOTAL_CPU}"
+echo "总内存       : ${TOTAL_MEM}"
+echo "任务名称     : ${JOB_NAME}"
+echo "分区         : ${PARTITION}"
+echo "时间限制     : ${TIME_LIMIT}"
+echo "QOS          : ${QOS:-"-"}"
+echo "=========================================================="
+
+# 生成临时 sbatch 脚本并提交
+TMP_SCRIPT=$(mktemp /tmp/${JOB_NAME}.XXXXXX.sbatch)
+
+cat > "$TMP_SCRIPT" <<EOF
+#!/bin/bash
+#SBATCH -p ${PARTITION}
+#SBATCH -A dyvm6xra
+#SBATCH -J ${JOB_NAME}
+#SBATCH -c ${TOTAL_CPU}
+#SBATCH --mem=${TOTAL_MEM}
+#SBATCH -t ${TIME_LIMIT}
+#SBATCH -o ${JOB_NAME}.%j.out
+#SBATCH -e ${JOB_NAME}.%j.err
+EOF
+
+if [[ -n "$QOS" ]]; then
+    echo "#SBATCH -q ${QOS}" >> "$TMP_SCRIPT"
+fi
+
+if [[ -n "$GPU_DIRECTIVE" ]]; then
+    echo "${GPU_DIRECTIVE}" >> "$TMP_SCRIPT"
+fi
+
+cat >> "$TMP_SCRIPT" <<'EOF'
+
+echo "Job started on $(hostname)"
+echo "Start time: $(date)"
+echo "CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
+
+# ===== 在这里写你的实际任务命令 =====
+# 例子：
+# cd /path/to/your/project
+# source ~/.bashrc
+# conda activate your_env
+# python train.py
+
+cd /home/dyvm6xra/dyvm6xrauser11/workspace/cz/FlowCache/FlowCache4MAGI-1
+source ~/.bashrc
+conda activate magi
+bash scripts/sample/flowcache_vbench.sh
+
+echo "请把实际运行命令写到脚本里"
+sleep 60
+
+echo "End time: $(date)"
+EOF
+
+sbatch "$TMP_SCRIPT"