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