/* Copyright 2021 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ #ifndef TENSORFLOW_LITE_PROFILING_MEMORY_USAGE_MONITOR_H_ #define TENSORFLOW_LITE_PROFILING_MEMORY_USAGE_MONITOR_H_ #include <memory> #include <thread> // NOLINT(build/c++11) #include "absl/synchronization/notification.h" #include "absl/time/clock.h" #include "absl/time/time.h" #include "tensorflow/lite/profiling/memory_info.h" namespace tflite { namespace profiling { namespace memory { // This class could help to tell the peak memory footprint of a running program. // It achieves this by spawning a thread to check the memory usage periodically // at a pre-defined frequency. class MemoryUsageMonitor { … }; } // namespace memory } // namespace profiling } // namespace tflite #endif // TENSORFLOW_LITE_PROFILING_MEMORY_USAGE_MONITOR_H_