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tensorflow/tensorflow/python/grappler/cluster.py at master · codeplaysoftware/tensorflow · GitHub
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tensorflow
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python
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grappler
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cluster.py
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tensorflow
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tensorflow
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grappler
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cluster.py
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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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