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TensorFlower Gardener 2019-02-25 14:39:53 -08:00
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@ -55,24 +55,28 @@ TensorFlow coding style.
#### General guidelines and philosophy for contribution #### General guidelines and philosophy for contribution
* Include unit tests when you contribute new features, as they help to * Include unit tests when you contribute new features, as they help to a)
a) prove that your code works correctly, and b) guard against future breaking prove that your code works correctly, and b) guard against future breaking
changes to lower the maintenance cost. changes to lower the maintenance cost.
* Bug fixes also generally require unit tests, because the presence of bugs * Bug fixes also generally require unit tests, because the presence of bugs
usually indicates insufficient test coverage. usually indicates insufficient test coverage.
* Keep API compatibility in mind when you change code in core TensorFlow, * Keep API compatibility in mind when you change code in core TensorFlow,
e.g., code in [tensorflow/core](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core) and [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python). e.g., code in
TensorFlow has reached version 1 and hence cannot make [tensorflow/core](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core)
non-backward-compatible API changes without a major release. Reviewers of your and
pull request will comment on any API compatibility issues. [tensorflow/python](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/python).
* When you contribute a new feature to TensorFlow, the maintenance burden is (by TensorFlow has reached version 1 and hence cannot make
default) transferred to the TensorFlow team. This means that benefit of the non-backward-compatible API changes without a major release. Reviewers of
contribution must be compared against the cost of maintaining the feature. your pull request will comment on any API compatibility issues.
* Full new features (e.g., a new op implementing a cutting-edge algorithm) * When you contribute a new feature to TensorFlow, the maintenance burden is
typically will live in (by default) transferred to the TensorFlow team. This means that benefit of
[tensorflow/contrib](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib) the contribution must be compared against the cost of maintaining the
to get some airtime before decision is made regarding whether they are to be feature.
migrated to the core. * Full new features (e.g., a new op implementing a cutting-edge algorithm)
typically will live in
[tensorflow/addons](https://github.com/tensorflow/addons) to get some
airtime before decision is made regarding whether they are to be migrated to
the core.
#### License #### License