import org.apache.maven.api.annotations.Experimental;
import org.apache.maven.api.annotations.Immutable;

/**
 * Represents most common tools supported by CLIng.
 *
 * @since 4.0.0
 */
@Immutable
@Experimental
public final class Tools {
    private Tools() {}

    public static final String MVN_CMD = "mvn";
    public static final String MVN_NAME = "Maven";

## 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.

## Tools {#tools}

### Go command {#go-command}

# 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:

Guillaume Nodet <******@****.***> 1729859506 +0200

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"

/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/**

fi

tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS build $TFCI_BAZEL_COMMON_ARGS --config=cuda_wheel //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_BASE_ARGS $TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG --verbose_failures

tfrun "$TFCI_FIND_BIN" ./bazel-bin/tensorflow/tools/pip_package -iname "*.whl" -exec cp {} $TFCI_OUTPUT_DIR \;
tfrun mkdir -p ./dist
tfrun cp $TFCI_OUTPUT_DIR/*.whl ./dist

        assertBuildFileExists(result, PROJECT_NAME, "build-tools-exported/checkstyle.xml");
        assertBuildFileExists(result, PROJECT_NAME, "build-tools-exported/checkstyle_suppressions.xml");

        result = getGradleRunner().withArguments("buildResources", "-s", "-i").build();
        assertTaskUpToDate(result, ":buildResources");
        assertBuildFileExists(result, PROJECT_NAME, "build-tools-exported/checkstyle.xml");

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