tf.compat.v1.DeviceSpec 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
)
 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 
@classmethod
from_string(
    spec
)
 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 
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 View source 
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 View source 
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:/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 
replace(
    **kwargs
)
 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 
to_string()
 Return a string representation of this DeviceSpec.
 


 Returns   a string of the form /job:/replica:/task:/device::. 
 
 __eq__ View source 
__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.