I’m starting a new C++ project that requires TensorFlow. I would like to configure a way to import TensorFlow from github without having to build it manually for each environment I’m trying to use it.

I’m using Bazel to build my project and manage dependencies because TensorFlow is using it and it seems less of a struggle to import it with Bazel. On top of that I’m using bzlmod (the new way of importing modules).

I can’t find any good documentation or tutorial to import it properly. I’m quite new to C++ builds practices (coming from java / node world). I’ve tried TensorFlow Serving way of importing TensorFlow as mentioned in some posts. But nothing works for me.

As for now I have this error when trying to sync Bazel on CLion :

error loading package '@@org_tensorflow~//tensorflow/core': Unable to find package for @@[unknown repo 'local_config_cuda' requested from @@org_tensorflow~]//cuda:build_defs.bzl: The repository '@@[unknown repo 'local_config_cuda' requested from @@org_tensorflow~]' could not be resolved: No repository visible as '@local_config_cuda' from repository '@@org_tensorflow~'. and referenced by '//src/main:my_project_main'

Here is my current configuration :

registry/
modules/
org_tensorflow/
2.15.1/
MODULE.bazel
source.json
metadata.json
bazel_registry.json
src/
main/
BUILD.bazel
main.cc
.bazelrc
MODULE.bazel
WORKSPACE.bazel

I put aside the registry part that just specify the url to get the archive from as it seems to be working properly. The archive is well retrieve from github and I seem to have correct access to its files.

My .bazelrc looks like this :

# Enable Bzlmod for every Bazel command
common --enable_bzlmod --registry=https://bcr.bazel.build --registry=file://%workspace%/registry --experimental_repo_remote_exec

My MODULE.bazel looks like this

module(
    name = "my_project",
    repo_name = "my_project",
)

bazel_dep(name = "bazel_skylib", version = "1.6.1")
bazel_dep(name = "rules_java", version = "7.5.0")
bazel_dep(name = "rules_python", version = "0.31.0")
bazel_dep(name = "org_tensorflow", version = "2.15.1")

My WORKSPACE.bazel look like this :

load("@rules_python//python:repositories.bzl", "py_repositories", "python_register_toolchains")

py_repositories()

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = "3.9",
)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(
    name = "pypi",
    annotations = NUMPY_ANNOTATIONS,
    python_interpreter_target = interpreter,
    requirements_lock = "@org_tensorflow//:requirements_lock_3_9.txt",
)

load("@pypi//:requirements.bzl", "install_deps")

install_deps()

load("@org_tensorflow//tensorflow:workspace3.bzl", "tf_workspace3")

tf_workspace3()

load("@org_tensorflow//tensorflow:workspace2.bzl", "tf_workspace2")

tf_workspace2()

load("@org_tensorflow//tensorflow:workspace1.bzl", "tf_workspace1")

tf_workspace1()

load("@org_tensorflow//tensorflow:workspace0.bzl", "tf_workspace0")

tf_workspace0()

I tried to copied the way TensorFlow Serving include and configure TensorFlow and the way TensorFlow’s WORKSPACE is setup. But I have no real idea of what I’m doing.

Then I’m importing the code in my project with my src/main/BUILD.bazel like that :

cc_binary(
    name = "my_project_main",
    srcs = [
        "main.cc",
    ],
    deps = [
        "@org_tensorflow//tensorflow/core:tensorflow",
    ],
)

By the way I don’t know which target to specify as dependency. TensorFlow Serving uses @org_tensorflow//tensorflow/core:lib. Do you know which target is suited for my use case ? I will need to use basic TensorFlow classes in my C++ code such as tf.Tensor(), tf.float(), etc…

If you have any advice, solutions, thank you !