forked from tensorflow/tensorflow
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtimeline_test.py
More file actions
88 lines (74 loc) · 3.15 KB
/
Copy pathtimeline_test.py
File metadata and controls
88 lines (74 loc) · 3.15 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
# Copyright 2016 Google Inc. 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.
# ==============================================================================
"""Tests for tensorflow.python.client.Timeline."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import tensorflow as tf
from tensorflow.python.client import timeline
class TimelineTest(tf.test.TestCase):
def _validateTrace(self, chrome_trace_format):
# Check that the supplied string is valid JSON.
trace = json.loads(chrome_trace_format)
# It should have a top-level key containing events.
self.assertTrue('traceEvents' in trace)
# Every event in the list should have a 'ph' field.
for event in trace['traceEvents']:
self.assertTrue('ph' in event)
def testSimpleTimeline(self):
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
with tf.device('/cpu:0'):
with tf.Session() as sess:
sess.run(
tf.constant(1.0),
options=run_options,
run_metadata=run_metadata)
self.assertTrue(run_metadata.HasField('step_stats'))
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
self._validateTrace(ctf)
def testTimeline(self):
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
with tf.device('/cpu:0'):
with tf.Session() as sess:
const1 = tf.constant(1.0, name='const1')
const2 = tf.constant(2.0, name='const2')
result = tf.add(const1, const2) + const1 * const2
sess.run(result, options=run_options, run_metadata=run_metadata)
self.assertTrue(run_metadata.HasField('step_stats'))
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
self._validateTrace(ctf)
def testManyCPUs(self):
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
config = tf.ConfigProto(device_count={'CPU': 3})
with tf.Session(config=config) as sess:
with tf.device('/cpu:0'):
const1 = tf.constant(1.0, name='const1')
with tf.device('/cpu:1'):
const2 = tf.constant(2.0, name='const2')
with tf.device('/cpu:2'):
result = const1 + const2 + const1 * const2
sess.run(result, options=run_options, run_metadata=run_metadata)
self.assertTrue(run_metadata.HasField('step_stats'))
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
self._validateTrace(ctf)
if __name__ == '__main__':
tf.test.main()