Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/tensorflow/python/profiler/profiler_v2.py: 44%

1# Copyright 2020 The TensorFlow Authors. All Rights Reserved. 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14# ============================================================================== 

15"""TensorFlow 2.x Profiler. 

16 

17The profiler has two modes: 

18- Programmatic Mode: start(logdir), stop(), and Profiler class. Profiling starts 

19 when calling start(logdir) or create a Profiler class. 

20 Profiling stops when calling stop() to save to 

21 TensorBoard logdir or destroying the Profiler class. 

22- Sampling Mode: start_server(). It will perform profiling after receiving a 

23 profiling request. 

24 

25NOTE: Only one active profiler session is allowed. Use of simultaneous 

26Programmatic Mode and Sampling Mode is undefined and will likely fail. 

27 

28NOTE: The Keras TensorBoard callback will automatically perform sampled 

29profiling. Before enabling customized profiling, set the callback flag 

30"profile_batches=[]" to disable automatic sampled profiling. 

31""" 

32 

33import collections 

34import threading 

35 

36from tensorflow.python.framework import errors 

37from tensorflow.python.platform import tf_logging as logging 

38from tensorflow.python.profiler.internal import _pywrap_profiler 

39from tensorflow.python.util.tf_export import tf_export 

40 

41_profiler = None 

42_profiler_lock = threading.Lock() 

43 

44 

45@tf_export('profiler.experimental.ProfilerOptions', v1=[]) 

46class ProfilerOptions( 

47 collections.namedtuple('ProfilerOptions', [ 

48 'host_tracer_level', 'python_tracer_level', 'device_tracer_level', 

49 'delay_ms' 

50 ])): 

51 """Options for finer control over the profiler. 

52 

53 Use `tf.profiler.experimental.ProfilerOptions` to control `tf.profiler` 

54 behavior. 

55 

56 Fields: 

57 host_tracer_level: Adjust CPU tracing level. Values are: `1` - critical info 

58 only, `2` - info, `3` - verbose. [default value is `2`] 

59 python_tracer_level: Toggle tracing of Python function calls. Values are: 

60 `1` - enabled, `0` - disabled [default value is `0`] 

61 device_tracer_level: Adjust device (TPU/GPU) tracing level. Values are: 

62 `1` - enabled, `0` - disabled [default value is `1`] 

63 delay_ms: Requests for all hosts to start profiling at a timestamp that is 

64 `delay_ms` away from the current time. `delay_ms` is in milliseconds. If 

65 zero, each host will start profiling immediately upon receiving the 

66 request. Default value is `None`, allowing the profiler guess the best 

67 value. 

68 """ 

69 

70 def __new__(cls, 

71 host_tracer_level=2, 

72 python_tracer_level=0, 

73 device_tracer_level=1, 

74 delay_ms=None): 

75 return super(ProfilerOptions, 

76 cls).__new__(cls, host_tracer_level, python_tracer_level, 

77 device_tracer_level, delay_ms) 

78 

79 

80@tf_export('profiler.experimental.start', v1=[]) 

81def start(logdir, options=None): 

82 """Start profiling TensorFlow performance. 

83 

84 Args: 

85 logdir: Profiling results log directory. 

86 options: `ProfilerOptions` namedtuple to specify miscellaneous profiler 

87 options. See example usage below. 

88 

89 Raises: 

90 AlreadyExistsError: If a profiling session is already running. 

91 

92 Example usage: 

93 ```python 

94 options = tf.profiler.experimental.ProfilerOptions(host_tracer_level = 3, 

95 python_tracer_level = 1, 

96 device_tracer_level = 1) 

97 tf.profiler.experimental.start('logdir_path', options = options) 

98 # Training code here 

99 tf.profiler.experimental.stop() 

100 ``` 

101 

102 To view the profiling results, launch TensorBoard and point it to `logdir`. 

103 Open your browser and go to `localhost:6006/#profile` to view profiling 

104 results. 

105 

106 """ 

107 global _profiler 

108 with _profiler_lock: 

109 if _profiler is not None: 

110 raise errors.AlreadyExistsError(None, None, 

111 'Another profiler is running.') 

112 _profiler = _pywrap_profiler.ProfilerSession() 

113 try: 

114 # support for namedtuple in pybind11 is missing, we change it to 

115 # dict type first. 

116 opts = dict(options._asdict()) if options is not None else {} 

117 _profiler.start(logdir, opts) 

118 except errors.AlreadyExistsError: 

119 logging.warning('Another profiler session is running which is probably ' 

120 'created by profiler server. Please avoid using profiler ' 

121 'server and profiler APIs at the same time.') 

122 raise errors.AlreadyExistsError(None, None, 

123 'Another profiler is running.') 

124 except Exception: 

125 _profiler = None 

126 raise 

127 

128 

129@tf_export('profiler.experimental.stop', v1=[]) 

130def stop(save=True): 

131 """Stops the current profiling session. 

132 

133 The profiler session will be stopped and profile results can be saved. 

134 

135 Args: 

136 save: An optional variable to save the results to TensorBoard. Default True. 

137 

138 Raises: 

139 UnavailableError: If there is no active profiling session. 

140 """ 

141 global _profiler 

142 with _profiler_lock: 

143 if _profiler is None: 

144 raise errors.UnavailableError( 

145 None, None, 

146 'Cannot export profiling results. No profiler is running.') 

147 if save: 

148 try: 

149 _profiler.export_to_tb() 

150 except Exception: 

151 _profiler = None 

152 raise 

153 _profiler = None 

154 

155 

156def warmup(): 

157 """Warm-up the profiler session. 

158 

159 The profiler session will set up profiling context, including loading CUPTI 

160 library for GPU profiling. This is used for improving the accuracy of 

161 the profiling results. 

162 

163 """ 

164 start('') 

165 stop(save=False) 

166 

167 

168@tf_export('profiler.experimental.server.start', v1=[]) 

169def start_server(port): 

170 """Start a profiler grpc server that listens to given port. 

171 

172 The profiler server will exit when the process finishes. The service is 

173 defined in tensorflow/core/profiler/profiler_service.proto. 

174 

175 Args: 

176 port: port profiler server listens to. 

177 Example usage: ```python tf.profiler.experimental.server.start(6009) # do 

178 your training here. 

179 """ 

180 _pywrap_profiler.start_server(port) 

181 

182 

183@tf_export('profiler.experimental.Profile', v1=[]) 

184class Profile(object): 

185 """Context-manager profile API. 

186 

187 Profiling will start when entering the scope, and stop and save the results to 

188 the logdir when exits the scope. Open TensorBoard profile tab to view results. 

189 

190 Example usage: 

191 ```python 

192 with tf.profiler.experimental.Profile("/path/to/logdir"): 

193 # do some work 

194 ``` 

195 """ 

196 

197 def __init__(self, logdir, options=None): 

198 """Creates a context manager object for profiler API. 

199 

200 Args: 

201 logdir: profile data will save to this directory. 

202 options: An optional `tf.profiler.experimental.ProfilerOptions` can be 

203 provided to fine tune the profiler's behavior. 

204 """ 

205 self._logdir = logdir 

206 self._options = options 

207 

208 def __enter__(self): 

209 start(self._logdir, self._options) 

210 

211 def __exit__(self, typ, value, tb): 

212 stop()