3RNN/Lib/site-packages/tensorflow/python/eager/profiler.py
2024-05-26 19:49:15 +02:00

194 lines
6.3 KiB
Python

# Copyright 2017 The TensorFlow Authors. 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.
# ==============================================================================
"""TensorFlow 2.0 Profiler for both Eager Mode and Graph Mode.
The profiler has two mode:
- Programmatic Mode: start(), stop() and Profiler class. It will perform
when calling start() or create Profiler class and will stop
when calling stop() or destroying Profiler class.
- On-demand Mode: start_profiler_server(). It will perform profiling when
receive profiling request.
NOTE: Only one active profiler session is allowed. Use of simultaneous
Programmatic Mode and On-demand Mode is undefined and will likely fail.
NOTE: The Keras TensorBoard callback will automatically perform sampled
profiling. Before enabling customized profiling, set the callback flag
"profile_batches=[]" to disable automatic sampled profiling.
customized profiling.
"""
import datetime
import os
import threading
from tensorflow.python.client import _pywrap_events_writer
from tensorflow.python.eager import context
from tensorflow.python.framework import errors
from tensorflow.python.platform import gfile
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.profiler.internal import _pywrap_profiler
from tensorflow.python.util import compat
from tensorflow.python.util.deprecation import deprecated
_profiler = None
_profiler_lock = threading.Lock()
_run_num = 0
# This suffix should be kept in sync with kProfileEmptySuffix in
# tensorflow/core/profiler/rpc/client/capture_profile.cc.
_EVENT_FILE_SUFFIX = '.profile-empty'
class ProfilerAlreadyRunningError(Exception):
pass
class ProfilerNotRunningError(Exception):
pass
@deprecated('2020-07-01', 'use `tf.profiler.experimental.start` instead.')
def start(options=None):
"""Start profiling.
Args:
options: profiler options.
Raises:
ProfilerAlreadyRunningError: If another profiling session is running.
"""
global _profiler
with _profiler_lock:
if _profiler is not None:
raise ProfilerAlreadyRunningError('Another profiler is running.')
if context.default_execution_mode == context.EAGER_MODE:
context.ensure_initialized()
_profiler = _pywrap_profiler.ProfilerSession()
try:
_profiler.start('', options if options is not None else {})
except errors.AlreadyExistsError:
logging.warning('Another profiler session is running which is probably '
'created by profiler server. Please avoid using profiler '
'server and profiler APIs at the same time.')
raise ProfilerAlreadyRunningError('Another profiler is running.')
@deprecated('2020-07-01', 'use `tf.profiler.experimental.stop` instead.')
def stop():
"""Stop current profiling session and return its result.
Returns:
A binary string of tensorflow.tpu.Trace. User can write the string
to file for offline analysis by tensorboard.
Raises:
ProfilerNotRunningError: If there is no active profiling session.
"""
global _profiler
global _run_num
with _profiler_lock:
if _profiler is None:
raise ProfilerNotRunningError(
'Cannot stop profiling. No profiler is running.')
if context.default_execution_mode == context.EAGER_MODE:
context.context().executor.wait()
result = _profiler.stop()
_profiler = None
_run_num += 1
return result
@deprecated(
'2020-07-01',
'`tf.python.eager.profiler` has deprecated, use `tf.profiler` instead.'
)
def maybe_create_event_file(logdir):
"""Create an empty event file if not already exists.
This event file indicates that we have a plugins/profile/ directory in the
current logdir.
Args:
logdir: log directory.
"""
for file_name in gfile.ListDirectory(logdir):
if file_name.endswith(_EVENT_FILE_SUFFIX):
return
# TODO(b/127330388): Use summary_ops_v2.create_file_writer instead.
event_writer = _pywrap_events_writer.EventsWriter(
compat.as_bytes(os.path.join(logdir, 'events')))
event_writer.InitWithSuffix(compat.as_bytes(_EVENT_FILE_SUFFIX))
@deprecated(
'2020-07-01',
'`tf.python.eager.profiler` has deprecated, use `tf.profiler` instead.'
)
def save(logdir, result):
"""Save profile result to TensorBoard logdir.
Args:
logdir: log directory read by TensorBoard.
result: profiling result returned by stop().
"""
plugin_dir = os.path.join(
logdir, 'plugins', 'profile',
datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S'))
gfile.MakeDirs(plugin_dir)
maybe_create_event_file(logdir)
with gfile.Open(os.path.join(plugin_dir, 'local.trace'), 'wb') as f:
f.write(result)
@deprecated('2020-07-01', 'use `tf.profiler.experimental.server.start`.')
def start_profiler_server(port):
"""Start a profiler grpc server that listens to given port.
The profiler server will keep the program running even the training finishes.
Please shutdown the server with CTRL-C. It can be used in both eager mode and
graph mode. The service defined in
tensorflow/core/profiler/profiler_service.proto. Please use
tensorflow/contrib/tpu/profiler/capture_tpu_profile to capture tracable
file following https://cloud.google.com/tpu/docs/cloud-tpu-tools#capture_trace
Args:
port: port profiler server listens to.
"""
if context.default_execution_mode == context.EAGER_MODE:
context.ensure_initialized()
_pywrap_profiler.start_server(port)
@deprecated('2020-07-01', 'use `tf.profiler.experimental.Profile` instead.')
class Profiler(object):
"""Context-manager eager profiler api.
Example usage:
```python
with Profiler("/path/to/logdir"):
# do some work
```
"""
def __init__(self, logdir):
self._logdir = logdir
def __enter__(self):
start()
def __exit__(self, typ, value, tb):
result = stop()
save(self._logdir, result)