Voici un petit bout de cette histoire.

## Alternatives

Je crée des API avec des exigences complexes depuis plusieurs années (Machine Learning, systèmes distribués, jobs asynchrones, bases de données NoSQL, etc), en dirigeant plusieurs équipes de développeurs.

Dans ce cadre, j'ai dû étudier, tester et utiliser de nombreuses alternatives.

L'histoire de **FastAPI** est en grande partie l'histoire de ses prédécesseurs.

Here's a little bit of that history.

## Alternatives { #alternatives }

I have been creating APIs with complex requirements for several years (Machine Learning, distributed systems, asynchronous jobs, NoSQL databases, etc), leading several teams of developers.

As part of that, I needed to investigate, test and use many alternatives.

The history of **FastAPI** is in great part the history of its predecessors.

**Describe the solution you'd like**
A clear and concise description of what you want to happen.

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**

        #### Utility: compare with alternatives


        There is always *some* alternative to adding a new feature to Guava, even if it's just
        forking Guava yourself.


        We want to see that new features have some significant advantage over the alternatives.
        These advantages can take

![Swagger UI](https://fastapi.tiangolo.com/img/index/index-01-swagger-ui-simple.png)

### Alternative API docs { #alternative-api-docs }

And now, go to <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.

You will see the alternative automatic documentation (provided by <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>):

      required: false
  - type: textarea
    id: context
    attributes:
      label: Context
      description: |
        How has this issue affected you? What are you trying to accomplish? What other alternatives have you considered?
        Providing context helps us come up with a solution that is most useful in the real world
    validations:

Wie im Abschnitt [Alternativen](alternatives.md){.internal-link target=_blank} gesagt:

<blockquote markdown="1">

**FastAPI** würde ohne die frühere Arbeit anderer nicht existieren.

Es wurden zuvor viele Tools entwickelt, die als Inspiration für seine Entwicklung dienten.

📥 🐥 🍖 👈 📖.

## 🎛

👤 ✔️ 🏗 🔗 ⏮️ 🏗 📄 📚 1️⃣2️⃣🗓️ (🎰 🏫, 📎 ⚙️, 🔁 👨‍🏭, ☁ 💽, ♒️), ↘️ 📚 🏉 👩‍💻.

🍕 👈, 👤 💪 🔬, 💯 & ⚙️ 📚 🎛.

📖 **FastAPI** 👑 🍕 📖 🚮 ⏪.

🙆‍♀ 📄 [🎛](alternatives.md){.internal-link target=_blank}:

<blockquote markdown="1">

**FastAPI** 🚫🔜 🔀 🚥 🚫 ⏮️ 👷 🎏.

📤 ✔️ 📚 🧰 ✍ ⏭ 👈 ✔️ ℹ 😮 🚮 🏗.

$ python main.py

I solemnly swear 🐺
```

</div>

## Alternatives { #alternatives }

This is a simple guide to get you started and teach you how everything works **underneath**.

There are many **alternatives** to managing virtual environments, package dependencies (requirements), projects.

   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.