Commit 1f18efcd authored by Zachary Garrett's avatar Zachary Garrett Committed by tensorflow-copybara
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Create 0.9.0 release.

PiperOrigin-RevId: 273346886
parent e73f124d
......@@ -87,7 +87,8 @@ versions.
TensorFlow Federated | TensorFlow
------------------------------------------------------------ | ----------
[master](https://github.com/tensorflow/federated) | [tf-nightly 1.15.0.dev20190805](https://pypi.org/project/tf-nightly/1.15.0.dev20190805/)
[master](https://github.com/tensorflow/federated) | [tf-nightly 2.1.0.dev20191005](https://pypi.org/project/tf-nightly/2.1.0.dev20191005/)
[0.9.0](https://github.com/tensorflow/federated/tree/v0.9.0) | [tf-nightly 2.1.0.dev20191005](https://pypi.org/project/tf-nightly/2.1.0.dev20191005/)
[0.8.0](https://github.com/tensorflow/federated/tree/v0.8.0) | [tf-nightly 1.15.0.dev20190805](https://pypi.org/project/tf-nightly/1.15.0.dev20190805/)
[0.7.0](https://github.com/tensorflow/federated/tree/v0.7.0) | [tf-nightly 1.15.0.dev20190711](https://pypi.org/project/tf-nightly/1.15.0.dev20190711/)
[0.6.0](https://github.com/tensorflow/federated/tree/v0.6.0) | [tf-nightly 1.15.0.dev20190626](https://pypi.org/project/tf-nightly/1.15.0.dev20190626/)
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# Release 0.9.0
## Major Features and Improvements
* TFF is now fully compatible and dependent on TensorFlow 2.0
* Add stateful aggregation with differential privacy using TensorFlow Privacy
(https://pypi.org/project/tensorflow-privacy/).
* Additional stateful aggregation lwith compression using TensorFlow Model
Optimization (https://pypi.org/project/tensorflow-model-optimization/).
* Improved executor stack for simulations, documentation and scripts for
starting simulations on GCP.
* New libraries for creating synthetic IID and non-IID datsets in simulation.
## Breaking Changes
* `examples` package split to `simulation` and `research`.
## Bug Fixes
* Various error message string improvements.
* Dataset serialization fixed for V1/V2 datasets.
* `tff.federated_aggregate` supports `accumulate`, `merge` and `report`
methods with signatures containing tensors with undefined dimensions.
# Release 0.8.0
## Major Features and Improvements
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......@@ -133,4 +133,4 @@ the abstract class <a href="./tff/Value.md"><code>tff.Value</code></a>.
* `CLIENTS` <a id="CLIENTS"></a>
* `SERVER` <a id="SERVER"></a>
* `__version__ = '0.8.0'` <a id="__version__"></a>
* `__version__ = '0.9.0'` <a id="__version__"></a>
......@@ -27,14 +27,14 @@
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/federated/tutorials/custom_federated_algorithms_1"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.8.0/docs/tutorials/custom_federated_algorithms_1.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.9.0/docs/tutorials/custom_federated_algorithms_1.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
</td>
<td>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.8.0/docs/tutorials/custom_federated_algorithms_1.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.9.0/docs/tutorials/custom_federated_algorithms_1.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
</td>
</table>
%% Cell type:markdown id: tags:
......
......@@ -27,14 +27,14 @@
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/federated/tutorials/custom_federated_algorithms_2"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.8.0/docs/tutorials/custom_federated_algorithms_2.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.9.0/docs/tutorials/custom_federated_algorithms_2.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
</td>
<td>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.8.0/docs/tutorials/custom_federated_algorithms_2.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.9.0/docs/tutorials/custom_federated_algorithms_2.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
</td>
</table>
%% Cell type:markdown id: tags:
......
......@@ -27,20 +27,20 @@
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.8.0/docs/tutorials/federated_learning_for_image_classification.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.9.0/docs/tutorials/federated_learning_for_image_classification.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
</td>
<td>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.8.0/docs/tutorials/federated_learning_for_image_classification.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.9.0/docs/tutorials/federated_learning_for_image_classification.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
</td>
</table>
%% Cell type:markdown id: tags:
**NOTE**: This colab has been verified to work with the `0.7.0` version of the `tensorflow_federated` pip package, but the Tensorflow Federated project is still in pre-release development and may not work on `master`.
**NOTE**: This colab has been verified to work with the [latest released version](https://github.com/tensorflow/federated#compatibility) of the `tensorflow_federated` pip package, but the Tensorflow Federated project is still in pre-release development and may not work on `master`.
In this tutorial, we use the classic MNIST training example to introduce the
Federated Learning (FL) API layer of TFF, `tff.learning` - a set of
higher-level interfaces that can be used to perform common types of federated
learning tasks, such as federated training, against user-supplied models
......
......@@ -27,20 +27,20 @@
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/federated/tutorials/federated_learning_for_text_generation"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.8.0/docs/tutorials/federated_learning_for_text_generation.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
<a target="_blank" href="https://colab.research.google.com/github/tensorflow/federated/blob/v0.9.0/docs/tutorials/federated_learning_for_text_generation.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
</td>
<td>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.8.0/docs/tutorials/federated_learning_for_text_generation.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
<a target="_blank" href="https://github.com/tensorflow/federated/blob/v0.9.0/docs/tutorials/federated_learning_for_text_generation.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
</td>
</table>
%% Cell type:markdown id: tags:
**NOTE**: This colab has been verified to work with the `0.8.0` version of the `tensorflow_federated` pip package, but the Tensorflow Federated project is still in pre-release development and may not work on `master`.
**NOTE**: This colab has been verified to work with the [latest released version](https://github.com/tensorflow/federated#compatibility) of the `tensorflow_federated` pip package, but the Tensorflow Federated project is still in pre-release development and may not work on `master`.
This tutorial builds on the concepts in the [Federated Learning for Image Classification](federated_learning_for_image_classification.md) tutorial, and demonstrates several other useful approaches for federated learning.
In particular, we load a previously trained Keras model, and refine it using federated training on a (simulated) decentralized dataset. This is practically important for several reasons . The ability to use serialized models makes it easy to mix federated learning with other ML approaches. Further, this allows use of an increasing range of pre-trained models --- for example, training language models from scratch is rarely necessary, as numerous pre-trained models are now widely available (see, e.g., [TF Hub](https://www.tensorflow.org/hub)). Instead, it makes more sense to start from a pre-trained model, and refine it using Federated Learning, adapting to the particular characteristics of the decentralized data for a particular application.
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......@@ -18,4 +18,4 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__version__ = '0.8.0'
__version__ = '0.9.0'
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