### Install requirements

Create a virtual environment and install the required packages with [`uv`](https://github.com/astral-sh/uv):

<div class="termy">

```console
$ uv sync --extra all

---> 100%
```

</div>

It will install all the dependencies and your local FastAPI in your local environment.

### Using your local FastAPI

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

3. Rebase your changes

You have already seen how to test your **FastAPI** applications using the provided `TestClient`. Up to now, you have only seen how to write synchronous tests, without using `async` functions.

  `performance` CPU governor.
* Vary the problem input size with `@Param`.
* Use the integrated profilers in JMH to dig deeper if benchmark results to not match your hypotheses:
    * Add `-prof gc` to the options to check whether the garbage collector runs during a microbenchmarks and skews

# OpenAPI Webhooks { #openapi-webhooks }

There are cases where you want to tell your API **users** that your app could call *their* app (sending a request) with some data, normally to **notify** of some type of **event**.

This means that instead of the normal process of your users sending requests to your API, it's **your API** (or your app) that could **send requests to their system** (to their API, their app).

This is normally called a **webhook**.

### Cache the OpenAPI schema { #cache-the-openapi-schema }

You can use the property `.openapi_schema` as a "cache", to store your generated schema.

That way, your application won't have to generate the schema every time a user opens your API docs.

It will be generated only once, and then the same cached schema will be used for the next requests.

But then, even though you still don't have your burgers, your work with the cashier is "on pause" ⏸, because you have to wait 🕙 for your burgers to be ready.

But as you go away from the counter and sit at the table with a number for your turn, you can switch 🔀 your attention to your crush, and "work" ⏯ 🤓 on that. Then you are again doing something very "productive" as is flirting with your crush 😍.

```
(You can paste links or attach files by dragging & dropping them below)
- Provide links to your updated versions of the above two colab notebooks.
- Provide links to your TensorFlow model and (optionally) TensorFlow Lite Model.
```

```

### Build the Docker Image { #build-the-docker-image }

Now that all the files are in place, let's build the container image.

* Go to the project directory (in where your `Dockerfile` is, containing your `app` directory).
* Build your FastAPI image:

<div class="termy">

```console
$ docker build -t myimage .

---> 100%
```

</div>

/// tip