123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
# Copyright 2017 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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"""A python interface for Grappler clusters."""
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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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import contextlib
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from tensorflow.core.framework import step_stats_pb2
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from tensorflow.core.grappler.costs import op_performance_data_pb2
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from tensorflow.core.protobuf import device_properties_pb2
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from tensorflow.python import _pywrap_tf_cluster as tf_cluster
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class Cluster(object):
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"""Grappler Clusters."""
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def __init__(self,
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allow_soft_placement=True,
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disable_detailed_stats=True,
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disable_timeline=True,
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devices=None):
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"""Creates a Cluster.
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Args:
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allow_soft_placement: If True, TF will automatically fix illegal
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placements instead of erroring out if the placement isn't legal.
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disable_detailed_stats: If True, detailed statistics will not be
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available.
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disable_timeline: If True, the timeline information will not be reported.
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devices: A list of devices of type device_properties_pb2.NamedDevice.
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If None, a device list will be created based on the spec of
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the local machine.
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"""
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self._tf_cluster = None
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self._generate_timeline = not disable_timeline
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if devices is None:
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self._tf_cluster = tf_cluster.TF_NewCluster(allow_soft_placement,
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disable_detailed_stats)
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else:
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devices_serialized = [device.SerializeToString() for device in devices]
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self._tf_cluster = tf_cluster.TF_NewVirtualCluster(devices_serialized)
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def Shutdown(self):
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if self._tf_cluster is not None:
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tf_cluster.TF_ShutdownCluster(self._tf_cluster)
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self._tf_cluster = None
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def __del__(self):
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self.Shutdown()
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@property
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def tf_cluster(self):
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return self._tf_cluster
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def ListDevices(self):
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"""Returns a list of available hardware devices."""
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if self._tf_cluster is None:
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return []
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return [device_properties_pb2.NamedDevice.FromString(device)
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for device in tf_cluster.TF_ListDevices(self._tf_cluster)]
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def ListAvailableOps(self):
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"""Returns a list of all available operations (sorted alphabetically)."""
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return tf_cluster.TF_ListAvailableOps()
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def GetSupportedDevices(self, item):
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return tf_cluster.TF_GetSupportedDevices(self._tf_cluster, item.tf_item)
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def EstimatePerformance(self, device):
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return tf_cluster.TF_EstimatePerformance(device.SerializeToString())
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def MeasureCosts(self, item):
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"""Returns the cost of running the specified item.
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Args:
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item: The item for which to measure the costs.
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Returns: The triplet op_perfs, runtime, step_stats.
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"""
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op_perf_bytes_list, run_time, step_stats_bytes = tf_cluster.TF_MeasureCosts(
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item.tf_item, self._tf_cluster, self._generate_timeline)
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op_perfs = [op_performance_data_pb2.OpPerformance.FromString(op_perf_bytes)
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for op_perf_bytes in op_perf_bytes_list]
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return (op_perfs, run_time,
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step_stats_pb2.StepStats.FromString(step_stats_bytes))
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def DeterminePeakMemoryUsage(self, item):
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"""Returns a snapshot of the peak memory usage.
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Args:
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item: The item for which to measure the costs.
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Returns: A hashtable indexed by device name.
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"""
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return tf_cluster.TF_DeterminePeakMemoryUsage(item.tf_item,
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self._tf_cluster)
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@contextlib.contextmanager
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def Provision(allow_soft_placement=True,
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disable_detailed_stats=True,
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disable_timeline=True,
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devices=None):
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cluster = Cluster(allow_soft_placement, disable_detailed_stats,
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disable_timeline, devices)
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yield cluster
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cluster.Shutdown()
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