# Use Python 3.X as installed in container image
build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3"
build --python_path="/usr/local/bin/python3"

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Prevent double-compilation of some TF code, ref. b/183279666 (internal)
# > TF's gen_api_init_files has a genrule to run the core TensorFlow code

config_setting(
    name = "dynamic_loaded_kernels",
    define_values = {
        "dynamic_loaded_kernels": "true",
        "framework_shared_object": "true",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "using_rocm_hipcc",
    define_values = {"using_rocm_hipcc": "true"},
)

config_setting(
    name = "override_eigen_strong_inline",
    define_values = {"override_eigen_strong_inline": "true"},

build:mkl --define=build_with_mkl=true --define=enable_mkl=true
build:mkl --define=tensorflow_mkldnn_contraction_kernel=0
build:mkl --define=build_with_openmp=true
build:mkl -c opt

# config to build OneDNN backend with a user specified threadpool.
build:mkl_threadpool --define=build_with_mkl=true --define=enable_mkl=true
build:mkl_threadpool --define=tensorflow_mkldnn_contraction_kernel=0

# Use Python 3.X as installed in container image
build --action_env PYTHON_BIN_PATH="/usr/local/bin/python3"
build --python_path="/usr/local/bin/python3"

# Build TensorFlow v2
build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1

# Use lld as the linker
build --linkopt="-fuse-ld=lld"
build --linkopt="-lm"
build --linkopt="-Wl,--undefined-version"

}

@test "Pip package doesn't depend on CUDA for static builds (i.e. Windows)" {
  bazel cquery \
    --experimental_cc_shared_library \
    --@local_config_cuda//:enable_cuda \
    --define framework_shared_object=false \
    "somepath(//tensorflow/tools/pip_package:wheel, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\

# tensorflow/python:_pywrap_parallel_device.
filegroup(
    name = "lib_headers",
    srcs = ["parallel_device_lib.h"],
)

filegroup(
    name = "lib_sources",
    srcs = ["parallel_device_lib.cc"],
)

filegroup(
    name = "device_headers",
    srcs = ["parallel_device.h"],
)

filegroup(
    name = "device_sources",
    srcs = ["parallel_device.cc"],
)

filegroup(
    name = "headers",

            compatibility with GPU delegate.

*   Extension Types

    *   Add experimental API to define new Python classes that can be handled by
        TensorFlow APIs. To create an extension type, simply define a Python
        class with `tf.experimental.ExtensionType` as its base, and use type
        annotations to specify the type for each field. E.g.: `python class

These are issues that we believe are particularly well suited for outside
contributions, often because we probably won't get to them right now. If you
decide to start on an issue, leave a comment so that other people know that
you're working on it. If you want to help out, but not alone, use the issue
comment thread to coordinate.

### Contribution guidelines and standards