- 🤖 一个自动化生成的前端客户端。
    - 🧪 [Playwright](https://playwright.dev)用于端到端测试。
    - 🦇 支持暗黑主题（Dark mode）。
- 🐋 [Docker Compose](https://www.docker.com) 用于开发环境和生产环境。
- 🔒 默认使用密码哈希来保证安全。
- 🔑 JWT令牌用于权限验证。
- 📫 使用邮箱来进行密码恢复。
- ✅ 单元测试用了[Pytest](https://pytest.org).
- 📞 [Traefik](https://traefik.io) 用于反向代理和负载均衡。
- 🚢 部署指南（Docker Compose）包含了如何起一个Traefik前端代理来自动化HTTPS认证。

# To run locally an OpenLDAP instance using Docker
# $ docker-compose -f ldap.yaml up -d
version: '3.7'

services:
  openldap:
    image: quay.io/minio/openldap
    ports:
      - "389:389"
      - "636:636"
    environment:
      LDAP_ORGANIZATION: "MinIO Inc"
      LDAP_DOMAIN: "min.io"

	internalLogIf(GlobalContext, err)

	return err == nil
}

// IsDCOS returns true if minio is running in DCOS.
func IsDCOS() bool {
	// http://mesos.apache.org/documentation/latest/docker-containerizer/
	// Mesos docker containerizer sets this value
	return env.Get("MESOS_CONTAINER_NAME", "") != ""
}

// IsKubernetes returns true if minio is running in kubernetes.
func IsKubernetes() bool {

# ==============================================================================

# Temporary envs file while we migrate to the new docker image.
# Once the migration is complete, this file will be removed.
source ci/official/envs/linux_x86
TFCI_DOCKER_IMAGE=us-central1-docker.pkg.dev/tensorflow-sigs/tensorflow/ml-build:latest

/// tip | "Tipp"

Machen Sie sich keine Sorgen, wenn einige dieser Punkte zu **Containern**, Docker oder Kubernetes noch nicht viel Sinn ergeben.

Ich werde Ihnen in einem zukünftigen Kapitel mehr über Container-Images, Docker, Kubernetes, usw. erzählen: [FastAPI in Containern – Docker](docker.md){.internal-link target=_blank}.

///

## Schritte vor dem Start

/// tip | "Dica"

Não se preocupe se alguns desses itens sobre **contêineres**, Docker ou Kubernetes ainda não fizerem muito sentido.

Falarei mais sobre imagens de contêiner, Docker, Kubernetes, etc. em um capítulo futuro: [FastAPI em contêineres - Docker](docker.md){.internal-link target=_blank}.

///

## Etapas anteriores antes de começar

        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash

COPY --from=build /go/bin/mc* /usr/bin/
COPY --from=build /go/bin/curl* /usr/bin/

COPY CREDITS /licenses/CREDITS
COPY LICENSE /licenses/LICENSE
COPY dockerscripts/docker-entrypoint.sh /usr/bin/docker-entrypoint.sh

EXPOSE 9000
VOLUME ["/data"]

ENTRYPOINT ["/usr/bin/docker-entrypoint.sh"]

COPY --from=build /go/bin/mc* /usr/bin/
COPY --from=build /go/bin/cur* /usr/bin/

COPY CREDITS /licenses/CREDITS
COPY LICENSE /licenses/LICENSE
COPY dockerscripts/docker-entrypoint.sh /usr/bin/docker-entrypoint.sh

EXPOSE 9000
VOLUME ["/data"]

ENTRYPOINT ["/usr/bin/docker-entrypoint.sh"]

    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash
    # Install Docker first, then this will build and run cpu tests
    tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
    ```

    See