tensorflow/python/profiler/profiler_client.py - platform/external/tensorflow - Git at Google

 # Copyright 2020 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.
 # ==============================================================================
 """Profiler client APIs."""

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function

 from tensorflow.python.framework import errors
 from tensorflow.python.profiler.internal import _pywrap_profiler

 from tensorflow.python.util.tf_export import tf_export

 _GRPC_PREFIX = 'grpc://'


 @tf_export('profiler.experimental.client.trace', v1=[])
 def trace(service_addr,
           logdir,
           duration_ms,
           worker_list='',
           num_tracing_attempts=3,
           options=None):
   """Sends gRPC requests to one or more profiler servers to perform on-demand profiling.

   This method will block the calling thread until it receives responses from all
   servers or until deadline expiration. Both single host and multiple host
   profiling are supported on CPU, GPU, and TPU.
   The profiled results will be saved by each server to the specified TensorBoard
   log directory (i.e. the directory you save your model checkpoints). Use the
   TensorBoard profile plugin to view the visualization and analysis results.

   Args:
     service_addr: A comma delimited string of gRPC addresses of the workers to
       profile.
       e.g. service_addr='grpc://localhost:6009'
            service_addr='grpc://10.0.0.2:8466,grpc://10.0.0.3:8466'
            service_addr='grpc://localhost:12345,grpc://localhost:23456'
     logdir: Path to save profile data to, typically a TensorBoard log directory.
       This path must be accessible to both the client and server.
       e.g. logdir='gs://your_tb_dir'
     duration_ms: Duration of tracing or monitoring in milliseconds. Must be
       greater than zero.
     worker_list: An optional TPU only configuration. The list of workers to
       profile in the current session.
     num_tracing_attempts: Optional. Automatically retry N times when no trace
       event is collected (default 3).
     options: profiler.experimental.ProfilerOptions namedtuple for miscellaneous
       profiler options.

   Raises:
     InvalidArgumentError: For when arguments fail validation checks.
     UnavailableError: If no trace event was collected.

   Example usage (CPU/GPU):
   # Start a profiler server before your model runs.
   ```python
   tf.profiler.experimental.server.start(6009)
   # (Model code goes here).
   # Send gRPC request to the profiler server to collect a trace of your model.
   ```python
   tf.profiler.experimental.client.trace('grpc://localhost:6009',
                                         '/nfs/tb_log', 2000)

   Example usage (Multiple GPUs):
   # E.g. your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you
   # would like to schedule start of profiling 1 second from now, for a duration
   # of 2 seconds.
   options['delay_ms'] = 1000
   tf.profiler.experimental.client.trace(
       'grpc://10.0.0.2:8466,grpc://10.0.0.3:8466,grpc://10.0.0.4:8466',
       'gs://your_tb_dir',
       2000,
       options=options)

   Example usage (TPU):
   # Send gRPC request to a TPU worker to collect a trace of your model. A
   # profiler service has been started in the TPU worker at port 8466.
   ```python
   # E.g. your TPU IP address is 10.0.0.2 and you want to profile for 2 seconds.
   tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
                                         'gs://your_tb_dir', 2000)

   Example usage (Multiple TPUs):
   # Send gRPC request to a TPU pod to collect a trace of your model on multiple
   # TPUs. A profiler service has been started in all the TPU workers at the
   # port 8466.
   ```python
   # E.g. your TPU IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you want to
   # profile for 2 seconds.
   tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
                                         'gs://your_tb_dir',
                                         2000, '10.0.0.2,10.0.0.3,10.0.0.4')
   Launch TensorBoard and point it to the same logdir you provided to this API.
   $ tensorboard --logdir=/tmp/tb_log (or gs://your_tb_dir in the above examples)
   Open your browser and go to localhost:6006/#profile to view profiling results.

   """
   if duration_ms <= 0:
     raise errors.InvalidArgumentError(None, None,
                                       'duration_ms must be greater than zero.')

   opts = dict(options._asdict()) if options is not None else {}
   _pywrap_profiler.trace(
       _strip_addresses(service_addr, _GRPC_PREFIX), logdir, worker_list, True,
       duration_ms, num_tracing_attempts, opts)


 @tf_export('profiler.experimental.client.monitor', v1=[])
 def monitor(service_addr, duration_ms, level=1):
   """Sends grpc requests to profiler server to perform on-demand monitoring.

   The monitoring result is a light weight performance summary of your model
   execution. This method will block the caller thread until it receives the
   monitoring result. This method currently supports Cloud TPU only.

   Args:
     service_addr: gRPC address of profiler service e.g. grpc://10.0.0.2:8466.
     duration_ms: Duration of monitoring in ms.
     level: Choose a monitoring level between 1 and 2 to monitor your job. Level
       2 is more verbose than level 1 and shows more metrics.

   Returns:
     A string of monitoring output.

   Example usage:
   # Continuously send gRPC requests to the Cloud TPU to monitor the model
   # execution.
   ```python
   for query in range(0, 100):
     print(tf.profiler.experimental.client.monitor('grpc://10.0.0.2:8466', 1000))


   """
   return _pywrap_profiler.monitor(
       _strip_prefix(service_addr, _GRPC_PREFIX), duration_ms, level, True)


 def _strip_prefix(s, prefix):
   return s[len(prefix):] if s.startswith(prefix) else s


 def _strip_addresses(addresses, prefix):
   return ','.join([_strip_prefix(s, prefix) for s in addresses.split(',')])
	# Copyright 2020 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.
	# ==============================================================================
	"""Profiler client APIs."""

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	from tensorflow.python.framework import errors
	from tensorflow.python.profiler.internal import _pywrap_profiler

	from tensorflow.python.util.tf_export import tf_export

	_GRPC_PREFIX = 'grpc://'


	@tf_export('profiler.experimental.client.trace', v1=[])
	def trace(service_addr,
	logdir,
	duration_ms,
	worker_list='',
	num_tracing_attempts=3,
	options=None):
	"""Sends gRPC requests to one or more profiler servers to perform on-demand profiling.

	This method will block the calling thread until it receives responses from all
	servers or until deadline expiration. Both single host and multiple host
	profiling are supported on CPU, GPU, and TPU.
	The profiled results will be saved by each server to the specified TensorBoard
	log directory (i.e. the directory you save your model checkpoints). Use the
	TensorBoard profile plugin to view the visualization and analysis results.

	Args:
	service_addr: A comma delimited string of gRPC addresses of the workers to
	profile.
	e.g. service_addr='grpc://localhost:6009'
	service_addr='grpc://10.0.0.2:8466,grpc://10.0.0.3:8466'
	service_addr='grpc://localhost:12345,grpc://localhost:23456'
	logdir: Path to save profile data to, typically a TensorBoard log directory.
	This path must be accessible to both the client and server.
	e.g. logdir='gs://your_tb_dir'
	duration_ms: Duration of tracing or monitoring in milliseconds. Must be
	greater than zero.
	worker_list: An optional TPU only configuration. The list of workers to
	profile in the current session.
	num_tracing_attempts: Optional. Automatically retry N times when no trace
	event is collected (default 3).
	options: profiler.experimental.ProfilerOptions namedtuple for miscellaneous
	profiler options.

	Raises:
	InvalidArgumentError: For when arguments fail validation checks.
	UnavailableError: If no trace event was collected.

	Example usage (CPU/GPU):
	# Start a profiler server before your model runs.
	```python
	tf.profiler.experimental.server.start(6009)
	# (Model code goes here).
	# Send gRPC request to the profiler server to collect a trace of your model.
	```python
	tf.profiler.experimental.client.trace('grpc://localhost:6009',
	'/nfs/tb_log', 2000)

	Example usage (Multiple GPUs):
	# E.g. your worker IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you
	# would like to schedule start of profiling 1 second from now, for a duration
	# of 2 seconds.
	options['delay_ms'] = 1000
	tf.profiler.experimental.client.trace(
	'grpc://10.0.0.2:8466,grpc://10.0.0.3:8466,grpc://10.0.0.4:8466',
	'gs://your_tb_dir',
	2000,
	options=options)

	Example usage (TPU):
	# Send gRPC request to a TPU worker to collect a trace of your model. A
	# profiler service has been started in the TPU worker at port 8466.
	```python
	# E.g. your TPU IP address is 10.0.0.2 and you want to profile for 2 seconds.
	tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
	'gs://your_tb_dir', 2000)

	Example usage (Multiple TPUs):
	# Send gRPC request to a TPU pod to collect a trace of your model on multiple
	# TPUs. A profiler service has been started in all the TPU workers at the
	# port 8466.
	```python
	# E.g. your TPU IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you want to
	# profile for 2 seconds.
	tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
	'gs://your_tb_dir',
	2000, '10.0.0.2,10.0.0.3,10.0.0.4')
	Launch TensorBoard and point it to the same logdir you provided to this API.
	$ tensorboard --logdir=/tmp/tb_log (or gs://your_tb_dir in the above examples)
	Open your browser and go to localhost:6006/#profile to view profiling results.

	"""
	if duration_ms <= 0:
	raise errors.InvalidArgumentError(None, None,
	'duration_ms must be greater than zero.')

	opts = dict(options._asdict()) if options is not None else {}
	_pywrap_profiler.trace(
	_strip_addresses(service_addr, _GRPC_PREFIX), logdir, worker_list, True,
	duration_ms, num_tracing_attempts, opts)


	@tf_export('profiler.experimental.client.monitor', v1=[])
	def monitor(service_addr, duration_ms, level=1):
	"""Sends grpc requests to profiler server to perform on-demand monitoring.

	The monitoring result is a light weight performance summary of your model
	execution. This method will block the caller thread until it receives the
	monitoring result. This method currently supports Cloud TPU only.

	Args:
	service_addr: gRPC address of profiler service e.g. grpc://10.0.0.2:8466.
	duration_ms: Duration of monitoring in ms.
	level: Choose a monitoring level between 1 and 2 to monitor your job. Level
	2 is more verbose than level 1 and shows more metrics.

	Returns:
	A string of monitoring output.

	Example usage:
	# Continuously send gRPC requests to the Cloud TPU to monitor the model
	# execution.
	```python
	for query in range(0, 100):
	print(tf.profiler.experimental.client.monitor('grpc://10.0.0.2:8466', 1000))


	"""
	return _pywrap_profiler.monitor(
	_strip_prefix(service_addr, _GRPC_PREFIX), duration_ms, level, True)


	def _strip_prefix(s, prefix):
	return s[len(prefix):] if s.startswith(prefix) else s


	def _strip_addresses(addresses, prefix):
	return ','.join([_strip_prefix(s, prefix) for s in addresses.split(',')])