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. 🤯

- 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]

(2^256-1) else (x1 & (2^256-1)) - 2^256 package edwards25519 import "math/bits" type fiatScalarUint1 uint64 // We use uint64 instead of a more narrow type for performance reasons; see https://github.com/mit-plv/fiat-crypto/pull/1006#issuecomment-892625927 type fiatScalarInt1 int64 // We use uint64 instead of a more narrow type for performance reasons; see https://github.com/mit-plv/fiat-crypto/pull/1006#issuecomment-892625927 // The type fiatScalarMontgomery is a field element in the Montgomery domain....