Represents a (possibly partial) specification for a TensorFlow device.
Inherits From: DeviceSpec
tf.compat.v1.DeviceSpec( job=None, replica=None, task=None, device_type=None, device_index=None )
DeviceSpec
s are used throughout TensorFlow to describe where state is stored and computations occur. Using DeviceSpec
allows you to parse device spec strings to verify their validity, merge them or compose them programmatically.
# Place the operations on device "GPU:0" in the "ps" job. device_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0) with tf.device(device_spec.to_string()): # Both my_var and squared_var will be placed on /job:ps/device:GPU:0. my_var = tf.Variable(..., name="my_variable") squared_var = tf.square(my_var)
With eager execution disabled (by default in TensorFlow 1.x and by calling disable_eager_execution() in TensorFlow 2.x), the following syntax can be used:
tf.compat.v1.disable_eager_execution() # Same as previous device_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0) # No need of .to_string() method. with tf.device(device_spec): my_var = tf.Variable(..., name="my_variable") squared_var = tf.square(my_var) ``` If a `DeviceSpec` is partially specified, it will be merged with other `DeviceSpec`s according to the scope in which it is defined. `DeviceSpec` components defined in inner scopes take precedence over those defined in outer scopes. ```python gpu0_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0) with tf.device(DeviceSpec(job="train").to_string()): with tf.device(gpu0_spec.to_string()): # Nodes created here will be assigned to /job:ps/device:GPU:0. with tf.device(DeviceSpec(device_type="GPU", device_index=1).to_string()): # Nodes created here will be assigned to /job:train/device:GPU:1.
A DeviceSpec
consists of 5 components -- each of which is optionally specified:
Args | |
---|---|
job | string. Optional job name. |
replica | int. Optional replica index. |
task | int. Optional task index. |
device_type | Optional device type string (e.g. "CPU" or "GPU") |
device_index | int. Optional device index. If left unspecified, device represents 'any' device_index. |
Attributes | |
---|---|
device_index | |
device_type | |
job | |
replica | |
task |
from_string
@classmethod from_string( spec )
Construct a DeviceSpec
from a string.
Args | |
---|---|
spec | a string of the form /job: |
Returns | |
---|---|
A DeviceSpec. |
make_merged_spec
make_merged_spec( dev )
Returns a new DeviceSpec which incorporates dev
.
When combining specs, dev
will take precedence over the current spec. So for instance:
first_spec = tf.DeviceSpec(job=0, device_type="CPU") second_spec = tf.DeviceSpec(device_type="GPU") combined_spec = first_spec.make_merged_spec(second_spec)
is equivalent to:
combined_spec = tf.DeviceSpec(job=0, device_type="GPU")
Args | |
---|---|
dev | a DeviceSpec |
Returns | |
---|---|
A new DeviceSpec which combines self and dev |
merge_from
merge_from( dev )
Merge the properties of "dev" into this DeviceSpec
.
Note: Will be removed in TensorFlow 2.x since DeviceSpecs will become immutable.
Args | |
---|---|
dev | a DeviceSpec . |
parse_from_string
parse_from_string( spec )
Parse a DeviceSpec
name into its components.
2.x behavior change: In TensorFlow 1.x, this function mutates its own state and returns itself. In 2.x, DeviceSpecs are immutable, and this function will return a DeviceSpec which contains the spec.
Recommended:
# my_spec and my_updated_spec are unrelated. my_spec = tf.DeviceSpec.from_string("/CPU:0") my_updated_spec = tf.DeviceSpec.from_string("/GPU:0") with tf.device(my_updated_spec): ...
Will work in 1.x and 2.x (though deprecated in 2.x):
my_spec = tf.DeviceSpec.from_string("/CPU:0") my_updated_spec = my_spec.parse_from_string("/GPU:0") with tf.device(my_updated_spec): ...
Will NOT work in 2.x:
my_spec = tf.DeviceSpec.from_string("/CPU:0") my_spec.parse_from_string("/GPU:0") # <== Will not update my_spec with tf.device(my_spec): ...
In general, DeviceSpec.from_string
should completely replace DeviceSpec.parse_from_string
, and DeviceSpec.replace
should completely replace setting attributes directly.
Args | |
---|---|
spec | an optional string of the form /job: |
Returns | |
---|---|
The DeviceSpec . |
Raises | |
---|---|
ValueError | if the spec was not valid. |
replace
replace( **kwargs )
Convenience method for making a new DeviceSpec by overriding fields.
my_spec = DeviceSpec=(job="my_job", device="CPU") my_updated_spec = my_spec.replace(device="GPU") my_other_spec = my_spec.replace(device=None)
Args | |
---|---|
**kwargs | This method takes the same args as the DeviceSpec constructor |
Returns | |
---|---|
A DeviceSpec with the fields specified in kwargs overridden. |
to_string
to_string()
Return a string representation of this DeviceSpec
.
Returns | |
---|---|
a string of the form /job: |
__eq__
__eq__( other )
Checks if the other
DeviceSpec is same as the current instance, eg have
same value for all the internal fields.
Args | |
---|---|
other | Another DeviceSpec |
Returns | |
---|---|
Return True if other is also a DeviceSpec instance and has same value as the current instance. Return False otherwise. |
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/compat/v1/DeviceSpec