From c76a3a8809cbcf152167b47d29237e6410188736 Mon Sep 17 00:00:00 2001 From: michaelreneer Date: Tue, 19 Feb 2019 20:38:48 -0800 Subject: [PATCH] Increment the version and add release documentation. PiperOrigin-RevId: 234727703 --- RELEASE.md | 3 +++ tensorflow_federated/tools/setup.py | 28 ++++++++++++++++++++++------ 2 files changed, 25 insertions(+), 6 deletions(-) create mode 100644 RELEASE.md diff --git a/RELEASE.md b/RELEASE.md new file mode 100644 index 0000000000..dd9f4166df --- /dev/null +++ b/RELEASE.md @@ -0,0 +1,3 @@ +# Release 0.1.0 + +Initial public release. diff --git a/tensorflow_federated/tools/setup.py b/tensorflow_federated/tools/setup.py index 7910048606..c3f5c0de37 100644 --- a/tensorflow_federated/tools/setup.py +++ b/tensorflow_federated/tools/setup.py @@ -11,13 +11,30 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""TensorFlow Federated is an open source federated learning framework. +# pylint: disable=line-too-long +"""TensorFlow Federated is an open-source federated learning framework. -TensorFlow Federated is a framework for implementing federated learning -algorithms and other computations on privacy-sensitive decentralized data. +TensorFlow Federated (TFF) is an open-source framework for collaborative +computations on distributed data that does not require collecting data at a +centralized location. -TODO(b/112658581): Add TFF high level overview. +The framework has initially been developed to facilitate open research and +experimentation with +[Federated Learning](https://ai.googleblog.com/2017/04/federated-learning-collaborative.html), +a technology that enables devices owned by end users to collaboratively learn a +shared prediction model while keeping potentially sensitive training data on the +devices, thus decoupling the ability to do machine learning from the need to +collect and store the data in the cloud. + +With the interfaces provided by TFF, developers can test existing federated +learning algorithms on their models and data, or design new experimental +algorithms and run them on existing models and data, all within the same open +source environment. The framework has been designed with compositionality in +mind, and can be used to combine independently-developed techniques and +components that offer complementary capabilities into larger systems. """ +# pylint: enable=line-too-long +# TODO(b/124800187): Keep in sync with the contents of README. import sys @@ -25,8 +42,7 @@ DOCLINES = __doc__.split('\n') -# Set when releasing a new version of TensorFlow Federated (e.g. 1.0.0). -_VERSION = '0.0.0' +_VERSION = '0.1.0' project_name = 'tensorflow_federated'