`uv` can do a lot of things, it can:

* **Install Python** for you, including different versions
* Manage the **virtual environment** for your projects
* Install **packages**
* Manage package **dependencies and versions** for your project

Thực hiện điều này **một lần** khi cài đặt hoặc cập nhật gói cần thiết cho dự án của bạn.

Nếu bạn cần cập nhật phiên bản hoặc thêm một gói mới, bạn sẽ **thực hiện điều này lại**.

///

### Cài đặt gói trực tiếp

Nếu bạn cần cập nhật phiên bản hoặc thêm một gói mới, bạn sẽ **thực hiện điều này lại**.

/// tip

Here's the reference information for the request parameters.

These are the special functions that you can put in *path operation function* parameters or dependency functions with `Annotated` to get data from the request.

It includes:

* `Query()`
* `Path()`
* `Body()`
* `Cookie()`
* `Header()`
* `Form()`
* `File()`

You can import them all directly from `fastapi`:

```python

   
   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.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

![ReDoc](https://fastapi.tiangolo.com/img/index/index-06-redoc-02.png)

### Chỉ cần phiên bản Python hiện đại

Tất cả được dựa trên khai báo kiểu dữ liệu chuẩn của **Python 3.8** (cảm ơn Pydantic). Bạn không cần học cú pháp mới, chỉ cần biết chuẩn Python hiện đại.

# Templates { #templates }

You can use any template engine you want with **FastAPI**.

A common choice is Jinja2, the same one used by Flask and other tools.

There are utilities to configure it easily that you can use directly in your **FastAPI** application (provided by Starlette).

## Install dependencies { #install-dependencies }

### Starting and Stopping Containers

To start a stopped container, you can use the [`docker start`](https://docs.docker.com/engine/reference/commandline/start/) command.

```sh
docker start <container_id>
```

To stop a running container, you can use the [`docker stop`](https://docs.docker.com/engine/reference/commandline/stop/) command.

```sh
docker stop <container_id>

    String[] WINERR_MESSAGES = { "The operation completed successfully.", "Access is denied.",
            "No more connections can be made to this remote computer at this time because there are already as many connections as the computer can accept.",
            "The pipe state is invalid.", "All pipe instances are busy.", "The pipe is being closed.",

    String[] WINERR_MESSAGES = { "The operation completed successfully.", "Access is denied.",
            "No more connections can be made to this remote computer at this time because there are already as many connections as the computer can accept.",
            "The pipe state is invalid.", "All pipe instances are busy.", "The pipe is being closed.",

///

## Run your code with your debugger { #run-your-code-with-your-debugger }

Because you are running the Uvicorn server directly from your code, you can call your Python program (your FastAPI application) directly from the debugger.

---

For example, in Visual Studio Code, you can:

* Go to the "Debug" panel.
* "Add configuration...".
* Select "Python"