├── 01_simple_example.py ├── 02_example_with_placeholders_and_for_loop.py ├── 03_merged_timeline_example.py ├── LICENSE ├── README.rst └── profiles ├── CPU ├── timeline_01.json ├── timeline_02_step_0.json ├── timeline_02_step_1.json ├── timeline_02_step_2.json └── timeline_03_merged_5_runs.json └── GPU ├── timeline_01.json ├── timeline_02_step_0.json ├── timeline_02_step_1.json ├── timeline_02_step_2.json └── timeline_03_merged_5_runs.json /01_simple_example.py: -------------------------------------------------------------------------------- 1 | import tensorflow as tf 2 | from tensorflow.python.client import timeline 3 | 4 | a = tf.random_normal([2000, 5000]) 5 | b = tf.random_normal([5000, 1000]) 6 | res = tf.matmul(a, b) 7 | 8 | with tf.Session() as sess: 9 | # add additional options to trace the session execution 10 | options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) 11 | run_metadata = tf.RunMetadata() 12 | sess.run(res, options=options, run_metadata=run_metadata) 13 | 14 | # Create the Timeline object, and write it to a json file 15 | fetched_timeline = timeline.Timeline(run_metadata.step_stats) 16 | chrome_trace = fetched_timeline.generate_chrome_trace_format() 17 | with open('timeline_01.json', 'w') as f: 18 | f.write(chrome_trace) 19 | -------------------------------------------------------------------------------- /02_example_with_placeholders_and_for_loop.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tempfile 3 | 4 | import tensorflow as tf 5 | from tensorflow.contrib.layers import fully_connected as fc 6 | from tensorflow.examples.tutorials.mnist import input_data 7 | from tensorflow.python.client import timeline 8 | 9 | batch_size = 100 10 | 11 | inputs = tf.placeholder(tf.float32, [batch_size, 784]) 12 | targets = tf.placeholder(tf.float32, [batch_size, 10]) 13 | 14 | with tf.variable_scope("layer_1"): 15 | fc_1_out = fc(inputs, num_outputs=500, activation_fn=tf.nn.sigmoid) 16 | with tf.variable_scope("layer_2"): 17 | fc_2_out = fc(fc_1_out, num_outputs=784, activation_fn=tf.nn.sigmoid) 18 | with tf.variable_scope("layer_3"): 19 | logits = fc(fc_2_out, num_outputs=10) 20 | 21 | loss = tf.reduce_mean( 22 | tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets)) 23 | train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss) 24 | 25 | if __name__ == '__main__': 26 | mnist_save_dir = os.path.join(tempfile.gettempdir(), 'MNIST_data') 27 | mnist = input_data.read_data_sets(mnist_save_dir, one_hot=True) 28 | 29 | config = tf.ConfigProto() 30 | config.gpu_options.allow_growth = True 31 | with tf.Session(config=config) as sess: 32 | sess.run(tf.global_variables_initializer()) 33 | 34 | options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) 35 | run_metadata = tf.RunMetadata() 36 | for i in range(3): 37 | batch_input, batch_target = mnist.train.next_batch(batch_size) 38 | feed_dict = {inputs: batch_input, 39 | targets: batch_target} 40 | 41 | sess.run(train_op, 42 | feed_dict=feed_dict, 43 | options=options, 44 | run_metadata=run_metadata) 45 | 46 | fetched_timeline = timeline.Timeline(run_metadata.step_stats) 47 | chrome_trace = fetched_timeline.generate_chrome_trace_format() 48 | with open('timeline_02_step_%d.json' % i, 'w') as f: 49 | f.write(chrome_trace) 50 | -------------------------------------------------------------------------------- /03_merged_timeline_example.py: -------------------------------------------------------------------------------- 1 | import os 2 | import tempfile 3 | import json 4 | 5 | import tensorflow as tf 6 | from tensorflow.contrib.layers import fully_connected as fc 7 | from tensorflow.examples.tutorials.mnist import input_data 8 | from tensorflow.python.client import timeline 9 | 10 | 11 | class TimeLiner: 12 | _timeline_dict = None 13 | 14 | def update_timeline(self, chrome_trace): 15 | # convert crome trace to python dict 16 | chrome_trace_dict = json.loads(chrome_trace) 17 | # for first run store full trace 18 | if self._timeline_dict is None: 19 | self._timeline_dict = chrome_trace_dict 20 | # for other - update only time consumption, not definitions 21 | else: 22 | for event in chrome_trace_dict['traceEvents']: 23 | # events time consumption started with 'ts' prefix 24 | if 'ts' in event: 25 | self._timeline_dict['traceEvents'].append(event) 26 | 27 | def save(self, f_name): 28 | with open(f_name, 'w') as f: 29 | json.dump(self._timeline_dict, f) 30 | 31 | 32 | batch_size = 100 33 | 34 | inputs = tf.placeholder(tf.float32, [batch_size, 784]) 35 | targets = tf.placeholder(tf.float32, [batch_size, 10]) 36 | 37 | with tf.variable_scope("layer_1"): 38 | fc_1_out = fc(inputs, num_outputs=500, activation_fn=tf.nn.sigmoid) 39 | with tf.variable_scope("layer_2"): 40 | fc_2_out = fc(fc_1_out, num_outputs=784, activation_fn=tf.nn.sigmoid) 41 | with tf.variable_scope("layer_3"): 42 | logits = fc(fc_2_out, num_outputs=10) 43 | 44 | loss = tf.reduce_mean( 45 | tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=targets)) 46 | train_op = tf.train.GradientDescentOptimizer(0.01).minimize(loss) 47 | 48 | if __name__ == '__main__': 49 | mnist_save_dir = os.path.join(tempfile.gettempdir(), 'MNIST_data') 50 | mnist = input_data.read_data_sets(mnist_save_dir, one_hot=True) 51 | 52 | config = tf.ConfigProto() 53 | config.gpu_options.allow_growth = True 54 | with tf.Session(config=config) as sess: 55 | sess.run(tf.global_variables_initializer()) 56 | 57 | options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) 58 | run_metadata = tf.RunMetadata() 59 | many_runs_timeline = TimeLiner() 60 | runs = 5 61 | for i in range(runs): 62 | batch_input, batch_target = mnist.train.next_batch(batch_size) 63 | feed_dict = {inputs: batch_input, 64 | targets: batch_target} 65 | 66 | sess.run(train_op, 67 | feed_dict=feed_dict, 68 | options=options, 69 | run_metadata=run_metadata) 70 | 71 | fetched_timeline = timeline.Timeline(run_metadata.step_stats) 72 | chrome_trace = fetched_timeline.generate_chrome_trace_format() 73 | many_runs_timeline.update_timeline(chrome_trace) 74 | many_runs_timeline.save('timeline_03_merged_%d_runs.json' % runs) 75 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Illarion 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.rst: -------------------------------------------------------------------------------- 1 | This repo contains scripts with example usage of tensorflow profiler and already generated timelines for this `blog post `__. 2 | -------------------------------------------------------------------------------- /profiles/CPU/timeline_01.json: -------------------------------------------------------------------------------- 1 | { 2 | "traceEvents": [ 3 | { 4 | "ph": "M", 5 | "args": { 6 | "name": "Allocators" 7 | }, 8 | "name": "process_name", 9 | "pid": 0 10 | }, 11 | { 12 | "ph": "M", 13 | "args": { 14 | "name": "/job:localhost/replica:0/task:0/cpu:0 Compute" 15 | }, 16 | "name": "process_name", 17 | "pid": 1 18 | }, 19 | { 20 | "ph": "M", 21 | "args": { 22 | "name": "/job:localhost/replica:0/task:0/cpu:0 Tensors" 23 | }, 24 | "name": "process_name", 25 | "pid": 2 26 | }, 27 | { 28 | "ts": 1490279990817672, 29 | "cat": "Op", 30 | "tid": 0, 31 | "name": "NoOp", 32 | "args": { 33 | "name": "_SOURCE", 34 | "op": "NoOp" 35 | }, 36 | "ph": "X", 37 | "dur": 18, 38 | "pid": 1 39 | }, 40 | { 41 | "ts": 1490279990817714, 42 | "cat": "Op", 43 | "tid": 0, 44 | "name": "Const", 45 | "args": { 46 | "name": "random_normal/shape", 47 | "op": "Const" 48 | }, 49 | "ph": "X", 50 | "dur": 20, 51 | "pid": 1 52 | }, 53 | { 54 | "ts": 1490279990817750, 55 | "cat": "Op", 56 | "tid": 0, 57 | "name": "Const", 58 | "args": { 59 | "name": "random_normal/mean", 60 | "op": "Const" 61 | }, 62 | "ph": "X", 63 | "dur": 6, 64 | "pid": 1 65 | }, 66 | { 67 | "ts": 1490279990817758, 68 | "cat": "Op", 69 | "tid": 0, 70 | "name": "Const", 71 | "args": { 72 | "name": "random_normal/stddev", 73 | "op": "Const" 74 | }, 75 | "ph": "X", 76 | "dur": 4, 77 | "pid": 1 78 | }, 79 | { 80 | "ts": 1490279990817765, 81 | "cat": "Op", 82 | "tid": 0, 83 | "name": "Const", 84 | "args": { 85 | "name": "random_normal_1/shape", 86 | "op": "Const" 87 | }, 88 | "ph": "X", 89 | "dur": 5, 90 | "pid": 1 91 | }, 92 | { 93 | "ts": 1490279990817772, 94 | "cat": "Op", 95 | "tid": 0, 96 | "name": "RandomStandardNormal", 97 | "args": { 98 | "input0": "random_normal_1/shape", 99 | "name": "random_normal_1/RandomStandardNormal", 100 | "op": "RandomStandardNormal" 101 | }, 102 | "ph": "X", 103 | "dur": 147001, 104 | "pid": 1 105 | }, 106 | { 107 | "ts": 1490279990964790, 108 | "cat": "Op", 109 | "tid": 0, 110 | "name": "Mul", 111 | "args": { 112 | "input0": "random_normal_1/RandomStandardNormal", 113 | "input1": "random_normal_1/stddev", 114 | "name": "random_normal_1/mul", 115 | "op": "Mul" 116 | }, 117 | "ph": "X", 118 | "dur": 52467, 119 | "pid": 1 120 | }, 121 | { 122 | "ts": 1490279991017294, 123 | "cat": "Op", 124 | "tid": 0, 125 | "name": "Add", 126 | "args": { 127 | "input0": "random_normal_1/mul", 128 | "input1": "random_normal_1/mean", 129 | "name": "random_normal_1", 130 | "op": "Add" 131 | }, 132 | "ph": "X", 133 | "dur": 48454, 134 | "pid": 1 135 | }, 136 | { 137 | "ts": 1490279990817805, 138 | "cat": "Op", 139 | "tid": 1, 140 | "name": "RandomStandardNormal", 141 | "args": { 142 | "input0": "random_normal/shape", 143 | "name": "random_normal/RandomStandardNormal", 144 | "op": "RandomStandardNormal" 145 | }, 146 | "ph": "X", 147 | "dur": 249269, 148 | "pid": 1 149 | }, 150 | { 151 | "ts": 1490279990817734, 152 | "cat": "DataFlow", 153 | "tid": 0, 154 | "name": "random_normal/shape", 155 | "ph": "s", 156 | "id": 0, 157 | "pid": 1 158 | }, 159 | { 160 | "ts": 1490279990817805, 161 | "cat": "DataFlow", 162 | "tid": 1, 163 | "name": "random_normal/shape", 164 | "ph": "t", 165 | "id": 0, 166 | "pid": 1 167 | }, 168 | { 169 | "ts": 1490279991067084, 170 | "cat": "Op", 171 | "tid": 0, 172 | "name": "Mul", 173 | "args": { 174 | "input0": "random_normal/RandomStandardNormal", 175 | "input1": "random_normal/stddev", 176 | "name": "random_normal/mul", 177 | "op": "Mul" 178 | }, 179 | "ph": "X", 180 | "dur": 15173, 181 | "pid": 1 182 | }, 183 | { 184 | "ts": 1490279991067074, 185 | "cat": "DataFlow", 186 | "tid": 1, 187 | "name": "random_normal/RandomStandardNormal", 188 | "ph": "s", 189 | "id": 1, 190 | "pid": 1 191 | }, 192 | { 193 | "ts": 1490279991067084, 194 | "cat": "DataFlow", 195 | "tid": 0, 196 | "name": "random_normal/RandomStandardNormal", 197 | "ph": "t", 198 | "id": 1, 199 | "pid": 1 200 | }, 201 | { 202 | "ts": 1490279991082274, 203 | "cat": "Op", 204 | "tid": 0, 205 | "name": "Add", 206 | "args": { 207 | "input0": "random_normal/mul", 208 | "input1": "random_normal/mean", 209 | "name": "random_normal", 210 | "op": "Add" 211 | }, 212 | "ph": "X", 213 | "dur": 12930, 214 | "pid": 1 215 | }, 216 | { 217 | "ts": 1490279991095220, 218 | "cat": "Op", 219 | "tid": 0, 220 | "name": "MatMul", 221 | "args": { 222 | "input0": "random_normal", 223 | "input1": "random_normal_1", 224 | "name": "MatMul", 225 | "op": "MatMul" 226 | }, 227 | "ph": "X", 228 | "dur": 504781, 229 | "pid": 1 230 | } 231 | ] 232 | } -------------------------------------------------------------------------------- /profiles/CPU/timeline_02_step_0.json: -------------------------------------------------------------------------------- 1 | { 2 | "traceEvents": [ 3 | { 4 | "pid": 0, 5 | "name": "process_name", 6 | "args": { 7 | "name": "Allocators" 8 | }, 9 | "ph": "M" 10 | }, 11 | { 12 | "pid": 1, 13 | "name": "process_name", 14 | "args": { 15 | "name": "/job:localhost/replica:0/task:0/cpu:0 Compute" 16 | }, 17 | "ph": "M" 18 | }, 19 | { 20 | "pid": 2, 21 | "name": "process_name", 22 | "args": { 23 | "name": "/job:localhost/replica:0/task:0/cpu:0 Tensors" 24 | }, 25 | "ph": "M" 26 | }, 27 | { 28 | "name": "NoOp", 29 | "args": { 30 | "name": "_SOURCE", 31 | "op": "NoOp" 32 | }, 33 | "ph": "X", 34 | "pid": 1, 35 | "dur": 10, 36 | "ts": 1490279999386725, 37 | "tid": 0, 38 | "cat": "Op" 39 | }, 40 | { 41 | "name": "Const", 42 | "args": { 43 | "name": "gradients/Reshape_grad/Shape", 44 | "op": "Const" 45 | }, 46 | "ph": "X", 47 | "pid": 1, 48 | "dur": 13, 49 | "ts": 1490279999386756, 50 | "tid": 0, 51 | "cat": "Op" 52 | }, 53 | { 54 | "name": "Const", 55 | "args": { 56 | "name": "GradientDescent/learning_rate", 57 | "op": "Const" 58 | }, 59 | "ph": "X", 60 | "pid": 1, 61 | "dur": 4, 62 | "ts": 1490279999386772, 63 | "tid": 0, 64 | "cat": "Op" 65 | }, 66 | { 67 | "name": "Const", 68 | "args": { 69 | "name": "concat/_1__cf__4", 70 | "op": "Const" 71 | }, 72 | "ph": "X", 73 | "pid": 1, 74 | "dur": 4, 75 | "ts": 1490279999386778, 76 | "tid": 0, 77 | "cat": "Op" 78 | }, 79 | { 80 | "name": "Const", 81 | "args": { 82 | "name": "gradients/SoftmaxCrossEntropyWithLogits_grad/ExpandDims/_2__cf__5", 83 | "op": "Const" 84 | }, 85 | "ph": "X", 86 | "pid": 1, 87 | "dur": 3, 88 | "ts": 1490279999386784, 89 | "tid": 0, 90 | "cat": "Op" 91 | }, 92 | { 93 | "name": "Variable", 94 | "args": { 95 | "name": "layer_3/fully_connected/biases", 96 | "op": "Variable" 97 | }, 98 | "ph": "X", 99 | "pid": 1, 100 | "dur": 7, 101 | "ts": 1490279999386790, 102 | "tid": 0, 103 | "cat": "Op" 104 | }, 105 | { 106 | "name": "Identity", 107 | "args": { 108 | "name": "layer_3/fully_connected/biases/read", 109 | "input0": "layer_3/fully_connected/biases", 110 | "op": "Identity" 111 | }, 112 | "ph": "X", 113 | "pid": 1, 114 | "dur": 6, 115 | "ts": 1490279999386801, 116 | "tid": 0, 117 | "cat": "Op" 118 | }, 119 | { 120 | "name": "Variable", 121 | "args": { 122 | "name": "layer_3/fully_connected/weights", 123 | "op": "Variable" 124 | }, 125 | "ph": "X", 126 | "pid": 1, 127 | "dur": 32, 128 | "ts": 1490279999386812, 129 | "tid": 0, 130 | "cat": "Op" 131 | }, 132 | { 133 | "name": "Identity", 134 | "args": { 135 | "name": "layer_3/fully_connected/weights/read", 136 | "input0": "layer_3/fully_connected/weights", 137 | "op": "Identity" 138 | }, 139 | "ph": "X", 140 | "pid": 1, 141 | "dur": 5, 142 | "ts": 1490279999386846, 143 | "tid": 0, 144 | "cat": "Op" 145 | }, 146 | { 147 | "name": "Variable", 148 | "args": { 149 | "name": "layer_1/fully_connected/weights", 150 | "op": "Variable" 151 | }, 152 | "ph": "X", 153 | "pid": 1, 154 | "dur": 5, 155 | "ts": 1490279999386849, 156 | "tid": 1, 157 | "cat": "Op" 158 | }, 159 | { 160 | "name": "Variable", 161 | "args": { 162 | "name": "layer_2/fully_connected/biases", 163 | "op": "Variable" 164 | }, 165 | "ph": "X", 166 | "pid": 1, 167 | "dur": 3, 168 | "ts": 1490279999386854, 169 | "tid": 0, 170 | "cat": "Op" 171 | }, 172 | { 173 | "name": "Reshape", 174 | "args": { 175 | "input1": "concat_1", 176 | "name": "Reshape_1", 177 | "input0": "_recv_Placeholder_1_0", 178 | "op": "Reshape" 179 | }, 180 | "ph": "X", 181 | "pid": 1, 182 | "dur": 9, 183 | "ts": 1490279999386846, 184 | "tid": 2, 185 | "cat": "Op" 186 | }, 187 | { 188 | "name": "Identity", 189 | "args": { 190 | "name": "layer_1/fully_connected/weights/read", 191 | "input0": "layer_1/fully_connected/weights", 192 | "op": "Identity" 193 | }, 194 | "ph": "X", 195 | "pid": 1, 196 | "dur": 5, 197 | "ts": 1490279999386857, 198 | "tid": 1, 199 | "cat": "Op" 200 | }, 201 | { 202 | "name": "Identity", 203 | "args": { 204 | "name": "layer_2/fully_connected/biases/read", 205 | "input0": "layer_2/fully_connected/biases", 206 | "op": "Identity" 207 | }, 208 | "ph": "X", 209 | "pid": 1, 210 | "dur": 4, 211 | "ts": 1490279999386859, 212 | "tid": 0, 213 | "cat": "Op" 214 | }, 215 | { 216 | "name": "Variable", 217 | "args": { 218 | "name": "layer_1/fully_connected/biases", 219 | "op": "Variable" 220 | }, 221 | "ph": "X", 222 | "pid": 1, 223 | "dur": 5, 224 | "ts": 1490279999386862, 225 | "tid": 2, 226 | "cat": "Op" 227 | }, 228 | { 229 | "name": "Variable", 230 | "args": { 231 | "name": "layer_2/fully_connected/weights", 232 | "op": "Variable" 233 | }, 234 | "ph": "X", 235 | "pid": 1, 236 | "dur": 4, 237 | "ts": 1490279999386866, 238 | "tid": 0, 239 | "cat": "Op" 240 | }, 241 | { 242 | "name": "Identity", 243 | "args": { 244 | "name": "layer_1/fully_connected/biases/read", 245 | "input0": "layer_1/fully_connected/biases", 246 | "op": "Identity" 247 | }, 248 | "ph": "X", 249 | "pid": 1, 250 | "dur": 5, 251 | "ts": 1490279999386870, 252 | "tid": 1, 253 | "cat": "Op" 254 | }, 255 | { 256 | "id": 0, 257 | "name": "layer_1/fully_connected/biases", 258 | "ph": "s", 259 | "pid": 1, 260 | "ts": 1490279999386867, 261 | "tid": 2, 262 | "cat": "DataFlow" 263 | }, 264 | { 265 | "id": 0, 266 | "name": "layer_1/fully_connected/biases", 267 | "ph": "t", 268 | "pid": 1, 269 | "ts": 1490279999386870, 270 | "tid": 1, 271 | "cat": "DataFlow" 272 | }, 273 | { 274 | "name": "Identity", 275 | "args": { 276 | "name": "layer_2/fully_connected/weights/read", 277 | "input0": "layer_2/fully_connected/weights", 278 | "op": "Identity" 279 | }, 280 | "ph": "X", 281 | "pid": 1, 282 | "dur": 4, 283 | "ts": 1490279999386872, 284 | "tid": 0, 285 | "cat": "Op" 286 | }, 287 | { 288 | "name": "MatMul", 289 | "args": { 290 | "input1": "layer_1/fully_connected/weights/read", 291 | "name": "layer_1/fully_connected/MatMul", 292 | "input0": "_recv_Placeholder_0", 293 | "op": "MatMul" 294 | }, 295 | "ph": "X", 296 | "pid": 1, 297 | "dur": 4743, 298 | "ts": 1490279999386864, 299 | "tid": 3, 300 | "cat": "Op" 301 | }, 302 | { 303 | "id": 1, 304 | "name": "layer_1/fully_connected/weights/read", 305 | "ph": "s", 306 | "pid": 1, 307 | "ts": 1490279999386862, 308 | "tid": 1, 309 | "cat": "DataFlow" 310 | }, 311 | { 312 | "id": 1, 313 | "name": "layer_1/fully_connected/weights/read", 314 | "ph": "t", 315 | "pid": 1, 316 | "ts": 1490279999386864, 317 | "tid": 3, 318 | "cat": "DataFlow" 319 | }, 320 | { 321 | "name": "BiasAdd", 322 | "args": { 323 | "input1": "layer_1/fully_connected/biases/read", 324 | "name": "layer_1/fully_connected/BiasAdd", 325 | "input0": "layer_1/fully_connected/MatMul", 326 | "op": "BiasAdd" 327 | }, 328 | "ph": "X", 329 | "pid": 1, 330 | "dur": 955, 331 | "ts": 1490279999391625, 332 | "tid": 0, 333 | "cat": "Op" 334 | }, 335 | { 336 | "id": 2, 337 | "name": "layer_1/fully_connected/MatMul", 338 | "ph": "s", 339 | "pid": 1, 340 | "ts": 1490279999391607, 341 | "tid": 3, 342 | "cat": "DataFlow" 343 | }, 344 | { 345 | "id": 2, 346 | "name": "layer_1/fully_connected/MatMul", 347 | "ph": "t", 348 | "pid": 1, 349 | "ts": 1490279999391625, 350 | "tid": 0, 351 | "cat": "DataFlow" 352 | }, 353 | { 354 | "id": 3, 355 | "name": "layer_1/fully_connected/biases/read", 356 | "ph": "s", 357 | "pid": 1, 358 | "ts": 1490279999386875, 359 | "tid": 1, 360 | "cat": "DataFlow" 361 | }, 362 | { 363 | "id": 3, 364 | "name": "layer_1/fully_connected/biases/read", 365 | "ph": "t", 366 | "pid": 1, 367 | "ts": 1490279999391625, 368 | "tid": 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