Tensorflow-federated

Major Features and Improvements

* Mirrored user-provided types and minimize usage of `AnonymousTuple`.

Breaking Changes

* Renamed `AnonymousTuple` to `Struct`.

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.

Breaking Changes

* 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.

Bug Fixes

* 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.

Thanks to our Contributors

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

jvmcns

Major Features and Improvements

* Multiple TFF execution speedups.
* New `tff.templates.MeasuredProcess` specialization of `IterativeProcess`.
* Increased optimization of the `tff.templates.IterativeProcess` ->
`tff.backends.mapreduce.CanonicalForm` compiler.

Breaking Changes

* Moved `tff.utils.IterativeProcess` to `tff.templates.IterativeProcess`.
* Removed `tff.learning.TrainableModel`, client optimizers are now arguments
on the `tff.learning.build_federated_averaging_process`.
* Bump required version of pip packages for tensorflow (2.2), numpy (1.18),
pandas (0.24), grpcio (1.29).

Bug Fixes

* Issue with GPUs in multimachine simulations not being utilized, and bug on
deserializing datasets with GPU-backed runtime.
* TensorFlow lookup table initialization failures.

Known Bugs

* In some situations, TF will attempt to push Datasets inside of tf.functions
over GPU device boundaries, which fails by default. This can be hit by
certain usages of TFF,
[e.g. as tracked here](https://github.com/tensorflow/federated/issues/832).

Thanks to our Contributors

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

jvmcns

Bug Fixes

* Fixed issues in tutorial notebooks.

Major Features and Improvements

* Updated `absl-py` package dependency to `0.9.0`.
* Updated `h5py` package dependency to `2.8.0`.
* Updated `numpy` package dependency to `1.17.5`.
* Updated `tensorflow-privacy` package dependency to `0.2.2`.

Breaking Changes

* Deprecated `dummy_batch` parameter of the `tff.learning.from_keras_model`
function.

Bug Fixes

* Fixed issues with executor service using old executor API.
* Fixed issues with remote executor test using old executor API.
* Fixed issues in tutorial notebooks.

Major Features and Improvements

* Upgraded tensorflow dependency from `2.0.0` to `2.1.0`.
* Upgraded tensorflow-addons dependency from `0.6.0` to `0.7.0`.
* Upgraded attr dependency from `18.2` to `19.3`.
* Upgraded tfmot dependency from `0.1.3` to `0.2.1`.
* Added a federated partition of the CIFAR-100 dataset to
`tff.simulation.datasets.cifar100`.
* Made the high performance, parallel executor the default (replacing the
reference executor).
* Added a new `tff.learning.build_personalization_eval` for evaluating model
personalization strategies.
* Added new federated intrinsic `tff.federated_secure_sum`.
* `tff.learning.build_federated_averaing_process()` now takes a
`client_optimizer_fn` and a `tff.learning.Model`.
`tff.learning.TrainableModel` is now deprecated.
* Improved performance in the high performance executor stack.
* Implemented and exposed `tff.framework.ExecutorFactory`; all
`tff.framework...executor_factory` calls now return an instance of this
class.
* Added `remote_executor_example` binary which demonstrates using the
RemoteExecutor across multi-machine deployments.
* Added `close()` method to the Executor, allowing subclasses to proactively
release resources.
* Updated documentation and scripts for creating Docker images of the TFF
runtime.
* Automatically call `tff.federated_zip` on inputs to other federated
intrinsics.

Breaking Changes

* Dropped support for Python2.
* Renamed `tff.framework.create_local_executor` (and similar methods) to
`tff.framework.local_executor_factory`.
* Deprecated `federated_apply()`, instead use `federated_map()` for all
placements.

Bug Fixes

* Fixed problem with different instances of the same model having different
named types. `tff.learning.ModelWeights` no longer names the tuple fields
returned for model weights, instead relying on an ordered list.
* `tff.sequence_*` on unplaced types now correctly returns a `tff.Value`.

Known Bugs

* `tff.sequence_*`.. operations are not implemented yet on the new
high-performance executor stack.
* A subset of previously-allowed lambda captures are no longer supported on
the new execution stack.