# 서버를 수동으로 실행하기 { #run-a-server-manually }

## `fastapi run` 명령 사용하기 { #use-the-fastapi-run-command }

요약하면, `fastapi run`을 사용해 FastAPI 애플리케이션을 서비스하세요:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

TensorFlow's official CI jobs run the scripts in this folder. Our internal CI
system, Kokoro, schedules our CI jobs by combining a build script with a file
from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)
    -   Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh`
-   Continuous jobs (Run on every GitHub commit)
    -   Uses `pycpp.sh`

  }

  private abstract static class FailToForward extends ForwardingObject implements Runnable {
    @Override
    public void run() {}
  }

  private abstract static class ForwardToDelegate extends ForwardingObject implements Runnable {
    @Override
    public void run() {
      delegate().run();
    }

    @Override
    protected abstract Runnable delegate();
  }

# 手動執行伺服器 { #run-a-server-manually }

## 使用 `fastapi run` 指令 { #use-the-fastapi-run-command }

簡而言之，使用 `fastapi run` 來啟動你的 FastAPI 應用：

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

             Searching for package file structure from directories

        val result = run(":flaky-test-without-leftover:test").build()
        assertEquals(TaskOutcome.SUCCESS, result.task(":flaky-test-without-leftover:test")!!.outcome)

        assertArchivedFilesSeen("report-flaky-test-without-leftover-test.zip")
    }

    @Test
    fun `leftover files are archived when test fails`() {
        val result = run(
            ":failed-report-with-leftover:test",

    ```bash
    docker run -it --rm -v $PWD:/tmp -w /tmp tensorflow/build:2.15-python3.10
    ```

    Once you have the packages installed, you can run a specific unit test in
    bazel by doing as follows:

    ```bash
    export flags="--config=linux -k"
    ```

    If the tests are to be run on the GPU:

# Запустіть сервер вручну { #run-a-server-manually }

## Використовуйте команду `fastapi run` { #use-the-fastapi-run-command }

Коротко: використовуйте `fastapi run`, щоб запустити ваш застосунок FastAPI:

<div class="termy">

```console
$ <font color="#4E9A06">fastapi</font> run <u style="text-decoration-style:solid">main.py</u>

  <span style="background-color:#009485"><font color="#D3D7CF"> FastAPI </font></span>  Starting production server 🚀

  }

  private abstract static class FailToForward extends ForwardingObject implements Runnable {
    @Override
    public void run() {}
  }

  private abstract static class ForwardToDelegate extends ForwardingObject implements Runnable {
    @Override
    public void run() {
      delegate().run();
    }

    @Override
    protected abstract Runnable delegate();
  }

argument to the bazel command:

```
bazel run //ci/official/requirements_updater:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12 -- --pre
```

If you need to upgrade all of the packages in requirements lock file, just pass
the `--upgrade` parameter:

```
bazel run //ci/official/requirements_updater:requirements.update --repo_env=HERMETIC_PYTHON_VERSION=3.12 -- --upgrade
```

 * in the Gradle build.
 *
 * Each project may require a set of "target runtimes" that it must be able to run on.
 * Each target runtime reflects a runtime environment where code is executed. For one
 * project to be able to run in a given runtime, all of its dependencies must also
 * support running in that runtime.
 *