public class DockerElasticsearchDistributionType implements ElasticsearchDistributionType {

    DockerElasticsearchDistributionType() {}

    @Override
    public String getName() {
        return "docker";
    }

    @Override
    public boolean isDocker() {
        return true;
    }

* **보안 - HTTPS**
* **서버 시작 시 실행**
* ***재시작***
* 복제(실행 중인 프로세스 수)
* **메모리**
* **시작하기 전의 이전 단계**

## 컨테이너와 도커 { #containers-and-docker }

다음 장인 [컨테이너에서의 FastAPI - 도커](docker.md)에서는 다른 **배포 개념들**을 처리하기 위해 사용할 수 있는 몇 가지 전략을 설명하겠습니다.

단일 Uvicorn 프로세스를 실행하기 위해, **처음부터 여러분만의 이미지를 직접 빌드**하는 방법을 보여드리겠습니다. 이는 간단한 과정이며, **Kubernetes** 같은 분산 컨테이너 관리 시스템을 사용할 때 아마도 이렇게 하고 싶을 것입니다.

        assertEquals(ChatIntent.SEARCH, result.getIntent());
        assertEquals("singleKeyword", result.getQuery());
    }

    @Test
    public void test_search_withFessQuery() {
        final String query = "+Fess +Docker (tutorial OR guide)";
        final IntentDetectionResult result = IntentDetectionResult.search(query, "Fess query");

        assertEquals(ChatIntent.SEARCH, result.getIntent());
        assertEquals(query, result.getQuery());

	lm.mu.Lock()
	defer lm.mu.Unlock()

	lm.ref = 0
	lm.isWriteLock = false
}

// DRLocker returns a sync.Locker interface that implements
// the Lock and Unlock methods by calling drw.RLock and drw.RUnlock.
func (lm *LRWMutex) DRLocker() sync.Locker {
	return (*drlocker)(lm)
}

type drlocker LRWMutex

func (dr *drlocker) Lock()   { (*LRWMutex)(dr).RLock() }

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML

							endpoints[i].Hostname()), endpoints[i].Hostname(), logger.ErrorKind)
					}

					continue
				}

				// return err if not Docker or Kubernetes
				// We use IsDocker() to check for Docker environment
				// We use IsKubernetes() to check for Kubernetes environment
				isLocal, err := isLocalHost(endpoints[i].Hostname(),
					endpoints[i].Port(),
					globalMinioPort,
				)

TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cpu
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=devel ci/official/containers/ml_build"
TFCI_INDEX_HTML_ENABLE=1

# Bucket Lifecycle Configuration Quickstart Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

Enable object lifecycle configuration on buckets to setup automatic deletion of objects after a specified number of days or a specified date.

## 1. Prerequisites

# Distributed MinIO Quickstart Guide [![Slack](https://slack.min.io/slack?type=svg)](https://slack.min.io) [![Docker Pulls](https://img.shields.io/docker/pulls/minio/minio.svg?maxAge=604800)](https://hub.docker.com/r/minio/minio/)

etcd is a distributed key value store that provides a reliable way to store data across a cluster of machines.

## Get started

### 1. Prerequisites

- Docker 18.03 or above, refer here for [installation](https://docs.docker.com/install/).

### 2. Start etcd

etcd uses [gcr.io/etcd-development/etcd](https://console.cloud.google.com/gcr/images/etcd-development/GLOBAL/etcd) as a primary container registry.

```