- User can now use component config to configure NodeLabel plugin for the scheduler framework. ([#84297](https://github.com/kubernetes/kubernetes/pull/84297), [@liu-cong](https://github.com/liu-cong))
- Optimize inter-pod affinity preferredDuringSchedulingIgnoredDuringExecution type, up to 4x in some cases. ([#84264](https://github.com/kubernetes/kubernetes/pull/84264), [@ahg-g](https://github.com/ahg-g))

- github.com/go-kit/log: [v0.1.0](https://github.com/go-kit/log/tree/v0.1.0)
- github.com/gofrs/uuid: [v4.0.0+incompatible](https://github.com/gofrs/uuid/tree/v4.0.0)
- github.com/josharian/intern: [v1.0.0](https://github.com/josharian/intern/tree/v1.0.0)
- github.com/jpillora/backoff: [v1.0.0](https://github.com/jpillora/backoff/tree/v1.0.0)
- github.com/nxadm/tail: [v1.4.4](https://github.com/nxadm/tail/tree/v1.4.4)

  2. when transitioning to terminal phase for Job, the number of terminating pods is 0
  3. terminal Job conditions (Failed and Complete) should be preceded by adding the corresponding interim conditions: FailureTarget and SuccessCriteriaMet ([#125510](https://github.com/kubernetes/kubernetes/pull/125510), [@mimowo](https://github.com/mimowo)) [SIG Apps and Testing]

    provides class `ndarray`, which mimics the `ndarray` class in NumPy, and
    wraps an immutable `tf.Tensor` under the hood. A subset of NumPy functions
    (e.g. `numpy.add`) are provided. Their inter-operation with TF facilities is
    seamless in most cases. See
    [tensorflow/python/ops/numpy_ops/README.md](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/numpy_ops/README.md)

- Add improved load balancer naming.

### SIG-scheduling

SIG Scheduling development efforts have been primarily focused on improving performance and reliability of the scheduler.
- Performance of the inter-pod affinity/anti-affinity feature is improved over 100X via algorithmic optimization.

SIG Windows is also including several addition to this release:
 - Direct Server Return (DSR) mode support, allowing large numbers of services to scale up efficiently

OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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github.com/josharian/intern
https://github.com/josharian/intern
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MIT License

Copyright (c) 2019 Josh Bleecher Snyder