read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for
internal communication only. It is not built for use in untrusted environments
or networks.

For performance reasons, the default TensorFlow server does not include any

# limitations under the License.
# ==============================================================================

setup_file() {
    bazel version  # Start the bazel server

    # Fixes "fatal: detected dubious ownership in repository" for Docker.
    git config --system --add safe.directory '*'
    git config --system protocol.file.allow always

        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
    ],

            "python_api.cc",
            "python_api.h",
            "*test*",
        ],
    ) + [
        "//tensorflow/cc:srcs_no_runtime",
        "//tensorflow/core/distributed_runtime:server_lib.h",
        "@local_tsl//tsl/platform:ctstring",
        "@local_xla//xla/tsl/c:srcs",
    ],
    visibility = ["//visibility:public"],
)

cc_library(
    name = "pywrap_required_hdrs",

        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
        "@com_google_absl//absl/strings",
    ],
)

tf_cuda_cc_test(
    name = "c_api_remote_test",
    size = "medium",
    srcs = [
        "c_api_remote_test.cc",
    ],
    # TODO(b/136478427): Figure out how to correctly shut the server down
    args = ["--heap_check="],
    tags = [
        "no_windows",

        "//tensorflow/core/data/service:grpc_util",
        "//tensorflow/core/data/service:py_utils",
        "//tensorflow/core/data/service:server_lib",
        "//tensorflow/core/debug",
        "//tensorflow/core/distributed_runtime:server_lib",
        "//tensorflow/core/framework:full_type_util",
        "//tensorflow/core/function/runtime_client:runtime_client_cc",

tblib==2.0.0 \
    --hash=sha256:9100bfa016b047d5b980d66e7efed952fbd20bd85b56110aaf473cb97d18709a \
    --hash=sha256:a6df30f272c08bf8be66e0775fad862005d950a6b8449b94f7c788731d70ecd7
    # via -r requirements.in
tensorboard-data-server==0.7.2 \
    --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \
    --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \

# limitations under the License.
# ==============================================================================
setup_file() {
    cd /tf/tensorflow
    bazel version  # Start the bazel server
}

# Do a bazel query specifically for the licenses checker. It searches for
# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then

        based server to register tf.function methods and a GRPC client to invoke
        remote registered methods. RPC APIs are intended for multi-client setups
        i.e. server and clients are started in separate binaries independently.

    *   Example usage to create server: ```python server =
        tf.distribute.experimental.rpc.Server.create("grpc", "127.0.0.1:1234")

tblib==2.0.0 \
    --hash=sha256:9100bfa016b047d5b980d66e7efed952fbd20bd85b56110aaf473cb97d18709a \
    --hash=sha256:a6df30f272c08bf8be66e0775fad862005d950a6b8449b94f7c788731d70ecd7
    # via -r requirements.in
tensorboard-data-server==0.7.2 \
    --hash=sha256:7e0610d205889588983836ec05dc098e80f97b7e7bbff7e994ebb78f578d0ddb \
    --hash=sha256:9fe5d24221b29625dbc7328b0436ca7fc1c23de4acf4d272f1180856e32f9f60 \