mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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59a4ab343f
* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme * add cpp benchmark * add cpu and gpu mem * public part split * add runtime mode * fixed bugs * add cpu_thread_nums * deal with comments * deal with comments * deal with comments * rm useless code * add FASTDEPLOY_DECL * add FASTDEPLOY_DECL * fixed for windows * mv rss to pss * mv rss to pss * Update utils.cc * use thread to collect mem * Add ResourceUsageMonitor * rm useless code * fixed bug * fixed typo * update ResourceUsageMonitor * fixed bug * fixed bug * add note for ResourceUsageMonitor * deal with comments --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
85 lines
2.6 KiB
C++
Executable File
85 lines
2.6 KiB
C++
Executable File
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <memory>
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#include <thread> // NOLINT
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#include "fastdeploy/utils/utils.h"
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namespace fastdeploy {
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namespace benchmark {
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/*! @brief ResourceUsageMonitor object used when to collect memory info.
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*/
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class FASTDEPLOY_DECL ResourceUsageMonitor {
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public:
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/** \brief Set sampling_interval_ms and gpu_id for ResourceUsageMonitor.
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*
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* \param[in] sampling_interval_ms How often to collect memory info(ms).
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* \param[in] gpu_id Device(gpu) id, default 0.
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*/
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explicit ResourceUsageMonitor(int sampling_interval_ms, int gpu_id = 0);
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~ResourceUsageMonitor() { StopInternal(); }
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/// Start memory info collect
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void Start();
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/// Stop memory info collect
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void Stop();
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/// Get maximum cpu memory usage
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float GetMaxCpuMem() const {
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if (!is_supported_ || check_memory_thd_ == nullptr) {
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return -1.0f;
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}
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return max_cpu_mem_;
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}
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/// Get maximum gpu memory usage
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float GetMaxGpuMem() const {
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if (!is_supported_ || check_memory_thd_ == nullptr) {
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return -1.0f;
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}
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return max_gpu_mem_;
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}
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/// Get maximum gpu util
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float GetMaxGpuUtil() const {
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if (!is_supported_ || check_memory_thd_ == nullptr) {
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return -1.0f;
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}
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return max_gpu_util_;
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}
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ResourceUsageMonitor(ResourceUsageMonitor&) = delete;
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ResourceUsageMonitor& operator=(const ResourceUsageMonitor&) = delete;
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ResourceUsageMonitor(ResourceUsageMonitor&&) = delete;
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ResourceUsageMonitor& operator=(const ResourceUsageMonitor&&) = delete;
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private:
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void StopInternal();
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// Get current cpu memory info
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std::string GetCurrentCpuMemoryInfo();
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// Get current gpu memory info
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std::string GetCurrentGpuMemoryInfo(int device_id);
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bool is_supported_ = false;
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bool stop_signal_ = false;
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const int sampling_interval_;
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float max_cpu_mem_ = 0.0f;
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float max_gpu_mem_ = 0.0f;
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float max_gpu_util_ = 0.0f;
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const int gpu_id_ = 0;
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std::unique_ptr<std::thread> check_memory_thd_ = nullptr;
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};
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} // namespace benchmark
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} // namespace fastdeploy
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