139 lines
4.9 KiB
Python
139 lines
4.9 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.
|
|
# ==============================================================================
|
|
"""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()
|