replicas taking part in sync training.
    *   Performance improvements for GPU multi-worker distributed training using
        `tf.distribute.experimental.MultiWorkerMirroredStrategy`
    *   Update NVIDIA `NCCL` to `2.5.7-1` for better performance and performance
        tuning. Please see

* **Safer** types.
* Better **performance** and **less energy** consumption.
* Better **extensibility**.
* etc.

...all this while keeping the **same Python API**. In most of the cases, for simple models, you can simply upgrade the Pydantic version and get all the benefits. 🚀

In some cases, for pure data validation and processing, you can get performance improvements of **20x** or more. This means 2,000% or more. 🤯

SetBytes checks that (x, y) are reduced modulo p, and that they are on // the curve, performing Steps 2-3 of Section 5.6.2.3.4. p, err := c.newPoint().SetBytes(peer.q) if err != nil { return nil, err } // Compute P according to Section 5.7.1.2. if _, err := p.ScalarMult(p, k.d); err != nil { return nil, err } // BytesX checks that the result is not the identity element, and returns the // x-coordinate of the result, performing Steps 2-5 of Section 5.7.1.2. return p.BytesX() } // isZero reports whether x...

- Kubelet: improve the performance when waiting for a synchronization of the node list with the kube-apiserver ([#99336](https://github.com/kubernetes/kubernetes/pull/99336), [@neolit123](https://github.com/neolit123)) [SIG Node]