sPgMCEAY7Cg04Uk48LAsDhRA8MVQPEF0GAgqYYwSRlycNcWskCkApIyEAOwAAAAAAAAAAAA==);background-position:right 5px center;background-repeat:no-repeat;background-color:#FFF}div.form-error{background-color:#f2dede;padding:15px;margin-bottom:20px;border:1px solid #b94a48;border-radius:4px}div.form-error strong{font-weight:700;display:block;margin:0;padding:0 0 10px}div.form-error strong,div.form-error ul li{line-height:140%;color:#b94a48;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px}div.form-error...

Und gibt einen Header `WWW-Authenticate` mit dem Wert `Basic` und einem optionalen `realm`-Parameter („Bereich“) zurück.

Dadurch wird der Browser angewiesen, die integrierte Eingabeaufforderung für einen Benutzernamen und ein Passwort anzuzeigen.

Wenn Sie dann den Benutzernamen und das Passwort eingeben, sendet der Browser diese automatisch im Header.

## Einfaches HTTP Basic Auth

* Importieren Sie `HTTPBasic` und `HTTPBasicCredentials`.

# MinIO Deployment 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/)

{% for sponsor in sponsors.gold -%}
<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
{% endfor -%}
{%- for sponsor in sponsors.silver -%}
<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
{% endfor %}
{% endif %}

<!-- /sponsors -->

{% for sponsor in sponsors.gold -%}
<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
{% endfor -%}
{%- for sponsor in sponsors.silver -%}
<a href="{{ sponsor.url }}" target="_blank" title="{{ sponsor.title }}"><img src="{{ sponsor.img }}" style="border-radius:15px"></a>
{% endfor %}
{% endif %}

<!-- /sponsors -->

ics-vidhya/deploy-machine-learning-models-with-keras-fastapi-redis-and-docker-4940df614ece title: Deploy Machine Learning Models with Keras, FastAPI, Redis and Docker - author: Mandy Gu author_link: https://towardsdatascience.com/@mandygu link: https://towardsdatascience.com/deploying-iris-classifications-with-fastapi-and-docker-7c9b83fdec3a title: 'Towards Data Science: Deploying Iris Classifications with FastAPI and Docker' - author: Michael Herman author_link: https://testdriven.io/authors/herman...

- Additional memory overhead per container in docker v1.11 ([#21737](https://github.com/docker/docker/issues/21737))
- [List of upstream fixes](https://github.com/docker/docker/compare/v1.10.3...runcom:docker-1.10.3-stable) for docker v1.10 identified by RedHat

#### Rkt runtime Known Issues

- A detailed list of known issues can be found [here](https://github.com/kubernetes/kubernetes.github.io/blob/release-1.3/docs/getting-started-guides/rkt/notes.md)

### Action required

  * Docker v1.9.1 is officially recommended. Docker v1.8.3 and Docker v1.10 are
supported. If you are using an older release of Docker, please upgrade. Known
issues with Docker 1.9.1 can be found below.
  * CPU hardcapping will be enabled by default for containers with CPU limit set,

Als Nächstes überprüft der Browser, ob die Response gültig und mit dem richtigen kryptografischen Schlüssel usw. verschlüsselt ist. Anschließend **entschlüsselt er die Response** und verarbeitet sie.

<img src="/img/deployment/https/https07.drawio.svg">

    $ cd fess-14.17.x
    $ ./bin/fess

詳細については、[インストールガイド](https://fess.codelibs.org/14.17/install/index.html)を参照してください。

### Docker

[ghcr.io](https://github.com/orgs/codelibs/packages)にDockerイメージを提供しています。また、このリポジトリにはDocker Compose（YAML）ファイルも用意されています。[こちらのリポジトリ](https://github.com/codelibs/docker-fess/tree/master/compose)で確認できます。

### ブラウザUI

- 検索UI: http://localhost:8080/