across multiple computational devices, including multicore CPUs, general purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on...

TFE_Context* CreateContext(const std::string& serialized_server_def,
                           bool isolate_session_state,
                           int64_t init_timeout_in_ms);

tensorflow::ServerDef ReplaceTaskInServerDef(
    const tensorflow::ServerDef& server_def, int task_index);

void ReplaceTaskInServerDef(tensorflow::ServerDef* server_def, int task_index,

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

setup_file() {
    cd /tf/tensorflow
    bazel version  # Start the bazel server
    # Without this, git errors if /tf/tensorflow directory owner is different
    git config --global --add safe.directory /tf/tensorflow
    # Note that you could generate a list of all the affected targets with e.g.: