"//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

tf_cc_test(
    name = "parallel_device_remote_test",
    srcs = ["parallel_device_remote_test.cc"],
    # TODO(b/136478427): Enable global heap checking when servers shut down
    # cleanly.
    args = ["--heap_check="],
    deps = [
        ":parallel_device",

  // Set up distributed execution environment on local and remote tasks.
  // When `reset_context` is true, initialize new cluster context state based
  // on cluster configurations provided in `server_def`; otherwise, update
  // existing context state with the provided `server_def`. Contexts created
  // on remote tasks will be considered stale and garbage collected after
  // `keep_alive_secs` of inactivity.

    int64_t init_timeout_in_ms, int retries, TF_Status* status,
    bool clear_existing_contexts);

// Checks whether a remote worker is alive or not. This will return true even if
// the context doesn't exist on the remote worker.
TF_CAPI_EXPORT extern bool TFE_ContextCheckAlive(TFE_Context* ctx,
                                                 const char* worker_name,

        "//tensorflow/core/platform:env",
        "@com_google_absl//absl/strings",
    ],
)

tf_cuda_library(
    name = "c_api_remote_test_util",
    testonly = 1,
    srcs = ["c_api_remote_test_util.cc"],
    hdrs = ["c_api_remote_test_util.h"],
    visibility = ["//visibility:private"],
    deps = [
        ":c_api",
        ":c_api_internal",
        ":c_api_test_util",

        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",
        "//learning/serving/experimental/remote_predict/...",
        "//perftools/accelerators/xprof/convert/...",
        "//perftools/accelerators/xprof/integration_tests/...",
        "//smartass/brain/configure/...",
        "//tensorflow/...",

        @tf.function(input_signature=[ tf.TensorSpec([], tf.int32),
        tf.TensorSpec([], dtypes.int32) ]) def _remote_multiply(a, b): return
        tf.math.multiply(a, b)

        server.register("multiply", _remote_multiply) ```

    *   Example usage to create client: `python client =
        tf.distribute.experimental.rpc.Client.create("grpc", address) a =

build:resultstore --bes_timeout=600s

# Flag to enable remote config
common --experimental_repo_remote_exec

# Make Bazel not try to probe the host system for a C++ toolchain.
build:rbe_base --config=resultstore
build:rbe_base --repo_env=BAZEL_DO_NOT_DETECT_CPP_TOOLCHAIN=1
build:rbe_base --define=EXECUTOR=remote
build:rbe_base --jobs=800
build:rbe_base --remote_executor=grpcs://remotebuildexecution.googleapis.com

          # replace just the digest portion in it
          sed -i"" "/\"$1\"/ s/sha256:[[:alnum:]]*/$digest/g" tensorflow/tools/toolchains/remote_config/configs.bzl
          echo "success."
        }
        # See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/toolchains/remote_config/configs.bzl
        # This is a mapping of name_container_map keys under sigbuild_tf_configs

      tensorflow::ContextFromInterface(tensorflow::unwrap(ctx));
  auto collective_executor_handle = context->GetCollectiveExecutorHandle();
  tensorflow::Notification done;
  collective_executor_handle->get()->remote_access()->CheckPeerHealth(
      task, timeout_in_ms, [&done, status](const Status& s) {
        status->status = s;
        done.Notify();
      });
  done.WaitForNotification();
}