* <abbr title="Parallel Server Gateway Interface">PSGI</abbr>

### The abbr gives an explanation { #the-abbr-gives-an-explanation }

* <abbr title="A group of machines that are configured to be connected and work together in some way.">cluster</abbr>
* <abbr title="A method of machine learning that uses artificial neural networks with numerous hidden layers between input and output layers, thereby developing a comprehensive internal structure">Deep Learning</abbr>

// CONTROL EQUIPMENT IN HAZARDOUS ENVIRONMENTS REQUIRING FAIL-SAFE
// PERFORMANCE, SUCH AS IN THE OPERATION OF NUCLEAR FACILITIES, AIRCRAFT
// NAVIGATION OR COMMUNICATION SYSTEMS, AIR TRAFFIC CONTROL, DIRECT LIFE
// SUPPORT MACHINES, OR WEAPONS SYSTEMS, IN WHICH THE FAILURE OF THE
// SOFTWARE COULD LEAD DIRECTLY TO DEATH, PERSONAL INJURY, OR SEVERE
// PHYSICAL OR ENVIRONMENTAL DAMAGE ("HIGH RISK ACTIVITIES").  WIDGET WORKSHOP

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

describes the peculiarities that apply when writing assembly code to interact with Go.
</p>

<p>
The most important thing to know about Go's assembler is that it is not a direct representation of the underlying machine.
Some of the details map precisely to the machine, but some do not.
This is because the compiler suite (see
<a href="https://9p.io/sys/doc/compiler.html">this description</a>)
needs no assembler pass in the usual pipeline.

    - [Deprecated metrics](#deprecated-metrics)
  - [Notable Features](#notable-features)
  - [API Changes](#api-changes)
  - [Detailed Bug Fixes And Changes](#detailed-bug-fixes-and-changes)
    - [API Machinery](#api-machinery)
    - [Apps](#apps)
    - [Auth](#auth)
    - [AWS](#aws)
    - [Azure](#azure)
    - [CLI](#cli)
    - [Cloud Provider](#cloud-provider)
    - [Cluster Lifecycle](#cluster-lifecycle)
    - [GCP](#gcp)

컨테이너를 사용하지 않고서는, 어플리케이션을 구동하고 재시작하는 것이 매우 번거롭고 어려울 수 있습니다. 하지만 **컨테이너를 사용한다면** 대부분의 경우에 이런 기능은 기본적으로 포함되어 있습니다. ✨

## 복제 - 프로세스 개수

만약 여러분이 **쿠버네티스**와 머신 <abbr title="A group of machines that are configured to be connected and work together in some way.">클러스터</abbr>, 도커 스왐 모드, 노마드, 또는 다른 여러 머신 위에 분산 컨테이너를 관리하는 복잡한 시스템을 다루고 있다면, 여러분은 각 컨테이너에서 (워커와 함께 사용하는 Gunicorn 같은) **프로세스 매니저** 대신 **클러스터 레벨**에서 **복제를 다루**고 싶을 것입니다.

- Regenerate protobuf code to fix CVE-2021-3121 ([#100514](https://github.com/kubernetes/kubernetes/pull/100514), [@joelsmith](https://github.com/joelsmith)) [SIG API Machinery, Auth, CLI, Cloud Provider, Cluster Lifecycle, Instrumentation and Node]

### Feature

- Updated base image for Windows pause container images to one built on Windows machines to address limitations of building Windows container images on Linux machines. ([#110379](https://github.com/kubernetes/kubernetes/pull/110379), [@marosset](https://github.com/marosset))

* Use local disk for ConfigMap volume instead of tmpfs ([#25306](https://github.com/kubernetes/kubernetes/pull/25306), [@pmorie](https://github.com/pmorie))
* Alpha support for scheduling pods on machines with NVIDIA GPUs whose kubelets use the `--experimental-nvidia-gpus` flag, using the alpha.kubernetes.io/nvidia-gpu resource  ([#24836](https://github.com/kubernetes/kubernetes/pull/24836), [@therc](https://github.com/therc))

// watches : Identity Digital Limited
// https://www.iana.org/domains/root/db/watches.html
watches

// weather : International Business Machines Corporation
// https://www.iana.org/domains/root/db/weather.html
weather

// weatherchannel : International Business Machines Corporation
// https://www.iana.org/domains/root/db/weatherchannel.html
weatherchannel

// webcam : dot Webcam Limited