9f7f6e9faa
v1/eager profiler_client.py will use default options. add device_tracer_level to ProfilerOptions and allow default values (no need to specify all options and in specific order) PiperOrigin-RevId: 306308283 Change-Id: Ia9a6a7cfde37142be79e314627b9d63993b6283c
130 lines
5.1 KiB
Python
130 lines
5.1 KiB
Python
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Profiler client APIs."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.profiler.internal import _pywrap_profiler
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from tensorflow.python.util.tf_export import tf_export
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_GRPC_PREFIX = 'grpc://'
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@tf_export('profiler.experimental.client.trace', v1=[])
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def trace(service_addr,
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logdir,
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duration_ms,
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worker_list='',
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num_tracing_attempts=3,
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options=None):
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"""Sends grpc requests to profiler server to perform on-demand profiling.
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This method will block caller thread until it receives tracing result. This
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method supports CPU, GPU, and Cloud TPU. This method supports profiling a
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single host for CPU, GPU, TPU, as well as multiple TPU workers.
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The profiled results will be saved to your specified TensorBoard log
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directory (e.g. the directory you save your model checkpoints). Use the
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TensorBoard profile plugin to view the visualization and analysis results.
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Args:
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service_addr: gRPC address of profiler service e.g. grpc://localhost:6009.
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logdir: Path of TensorBoard log directory e.g. /tmp/tb_log.
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duration_ms: Duration of tracing or monitoring in ms.
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worker_list: Optional. The list of workers that we are about to profile in
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the current session (TPU only).
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num_tracing_attempts: Optional. Automatically retry N times when no trace
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event is collected (default 3).
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options: profiler.experimental.ProfilerOptions namedtuple for miscellaneous
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profiler options.
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Raises:
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UnavailableError: If no trace event is collected.
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Example usage (CPU/GPU):
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# Start a profiler server before your model runs.
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```python
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tf.profiler.experimental.server.start(6009)
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# your model code.
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# Send gRPC request to the profiler server to collect a trace of your model.
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```python
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tf.profiler.experimental.client.trace('grpc://localhost:6009',
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'/tmp/tb_log', 2000)
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Example usage (TPU):
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# Send gRPC request to a TPU worker to collect a trace of your model. A
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# profiler service has been started in the TPU worker at port 8466.
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```python
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# E.g. your TPU IP address is 10.0.0.2 and you want to profile for 2 seconds.
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tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
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'gs://your_tb_dir', 2000)
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Example usage (Multiple TPUs):
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# Send gRPC request to a TPU pod to collect a trace of your model on multiple
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# TPUs. A profiler service has been started in all the TPU workers at the
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# port 8466.
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```python
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# E.g. your TPU IP addresses are 10.0.0.2, 10.0.0.3, 10.0.0.4, and you want to
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# profile for 2 seconds.
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tf.profiler.experimental.client.trace('grpc://10.0.0.2:8466',
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'gs://your_tb_dir',
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2000, '10.0.0.3,10.0.0.4')
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Launch TensorBoard and point it to the same logdir you provided to this API.
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$ tensorboard --logdir=/tmp/tb_log (or gs://your_tb_dir in the above examples)
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Open your browser and go to localhost:6006/#profile to view profiling results.
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"""
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opts = dict(options._asdict()) if options is not None else {}
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_pywrap_profiler.trace(
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_strip_prefix(service_addr, _GRPC_PREFIX), logdir, worker_list, True,
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duration_ms, num_tracing_attempts, opts)
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@tf_export('profiler.experimental.client.monitor', v1=[])
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def monitor(service_addr, duration_ms, level=1):
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"""Sends grpc requests to profiler server to perform on-demand monitoring.
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The monitoring result is a light weight performance summary of your model
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execution. This method will block the caller thread until it receives the
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monitoring result. This method currently supports Cloud TPU only.
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Args:
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service_addr: gRPC address of profiler service e.g. grpc://10.0.0.2:8466.
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duration_ms: Duration of monitoring in ms.
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level: Choose a monitoring level between 1 and 2 to monitor your job. Level
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2 is more verbose than level 1 and shows more metrics.
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Returns:
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A string of monitoring output.
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Example usage:
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# Continuously send gRPC requests to the Cloud TPU to monitor the model
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# execution.
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```python
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for query in range(0, 100):
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print(tf.profiler.experimental.client.monitor('grpc://10.0.0.2:8466', 1000))
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"""
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return _pywrap_profiler.monitor(
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_strip_prefix(service_addr, _GRPC_PREFIX), duration_ms, level, True)
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def _strip_prefix(s, prefix):
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return s[len(prefix):] if s.startswith(prefix) else s
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