Release 0.15.0

Major Features and Improvements

Updated tensorflow-addons package dependency to 0.9.0.
Added API to expose the native backend more conveniently. See
tff.backends.native.* for more information.
Added a compiler argument to the tff.framework.ExecutionContext API and
provided a compiler for the native execution environment, which improves
TFF’s default concurrency pattern.
Introduced a new tff.templates.MeasuredProcess concept, a specialization
of tff.templates.IterativeProcess.
Extends tff.learning interfaces to accept tff.templates.MeasuredProcess
objects for aggregation and broadcast computations.
Introduce new convenience method tff.learning.weights_type_from_model.
Introduced the concept of a tff.framework.FederatingStrategy, which
parameterizes the tff.framework.FederatingExecutor so that the
implementation of a specific intrinsic is easier to provide.
Reduced duplication in TFF’s generated ASTs.
Enabled usage of GPUs on remote workers.
Documentation improvements.

The IterativeProcess return from
tff.learning.build_federated_averaging_process and
tff.learning.build_federated_sgd_process now zip the second tuple output
(the metrics) to change the result from a structure of federated values to
to a federated structure of values.
Removed tff.framework.set_default_executor function, instead you should
use the more convenient tff.backends.native.set_local_execution_context
function or manually construct a context an set it using
tff.framework.set_default_context.
The tff.Computation base class now contains an abstract __hash__ method,
to ensure compilation results can be cached. Any custom implementations of
this interface should be updated accordingly.

Fixed issue for missing variable initialization for variables explicitly not
added to any collections.
Fixed issue where table initializers were not run if the
tff.tf_computation decorated function used no variables.

This release contains contributions from many people at Google, as well as:

jvmcns@