[LLM] First commit the llm deployment code

scripts/run_unittest.sh

@@ -0,0 +1,63 @@
+#!/bin/bash
+set -x
+set -e
+cd FastDeploy
+
+export no_proxy=agent.baidu.com:8118,localhost,127.0.0.1,localaddress,.localdomain.com,.cdn.bcebos.com,.baidu.com,bcebos.com
+export https_proxy=
+export http_proxy=
+export LC_ALL=en_US.UTF-8
+export LANG=en_US.UTF-8
+export LD_LIBRARY_PATH=/usr/local/cuda/compat/:/usr/lib64/:$LD_LIBRARY_PATH
+
+ldconfig
+
+python -V
+pwd
+
+git config --global --add safe.directory /workspace1/FastDeploy
+
+python -m pip install --force-reinstall --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/
+python -m pip install --upgrade --force-reinstall -r requirements/unittest/requirements.txt
+bash tools/build_wheel.sh
+
+
+# 获取显存占用最小的gpu
+gpu_info=$(nvidia-smi --query-gpu=index,memory.used --format=csv,noheader,nounits 2>/dev/null)
+
+if [ -z "$gpu_info" ]; then
+  echo "错误: 未找到 GPU 或 nvidia-smi 不可用。"
+  exit 1
+fi
+
+min_vram=999999
+min_gpu=-1
+
+while read -r line; do
+  gpu_id=$(echo "$line" | awk -F', ' '{print $1}' | tr -d ' ')
+  vram=$(echo "$line" | awk -F', ' '{print $2}' | tr -d ' ')
+
+  if [ "$vram" -lt "$min_vram" ]; then
+    min_vram=$vram
+    min_gpu=$gpu_id
+  fi
+done <<< "$gpu_info"
+
+export CUDA_VISIBLE_DEVICES=${min_gpu}
+
+# 使用 find 命令查找 test 目录下的 .py 文件
+test_files=$(find test -type f -name "test*.py")
+
+# 遍历每个找到的测试文件
+for test_file in $test_files; do
+    python $test_file
+    
+    # 检查前一个命令的退出状态码
+    if [ $? -ne 0 ]; then
+        echo $test_file
+        exit 1
+    fi
+done
+
+echo "All tests passed."
+exit 0