# Debugging { #debugging }

You can connect the debugger in your editor, for example with Visual Studio Code or PyCharm.

## Call `uvicorn` { #call-uvicorn }

In your FastAPI application, import and run `uvicorn` directly:

{* ../../docs_src/debugging/tutorial001.py hl[1,15] *}

### About `__name__ == "__main__"` { #about-name-main }

The main purpose of the `__name__ == "__main__"` is to have some code that is executed when your file is called with:

$ echo "no" | avdmanager --verbose create avd --force --name "pixel5" --device "pixel" --package "system-images;android-29;google_apis;x86" --tag "google_apis" --abi "x86"
```

2. Run an Emulator using Android Studio or from command line.

```
$ emulator -no-window -no-snapshot-load @pixel5
```

2. Turn on logs with logcat

```

There were even some changes to Pydantic itself to support this.

The previous screenshots were taken with <a href="https://code.visualstudio.com" class="external-link" target="_blank">Visual Studio Code</a>.

But you would get the same editor support with <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a> and most of the other Python editors:

You will rarely need to come back to the docs.

Here's how your editor might help you:

* in <a href="https://code.visualstudio.com/" class="external-link" target="_blank">Visual Studio Code</a>:

![editor support](https://fastapi.tiangolo.com/img/vscode-completion.png)

* in <a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a>:

https://www.linkedin.com/in/mnrozhkov/ link: https://www.evidentlyai.com/blog/fastapi-tutorial title: ML serving and monitoring with FastAPI and Evidently - author: Visual Studio Code Team author_link: https://code.visualstudio.com/ link: https://code.visualstudio.com/docs/python/tutorial-fastapi title: FastAPI Tutorial in Visual Studio Code - author: Apitally author_link: https://apitally.io link: https://blog.apitally.io/fastapi-application-monitoring-made-easy title: FastAPI application monitoring...

これは偶然ではなく、このデザインに基づいてフレームワークが作られています。

全てのエディターで機能することを確認するために、実装前の設計時に徹底的にテストしました。

これをサポートするためにPydantic自体にもいくつかの変更がありました。

上記のスクリーンショットは<a href="https://code.visualstudio.com" class="external-link" target="_blank">Visual Studio Code</a>を撮ったものです。

しかし、<a href="https://www.jetbrains.com/pycharm/" class="external-link" target="_blank">PyCharm</a>やほとんどのPythonエディタでも同様なエディターサポートを受けられます:

<img src="/img/tutorial/body/image05.png">

/// tip | 豆知識

  "durations" : [ {
    "testProject" : "largeAndroidBuild",
    "linux" : 469
  } ]
}, {
  "scenario" : "org.gradle.performance.regression.android.RealLifeAndroidStudioPerformanceTest.run Android Studio sync",
  "durations" : [ {
    "testProject" : "largeAndroidBuild",
    "linux" : 765
  }, {
    "testProject" : "nowInAndroidBuild",
    "linux" : 1515
  }, {

			ndjson = true
		}

		toPrint := make([]string, 0, 16)
		for _, file := range files {
			var r io.Reader
			var sz int64
			switch file {
			case "-":
				r = os.Stdin
			default:
				f, err := os.Open(file)
				if err != nil {
					return err
				}
				if st, err := f.Stat(); err == nil {
					sz = st.Size()
				}
				defer f.Close()
				r = f
			}

*   **Windows users:** Officially-released `tensorflow` Pip packages are now
    built with Visual Studio 2019 version 16.4 in order to take advantage of the
    new `/d2ReducedOptimizeHugeFunctions` compiler flag. To use these new
    packages, you must install "Microsoft Visual C++ Redistributable for Visual
    Studio 2015, 2017 and 2019", available from Microsoft's website