📥 👤 🔜 🎦 👆 ❔ ⚙️ <a href="https://gunicorn.org/" class="external-link" target="_blank">**🐁**</a> ⏮️ **Uvicorn 👨‍🏭 🛠️**.

!!! info
    🚥 👆 ⚙️ 📦, 🖼 ⏮️ ☁ ⚖️ Kubernetes, 👤 🔜 💬 👆 🌅 🔃 👈 ⏭ 📃: [FastAPI 📦 - ☁](docker.md){.internal-link target=_blank}.

    🎯, 🕐❔ 🏃 🔛 **Kubernetes** 👆 🔜 🎲 **🚫** 💚 ⚙️ 🐁 &amp; ↩️ 🏃 **👁 Uvicorn 🛠️ 📍 📦**, ✋️ 👤 🔜 💬 👆 🔃 ⚫️ ⏪ 👈 📃.

## 🐁 ⏮️ Uvicorn 👨‍🏭

## Container Images

Docker has been one of the main tools to create and manage **container images** and **containers**.

And there's a public <a href="https://hub.docker.com/" class="external-link" target="_blank">Docker Hub</a> with pre-made **official container images** for many tools, environments, databases, and applications.

Und es gibt viele andere Images für verschiedene Dinge wie Datenbanken, zum Beispiel für:

* <a href="https://hub.docker.com/_/postgres" class="external-link" target="_blank">PostgreSQL</a>
* <a href="https://hub.docker.com/_/mysql" class="external-link" target="_blank">MySQL</a>

Por exemplo, há uma <a href="https://hub.docker.com/_/python" class="external-link" target="_blank">Imagem Python</a> oficial.

E existe muitas outras imagens para diferentes coisas, como bancos de dados, por exemplo:

* <a href="https://hub.docker.com/_/postgres" class="external-link" target="_blank">PostgreSQL</a>
* <a href="https://hub.docker.com/_/mysql" class="external-link" target="_blank">MySQL</a>

```console
$ pytest

================ test session starts ================
platform linux -- Python 3.6.9, pytest-5.3.5, py-1.8.1, pluggy-0.13.1
rootdir: /home/user/code/superawesome-cli/app
plugins: forked-1.1.3, xdist-1.31.0, cov-2.8.1
collected 6 items

---> 100%

test_main.py <span style="color: green; white-space: pre;">......                            [100%]</span>

    - deployment/versions.md
    - deployment/https.md
    - deployment/manually.md
    - deployment/concepts.md
    - deployment/cloud.md
    - deployment/server-workers.md
    - deployment/docker.md
  - How To - Recipes:
    - how-to/index.md
    - how-to/general.md
    - how-to/graphql.md
    - how-to/custom-request-and-route.md
    - how-to/conditional-openapi.md
    - how-to/extending-openapi.md

### Full Stack FastAPI PostgreSQL - Özellikler

* Full **Docker** entegrasyonu (Docker based).
* Docker Swarm Mode ile deployment.
* **Docker Compose** entegrasyonu ve lokal geliştirme için optimizasyon.
* Uvicorn ve Gunicorn ile **Production ready** Python web server'ı.

### Full Stack FastAPI PostgreSQL - Особенности

* Полностью интегрирован с **Docker** (основан на Docker).
* Развёртывается в режиме Docker Swarm.
* Интегрирован с **Docker Compose** и оптимизирован для локальной разработки.
* **Готовый к реальной работе** веб-сервер Python использующий Uvicorn и Gunicorn.

    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

### フルスタック FastAPI PostgreSQL - 機能

* 完全な**Docker**インテグレーション (Dockerベース)。
* Docker Swarm モードデプロイ。
* ローカル開発環境向けの**Docker Compose**インテグレーションと最適化。
* UvicornとGunicornを使用した**リリース可能な** Python web サーバ。
* Python <a href="https://github.com/tiangolo/fastapi" class="external-link" target="_blank">**FastAPI**</a> バックエンド: