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.

Aquí hay un poquito de esa historia.

## Alternativas { #alternatives }

He estado creando APIs con requisitos complejos durante varios años (Machine Learning, sistemas distribuidos, trabajos asíncronos, bases de datos NoSQL, etc.), liderando varios equipos de desarrolladores.

Como parte de eso, necesitaba investigar, probar y usar muchas alternativas.

Aqui está um pouco dessa história.

## Alternativas { #alternatives }

Eu tenho criado APIs com requisitos complexos por vários anos (Aprendizado de Máquina, sistemas distribuídos, tarefas assíncronas, banco de dados NoSQL etc.), liderando vários times de desenvolvedores.

Como parte disso, eu precisava investigar, testar e usar muitas alternativas.

Hier ist ein wenig über diese Geschichte.

## Alternativen { #alternatives }

Ich habe seit mehreren Jahren APIs mit komplexen Anforderungen (maschinelles Lernen, verteilte Systeme, asynchrone Jobs, NoSQL-Datenbanken, usw.) erstellt und leitete mehrere Entwicklerteams.

Dabei musste ich viele Alternativen untersuchen, testen und nutzen.

> Какова история этого проекта? Создаётся впечатление, что он явился из ниоткуда и завоевал мир за несколько недель [...]

Что ж, вот небольшая часть истории проекта.

## Альтернативы { #alternatives }

В течение нескольких лет я, возглавляя различные команды разработчиков, создавал довольно сложные API для машинного обучения, распределённых систем, асинхронных задач, баз данных NoSQL и т.д.

        #### 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

      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:

![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>):

- **Cross-Language Compatibility:**
Java serialization is inherently Java-centric and does not support cross-language scenarios well.

- **Type Safety:**
Java serialization does not enforce type safety as strictly as some alternatives, potentially leading to runtime errors.

## Decision

We do not use Java serialization.
Instead, we use custom serialization where we explicitly describe how data objects should be serialized and deserialized.

   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.