## Deployment Strategies { #deployment-strategies }

There are several ways to do it depending on your specific use case and the tools that you use.

You could **deploy a server** yourself using a combination of tools, you could use a **cloud service** that does part of the work for you, or other possible options.

     * @param scope the {link PathScope} to collect dependencies, must not be {@code null}
     * @return the collection result, never {@code null}
     * @throws DependencyResolverException if the dependency tree could not be built
     * @throws IllegalArgumentException if an argument is null or invalid
     * @see #collect(DependencyResolverRequest)
     */
    @Nonnull
    default DependencyResolverResult collect(

        super("The POM expression: " + expression + " could not be evaluated. Reason: " + message, cause);

        this.expression = expression;
        this.originalMessage = message;
    }

    public ModelInterpolationException(String expression, String message) {
        super("The POM expression: " + expression + " could not be evaluated. Reason: " + message);

        this.expression = expression;

# Learn { #learn }

Here are the introductory sections and the tutorials to learn **FastAPI**.

If it was in a type annotation we could have used the vertical bar, as:

```Python
some_variable: PlaneItem | CarItem
```

But if we put that in the assignment `response_model=PlaneItem | CarItem` we would get an error, because Python would try to perform an **invalid operation** between `PlaneItem` and `CarItem` instead of interpreting that as a type annotation.

After that, you would need to **install** FastAPI and any other **packages** you want to use.

To install packages you would normally use the `pip` command that comes with Python (or similar alternatives).

Nevertheless, if you just use `pip` directly, the packages would be installed in your **global Python environment** (the global installation of Python).

including tensorflow-io should be sandboxed if used to process untrusted data.

For example, if an attacker were to upload a malicious video file, they could
potentially exploit a vulnerability in the TensorFlow code that handles videos,
which could allow them to execute arbitrary code on the system running
TensorFlow.

It is important to keep TensorFlow up to date with the latest security patches

You would have **one single `engine` object** for all your code to connect to the same database.

{* ../../docs_src/sql_databases/tutorial001_an_py310.py ln[14:18] hl[14:15,17:18] *}

Using `check_same_thread=False` allows FastAPI to use the same SQLite database in different threads. This is necessary as **one single request** could use **more than one thread** (for example in dependencies).

   *     HashFunction#hashString} method if the string contains non-BMP characters. Use {@link
   *     #murmur3_32_fixed(int)} instead.
   */
  @Deprecated
  @SuppressWarnings("IdentifierName") // the best we could do for adjacent digit blocks
  public static HashFunction murmur3_32(int seed) {
    return new Murmur3_32HashFunction(seed, /* supplementaryPlaneFix= */ false);
  }

  /**

Nevertheless, it is already being used as a "standard" by several tools. This greatly improves interoperability, as you could switch Uvicorn for any other ASGI server (like Daphne or Hypercorn), or you could add ASGI compatible tools, like `python-socketio`.

///

/// check | **FastAPI** uses it to

Handle all the core web parts. Adding features on top.