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- Understand the Project object
- Update the Build script
- Update the Plugin
- Apply the Plugin
- Run a task from the Plugin
****

[[part5_begin]]
== Step 0. Before you Begin

1. You initialized your Java app in <<partr1_gradle_init.adoc#part1_begin,part 1>>.
2. You understand the Gradle Build Lifecycle from <<partr2_build_lifecycle.adoc#part2_begin,part 2>>.

Having a proxy with a stripped path prefix, in this case, means that you could declare a path at `/app` in your code, but then, you add a layer on top (the proxy) that would put your **FastAPI** application under a path like `/api/v1`.

In this case, the original path `/app` would actually be served at `/api/v1/app`.

Even though all your code is written assuming there's just `/app`.

```Python hl_lines="6"

### 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**.

If you have improvements to TensorFlow, send us your pull requests! For those
just getting started, GitHub has a
[how-to](https://help.github.com/articles/using-pull-requests/).

TensorFlow team members will be assigned to review your pull requests. Once the
pull requests are approved and pass continuous integration checks, a TensorFlow
team member will apply `ready to pull` label to your change. This means we are

"_If anyone is looking to build a production Python API, I would highly recommend **FastAPI**. It is **beautifully designed**, **simple to use** and **highly scalable**, it has become a **key component** in our API first development strategy and is driving many automations and services such as our Virtual TAC Engineer._"

include::sample[dir="snippets/workerApi/md5CustomTask/groovy",files="buildSrc/build.gradle[]"]
====
<1> Your custom task class will use https://commons.apache.org/proper/commons-codec/[Apache Commons Codec] to generate MD5 hashes.

Next, create a custom task class in your `buildSrc/src/main/java` directory.
You should name this class `CreateMD5`:

.buildSrc/src/main/java/CreateMD5.java
[source,java]
----

* A `limit` query parameter that is an `int`, with a default of `100`.

In both cases the data will be converted, validated, documented on the OpenAPI schema, etc.

## Use it

Now you can declare your dependency using this class.

=== "Python 3.10+"

    ```Python hl_lines="19"
    {!> ../../../docs_src/dependencies/tutorial002_an_py310.py!}
    ```

=== "Python 3.9+"

	// redetect for each pod anyway.
	//
	// Extreme corner case:
	// If for some reason your host had both binaries, and you were injecting out-of-band
	// iptables rules within a pod context into `legacy` tables, but your host context preferred
	// `nft`, we would still inject our rules in-pod into nft tables, which is a bit wonky.
	//
	// But that's stunningly unlikely (and would still work either way)

---

"_Nous avons adopté la bibliothèque **FastAPI** pour créer un serveur **REST** qui peut être interrogé pour obtenir des **prédictions**. [pour Ludwig]_"