tf.compat.v1.DeviceSpec

Represents a (possibly partial) specification for a TensorFlow device.

Inherits From: DeviceSpec

DeviceSpecs 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.

Example:

# 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:

  • Job: The job name.
  • Replica: The replica index.
  • Task: The task index.
  • Device type: The device type string (e.g. "CPU" or "GPU").
  • Device index: The device index.
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

Methods

from_string

View source

Construct a DeviceSpec from a string.

Args
spec a string of the form /job:/replica:/task:/device:CPU: or /job:/replica:/task:/device:GPU: as cpu and gpu are mutually exclusive. All entries are optional.
Returns
A DeviceSpec.

make_merged_spec

View source

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

View source

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

View source

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:/replica:/task:/device:CPU: or /job:/replica:/task:/device:GPU: as cpu and gpu are mutually exclusive. All entries are optional.
Returns
The DeviceSpec.
Raises
ValueError if the spec was not valid.

replace

View source

Convenience method for making a new DeviceSpec by overriding fields.

For instance:

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

View source

Return a string representation of this DeviceSpec.

Returns
a string of the form /job:/replica:/task:/device::.

__eq__

View source

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/r2.4/api_docs/python/tf/compat/v1/DeviceSpec