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"Hi **@allogic** ,\r\n- Sorry for the delay, Here i am seeing compatibility mismatch. Could you go through this [documentation](https://www.tensorflow.org/install/source_windows) once? Please let us know if issue still persists.\r\n\r\nThank you!",
"Look, I was going through the documentation for a whole week and still could not use tensorflow as a simple static library.\r\nHere is a simple step by step guide to reproduce the behavior I'm experiencing.\r\n\r\nNOTE: `session_header.lib` is not being built by default. It is only registered in bazel tests, but is not linked into the final binary!\r\n```sh\r\ngit clone --depth 1 --branch v2.16.1 https://github.com/tensorflow/tensorflow\r\n# ensure \"session_header.lib\" is being built in //tensorflow/cc/BUILD\r\npython configure.py\r\n\r\nset BAZEL_SH=\"C:\\msys64\\usr\\bin\\bash.exe\" # Is required for some odd reason...\r\n\r\nbazel clean --expunge\r\n```\r\nIt seems clang is the preferred compiler starting with tensorflow 2.16.1. But when building with clang using the following command, clang produces a linker error which I have never encountered. It seems to be a problem on the LLVM site.\r\n\r\nNOTE: I've not tested newer versions of clang, only the one described in the documentation!\r\n```sh\r\nbazel build --config=win_clang //tensorflow:tensorflow.lib\r\n# time_rep_timespec.obj error LNK2019: unresolved external symbol _Thrd_sleep_for referenced in function \"void __cdecl std::this_thread::sleep_for<__int64,struct std::ratio<1,1000000000> >(class std::chrono::duration<__int64,struct std::ratio<1,1000000000> > const &)\" (??$sleep_for@_JU?$ratio@$00$0DLJKMKAA@@std@@@this_thread@std@@YAXAEBV?$duration@_JU?$ratio@$00$0DLJKMKAA@@std@@@chrono@1@@Z)\r\n```\r\nRegardless, when I build with MSVC on the other hand, it works as expected. It generates all the static libraries that I can link against without error.\r\n\r\nThe monolithic option is described in the `.bazelrc` file and is used to create a mostly static build. It also states that it will DISABLE modular op registration which is the problem I am facing currently. All tho it \"states\" it will be disabled, it does not.\r\nModular op registration is still enabled and my guess is that all the operators get optimized away during the build to safe binary size.\r\n```sh\r\nbazel build --config=monolithic //tensorflow:tensorflow.lib\r\n```\r\nLast but not least I'm generating the headers.\r\n```sh\r\nbazel build //tensorflow:install_headers\r\n```\r\nHere are all the software versions that I use. They are strictly limited to the versions described in the documentation!\r\n```sh\r\n# Tensorflow: 2.16.1\r\n# Python: 3.13.0a6\r\n# LLVM: 17.0.6\r\n# MSVC: 19.39.33520\r\n# Bazel: 6.5.0\r\n```\r\nI'm forced to leave it like this as I have no proper experience with Bazel and Tensorflow as a whole. But I would be happy if someone could explain to me what I'm missing or doing wrong. It can't be that big of a problem, since the modular op registration has to be disabled somewhere..."
] | 2024-05-17T21:32:38 | 2024-05-21T07:34:14 | null | NONE | null | null | null | ### Issue type
Build/Install
### Have you reproduced the bug with TensorFlow Nightly?
No
### Source
source
### TensorFlow version
tf.2.16.1
### Custom code
No
### OS platform and distribution
Windows 11 x64
### Mobile device
_No response_
### Python version
3.13
### Bazel version
6.5.0
### GCC/compiler version
MSVC 19.39.33520 for x64
### CUDA/cuDNN version
_No response_
### GPU model and memory
_No response_
### Current behavior?
I'm trying to build tensorflow as a static library. When I create the root scope it tells me that I didn't register any operators or kernels. Specifically the "NoOp" is required.
I've read that with the tool "tensorflow/python/tools/print_selective_registration_header" I can generate the missing header file "ops_to_register.h" which registers all operators and kernels when I build the C++API again with the flag --cxxopt=”-DSELECTIVE_REGISTRATION”.
But I need a graph definition in order to produce the header file. I don't have a graph since I want to build my model in C++ instead. What can I do to register all operators and kernels. (I don't care about file size!)
### Standalone code to reproduce the issue
```cpp
std::printf(OpRegistry::Global()->DebugString(true).data()); // No output at all...
Scope root = Scope::NewRootScope(); // Crash because "NoOp" is required!
```
### Relevant log output
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} | Remove unnecessary uses of `gpu_any` backend
If a test is intended for the v100 backend, that will run in OSS, so no need for the extra `gpu_any` backend.
The usage in hlo_opt should've been removed already in a previous change.
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