CMake Error at fc_base/gflags-src/CMakeLists.txt:73

完整日志:

shell 复制代码
CMake Warning (dev) at /root/miniconda3/share/cmake-4.1/Modules/FetchContent.cmake:1373 (message):
  The DOWNLOAD_EXTRACT_TIMESTAMP option was not given and policy CMP0135 is
  not set.  The policy's OLD behavior will be used.  When using a URL
  download, the timestamps of extracted files should preferably be that of
  the time of extraction, otherwise code that depends on the extracted
  contents might not be rebuilt if the URL changes.  The OLD behavior
  preserves the timestamps from the archive instead, but this is usually not
  what you want.  Update your project to the NEW behavior or specify the
  DOWNLOAD_EXTRACT_TIMESTAMP option with a value of true to avoid this
  robustness issue.
Call Stack (most recent call first):
  cmake/gflags.cmake:1 (FetchContent_Declare)
  cmake/openfst.cmake:2 (include)
  CMakeLists.txt:44 (include)
This warning is for project developers.  Use -Wno-dev to suppress it.

-- Populating gflags
-- Configuring done (0.3s)
-- Generating done (0.0s)
-- Build files have been written to: /root/autodl-tmp/wenet/runtime/libtorch/fc_base/gflags-subbuild
[100%] Built target gflags-populate
CMake Error at fc_base/gflags-src/CMakeLists.txt:73 (cmake_minimum_required):
  Compatibility with CMake < 3.5 has been removed from CMake.

  Update the VERSION argument <min> value.  Or, use the <min>...<max> syntax
  to tell CMake that the project requires at least <min> but has been updated
  to work with policies introduced by <max> or earlier.

  Or, add -DCMAKE_POLICY_VERSION_MINIMUM=3.5 to try configuring anyway.


-- Configuring incomplete, errors occurred!

在构建 wenet runtime 时报错,如上。

构建命令为:

shell 复制代码
mkdir build && cd build && cmake .. && cmake --build .

构建命令指定版本即可解决:

shell 复制代码
cmake -version
# cmake version 4.1.2
shell 复制代码
mkdir build && cd build && cmake -DCMAKE_POLICY_VERSION_MINIMUM=4.1.2 .. && cmake --build .

需要将原本的 build 目录删了

shell 复制代码
...
[ 91%] Linking CXX executable label_checker_main
[ 91%] Built target label_checker_main
[ 93%] Building CXX object bin/CMakeFiles/api_main.dir/api_main.cc.o
[ 94%] Linking CXX executable api_main
[ 94%] Built target api_main
[ 95%] Building CXX object bin/CMakeFiles/websocket_client_main.dir/websocket_client_main.cc.o
[ 97%] Linking CXX executable websocket_client_main
[ 97%] Built target websocket_client_main
[ 98%] Building CXX object bin/CMakeFiles/websocket_server_main.dir/websocket_server_main.cc.o
[100%] Linking CXX executable websocket_server_main
[100%] Built target websocket_server_main
相关推荐
_小雨林21 分钟前
Transformer模型、整体结构,编码器与解码器内部组成
人工智能·深度学习·transformer
放下华子我只抽RuiKe526 分钟前
AI大模型开发-实战精讲:从零构建 RFM 会员价值模型(再进阶版:模拟数据 + 动态打分 + 策略落地)
大数据·人工智能·深度学习·elasticsearch·机器学习·搜索引擎·全文检索
V搜xhliang02461 小时前
世界模型、强化学习PPOSAC
人工智能·深度学习·机器学习·语言模型·自然语言处理
liliwoliliwo2 小时前
深度学习--CNN
人工智能·深度学习
闻道且行之3 小时前
PyTorch 深度学习开发 常见疑难报错与解决方案汇总
人工智能·pytorch·深度学习
Hali_Botebie3 小时前
条件卷积是什么卷积
深度学习·神经网络·cnn
冰西瓜6003 小时前
深度学习的数学原理(十七)—— 归一化:BN与LN
人工智能·深度学习
bryant_meng3 小时前
【Reading Notes】(7.11)Favorite Articles from 2024 November
人工智能·深度学习·计算机视觉·aigc·资讯
Pyeako4 小时前
深度学习--循环神经网络原理&局限&与LSTM解决方案
人工智能·python·rnn·深度学习·lstm·循环神经网络·遗忘门
Wu_Dylan5 小时前
液态神经网络系列(七) | 事件驱动与可变步长:把“稀疏计算”做到极致
人工智能·深度学习·神经网络