add first example

python/examples/pipeline/PaddleClas/DarkNet53/benchmark.sh

@@ -15,7 +15,7 @@ for thread_num in 1 2 4 8 12 16
 do
   for batch_size in 1
   do
-    echo "----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
+    echo "#----${modelname} thread num: ${thread_num} batch size: ${batch_size} mode:http ----" >>profile_log_$modelname
     # Start one web service, If you start the service yourself, you can ignore it here.
     #python3 web_service.py >web.log 2>&1 &
     #sleep 3
@@ -23,15 +23,15 @@ do
     # --id is the serial number of the GPU card, Must be the same as the gpu id used by the server.
     nvidia-smi --id=3 --query-gpu=memory.used --format=csv -lms 1000 > gpu_use.log 2>&1 &
     nvidia-smi --id=3 --query-gpu=utilization.gpu --format=csv -lms 1000 > gpu_utilization.log 2>&1 &
-    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTILIZATION:', cpu_utilization)\n" > cpu_utilization.py
+    echo "import psutil\ncpu_utilization=psutil.cpu_percent(1,False)\nprint('CPU_UTIL:', cpu_utilization)\n" > cpu_utilization.py
     # Start http client
     python3 benchmark.py run http $thread_num $batch_size > profile 2>&1
 
     # Collect CPU metrics, Filter data that is zero momentarily, Record the maximum value of GPU memory and the average value of GPU utilization
     python3 cpu_utilization.py >> profile_log_$modelname
     grep -av '^0 %' gpu_utilization.log > gpu_utilization.log.tmp
-    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "MAX_GPU_MEMORY:", max}' gpu_use.log >> profile_log_$modelname
-    awk -F' ' '{sum+=$1} END {print "GPU_UTILIZATION:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
+    awk 'BEGIN {max = 0} {if(NR>1){if ($modelname > max) max=$modelname}} END {print "GPU_MEM:", max}' gpu_use.log >> profile_log_$modelname
+    awk -F' ' '{sum+=$1} END {print "GPU_UTIL:", sum/NR, sum, NR }' gpu_utilization.log.tmp >> profile_log_$modelname
 
     # Show profiles
     python3 ../../../util/show_profile.py profile $thread_num >> profile_log_$modelname

python/paddle_serving_client/utils/__init__.py

@@ -41,9 +41,9 @@ def show_latency(latency_list):
     info = "latency:\n"
     info += "mean: {}ms\n".format(np.mean(latency_array))
     info += "median: {}ms\n".format(np.median(latency_array))
-    info += "80 percent: {}ms\n".format(np.percentile(latency_array, 80))
-    info += "90 percent: {}ms\n".format(np.percentile(latency_array, 90))
-    info += "99 percent: {}ms\n".format(np.percentile(latency_array, 99))
+    info += "80_percent: {}ms\n".format(np.percentile(latency_array, 80))
+    info += "90_percent: {}ms\n".format(np.percentile(latency_array, 90))
+    info += "99_percent: {}ms\n".format(np.percentile(latency_array, 99))
     sys.stderr.write(info)
 
 

python/paddle_serving_server/benchmark_utils.py

@@ -23,8 +23,8 @@ import paddle.inference as paddle_infer
 from pathlib import Path
 
 CUR_DIR = os.path.dirname(os.path.abspath(__file__))
-LOG_PATH_ROOT = f"{CUR_DIR}/../../tools/output"
-
+#LOG_PATH_ROOT = f"{CUR_DIR}/../../tools/output"
+LOG_PATH_ROOT = f"."
 
 class PaddleInferBenchmark(object):
     def __init__(self,
@@ -73,7 +73,6 @@ class PaddleInferBenchmark(object):
 
         # perf info
         self.perf_info = perf_info
-
         try:
             # required value
             self.model_name = model_info['model_name']
@@ -93,8 +92,8 @@ class PaddleInferBenchmark(object):
         self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
         self.total_time_s = perf_info.get('total_time_s', 0)
 
-        self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
-        self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
+        self.inference_time_s_90 = perf_info.get("inference_time_s_90", 0)
+        self.inference_time_s_99 = perf_info.get("inference_time_s_99", 0)
         self.succ_rate = perf_info.get("succ_rate", "")
         self.qps = perf_info.get("qps", "")
 
@@ -103,15 +102,17 @@ class PaddleInferBenchmark(object):
 
         # mem info
         if isinstance(resource_info, dict):
-            self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
-            self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
-            self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
-            self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
+            self.cpu_rss_mb = int("-1" if 'cpu_rss_mb' not in resource_info or resource_info.get('cpu_rss_mb').strip()=="" else resource_info.get('cpu_rss_mb', 0))
+            self.cpu_vms_mb = int("-1" if 'cpu_vms_mb' not in resource_info or resource_info.get('cpu_vms_mb').strip()=="" else resource_info.get('cpu_vms_mb', 0))
+            self.cpu_shared_mb = int("-1" if 'cpu_shared_mb' not in resource_info or resource_info.get('cpu_shared_mb').strip()=="" else resource_info.get('cpu_shared_mb', 0))
+            self.cpu_dirty_mb = int("-1" if 'cpu_dirty_mb' not in resource_info or resource_info.get('cpu_dirty_mb').strip()=="" else resource_info.get('cpu_dirty_mb', 0))
             self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
 
-            self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
-            self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
-            self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
+            self.gpu_rss_mb = int("-1" if 'gpu_rss_mb' not in resource_info or resource_info.get('gpu_rss_mb').strip()=="" else resource_info.get('gpu_rss_mb', 0))
+            #self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
+            self.gpu_util = resource_info.get('gpu_util', 0)
+            #self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
+            self.gpu_mem_util =  resource_info.get('gpu_mem_util', 0)
         else:
             self.cpu_rss_mb = 0
             self.cpu_vms_mb = 0
@@ -170,6 +171,7 @@ class PaddleInferBenchmark(object):
                 'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
                 )
         elif isinstance(config, dict):
+            config_status = {}
             config_status['runtime_device'] = config.get('runtime_device', "")
             config_status['ir_optim'] = config.get('ir_optim', "")
             config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
@@ -238,10 +240,10 @@ class PaddleInferBenchmark(object):
         self.logger.info(
             f"{identifier} total time spent(s): {self.total_time_s}")
         self.logger.info(
-            f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, inference_time(ms): {round(self.inference_time_s*1000, 1)}, postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
+            f"{identifier} preprocess_time(ms): {self.preprocess_time_s}, inference_time(ms): {self.inference_time_s}, postprocess_time(ms): {self.postprocess_time_s}"
         )
         if self.inference_time_s_90:
-            self.looger.info(
+            self.logger.info(
                 f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
             )
         if self.qps:

python/paddle_serving_server/parse_profile.py

@@ -17,7 +17,7 @@ import os
 import yaml
 import argparse
 
-import .benchmark_utils
+from .benchmark_utils import PaddleInferBenchmark
 """
 {'CPU_UTILIZATION': 0.8, 'MAX_GPU_MEMORY': 0, 'GPU_UTILIZATION': '0 %', 'DAG': {'50': 670.256, '60': 670.256, '70': 670.765, '80': 671.23, '90': 687.546, '95': 687.546, '99': 687.546, 'avg': 670.755625, 'qps': 0.8, 'query_count': 8, 'succ': 1.0}, 'demo': {'midp': 669.484375, 'postp': 0.184875, 'prep': 1.001875}}
 """
@@ -37,6 +37,39 @@ class LogHandler(object):
     def append(self, new_str):
         self.fstr += new_str + "\n"
 
+def handle_benchmark(benchmark_config, benchmark_raw, indentifier):
+    model_info = {
+        'model_name': benchmark_config["model_name"],
+        'precision': benchmark_config["precision"]
+    }
+    data_info = {
+        'batch_size': benchmark_config["batch_size"],
+        'shape': benchmark_config["input_shape"],
+        'data_num': benchmark_config["num_of_samples"]
+    }
+    perf_info = {
+        'preprocess_time_s': "",
+        'inference_time_s': float(benchmark_raw["median"][0:-2])/1000, # *** ms
+        'postprocess_time_s': "",
+        'total_time_s': "",
+        'inference_time_s_90': float(benchmark_raw["90_percent"][0:-2])/1000,  # *** ms
+        'inference_time_s_99': float(benchmark_raw["99_percent"][0:-2])/1000,  # *** ms
+        'qps': benchmark_raw["AVG_QPS"]
+    }
+    resource_info = {
+        'cpu_rss_mb': "",
+        'cpu_vms_mb': "",
+        'cpu_shared_mb': "",
+        'cpu_dirty_mb': "",
+        'cpu_util': benchmark_raw["CPU_UTIL"],
+        'gpu_rss_mb': "",
+        'gpu_util': benchmark_raw["GPU_UTIL"],
+        'gpu_mem_util': benchmark_raw["GPU_MEM"]
+    }
+
+    server_log = PaddleInferBenchmark(
+        benchmark_config, model_info, data_info, perf_info, resource_info)
+    server_log(indentifier)
 
 def parse_args():  # pylint: disable=doc-string-missing
     parser = argparse.ArgumentParser("serve")
@@ -65,41 +98,18 @@ if __name__ == "__main__":
     f = open(benchmark_cfg_filename, 'r')
     benchmark_config = yaml.load(f)
     f.close()
-    benchmark_raw_filename = args.benchmark_log
-    f = open(benchmark_raw_filename, 'r')
-    benchmark_raw = yaml.load(f)
-    f.close()
-
-    model_info = {
-        'model_name': benchmark_config["model_name"],
-        'precision': benchmark_config["precision"]
-    }
-    data_info = {
-        'batch_size': benchmark_config["batch_size"],
-        'shape': benchmark_config["input_shape"],
-        'data_num': benchmark_config["num_of_samples"]
-    }
-    perf_info = {
-        'preprocess_time_s': "",
-        'inference_time_s': benchmark_raw["DAG"]["avg"],
-        'postprocess_time_s': "",
-        'total_time_s': "",
-        'inference_time_s_90': benchmark_raw["DAG"]["90"],
-        'inference_time_s_99': benchmark_raw["DAG"]["99"],
-        'succ_rate': benchmark_raw["DAG"]["succ"],
-        'qps': benchmark_raw["DAG"]["qps"]
-    }
-    resource_info = {
-        'cpu_rss_mb': "",
-        'cpu_vms_mb': "",
-        'cpu_shared_mb': "",
-        'cpu_dirty_mb': "",
-        'cpu_util': benchmark_raw["CPU_MEM"],
-        'gpu_rss_mb': "",
-        'gpu_util': benchmark_raw["GPU_UTIL"],
-        'gpu_mem_util': benchmark_raw["GPU_MEM"]
-    }
-
-    server_log = benchmark_utils.PaddleInferBenchmark(
-        benchmark_config, model_info, data_info, perf_info, resource_info)
-    server_log('Serving')
+    benchmark_log_filename = args.benchmark_log
+    f = open(benchmark_log_filename, 'r')
+    lines = f.readlines()
+    line_no = 0
+    while line_no < len(lines):
+        if len(lines[line_no]) > 5 and lines[line_no].startswith("#---"):
+            iden = lines[line_no][5: -5]
+            line_no += 1
+            sub_log = lines[line_no: line_no + 13]
+            sub_dict = yaml.safe_load("".join(sub_log))
+            handle_benchmark(benchmark_config, sub_dict, iden)
+            line_no += 13
+        else:
+            line_no += 1
+