## Tools {#tools}

<!-- go.dev/issue/77177 -->

Response file (`@file`) parsing is now supported for the `compile`, `link`, `asm`, `cgo`, `cover`, and `pack` tools.
The response file contains whitespace-separated arguments with support for single-quoted and double-quoted strings, escape sequences, and backslash-newline line continuation.
The format is compatible with GCC's response file implementation to ensure interoperability with existing build systems.

# Other Tools

## uv

If uv is available, use it to manage dependencies.

## Ruff

If Ruff is available, use it to lint and format the code. Consider enabling the FastAPI rules.

## ty

If ty is available, use it to check types.

## Asyncer

When needing to run blocking code inside of async functions, or async code inside of blocking functions, suggest using Asyncer.

Prefer it over AnyIO or asyncio.

Install:

ENV BAZEL_LLVM "C:\tools\LLVM"

# These variables may be useful, but so far haven't been. Keeping for posterity.
# ENV CLANG_COMPILER_PATH "C:\tools\llvm\bin\clang.exe"
# ENV CC "C:\tools\llvm\bin\clang.exe"
# ENV BAZEL_COMPILER "C:\tools\llvm\bin\clang.exe"

ENV BAZEL_SH "C:\tools\msys64\usr\bin\bash.exe"

}

@test "API compatibility test passes, ensuring no unexpected changes to the TF API" {
    bazel test $TFCI_BAZEL_COMMON_ARGS //tensorflow/tools/api/tests:api_compatibility_test

/xla_configure.bazelrc
/bazel-*
/bazel_pip
/tools/python_bin_path.sh
/tensorflow/tools/git/gen
/pip_test
/_python_build
*.pyc
__pycache__
*.swp
.vscode/
cmake_build/
tensorflow/contrib/cmake/_build/
.idea/**
/build/
[Bb]uild/
/build_output/
/tensorflow/core/util/version_info.cc
/tensorflow/python/framework/fast_tensor_util.cpp
/tensorflow/lite/gen/**
/tensorflow/lite/tools/make/downloads/**

common:win_clang --extra_execution_platforms=//tensorflow/tools/toolchains/win:x64_windows-clang-cl
common:win_clang --host_platform=//tensorflow/tools/toolchains/win:x64_windows-clang-cl

common:windows_x86_cpu_2022 --config=win_clang_base
common:windows_x86_cpu_2022 --crosstool_top="//tensorflow/tools/toolchains/win2022/20241118:toolchain"

When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server

 generally generated from plugin sources using {{{/plugin-tools/maven-plugin-plugin/}maven-plugin-plugin}}.

* See Also

 * {{{/developers/mojo-api-specification.html}Mojo API Specification}}

 * {{{/plugin-tools/}Plugin Tools}} that provide {{{/plugin-tools/maven-plugin-plugin/}maven-plugin-plugin}} to generate the {{{./plugin.html}<<<META-INF/maven/plugin.xml>>> plugin descriptor}}

<blockquote markdown="1">

**FastAPI** wouldn't exist if not for the previous work of others.

There have been many tools created before that have helped inspire its creation.

I have been avoiding the creation of a new framework for several years. First I tried to solve all the features covered by **FastAPI** using many different frameworks, plug-ins, and tools.

But in most of the cases where we need to do something like this, we want the model just to **filter/remove** some of the data as in this example.

And in those cases, we can use classes and inheritance to take advantage of function **type annotations** to get better support in the editor and tools, and still get the FastAPI **data filtering**.