# FastAPI in Containers - Docker

When deploying FastAPI applications a common approach is to build a **Linux container image**. It's normally done using <a href="https://www.docker.com/" class="external-link" target="_blank">**Docker**</a>. You can then deploy that container image in one of a few possible ways.

Using Linux containers has several advantages including **security**, **replicability**, **simplicity**, and others.

!!! tip

  }

  protected Collection<Method> suppressForConcurrentSkipListSetWithComparator() {
    return Collections.emptySet();
  }

  public Test testsForCheckedNavigableSet() {
    return SortedSetTestSuiteBuilder.using(
            new TestStringSortedSetGenerator() {
              @Override
              public NavigableSet<String> create(String[] elements) {
                NavigableSet<String> innerSet = new TreeSet<>();

## Code Examples

[Using minio-go library](https://github.com/minio/minio/blob/master/docs/extensions/s3zip/examples/minio-go/main.go)
[Using AWS JS SDK v2](https://github.com/minio/minio/blob/master/docs/extensions/s3zip/examples/aws-js/main.js)
[Using boto3](https://github.com/minio/minio/blob/master/docs/extensions/s3zip/examples/boto3/main.py)

    link: https://www.analyticsvidhya.com/blog/2021/06/deploying-ml-models-as-api-using-fastapi-and-heroku/
    title: Deploying ML Models as API Using FastAPI and Heroku
  - link: https://jarmos.netlify.app/posts/using-github-actions-to-deploy-a-fastapi-project-to-heroku/
    title: Using GitHub Actions to Deploy a FastAPI Project to Heroku
    author_link: https://jarmos.netlify.app/
    author: Somraj Saha

		logger.CriticalIf(context.Background(), errors.New("Unable to encrypt ETag using object key"))
	}
	return buffer.Bytes()
}

// UnsealETag unseals the etag using the provided object key.
// It does not try to decrypt the ETag if len(etag) == 16
// because such ETags indicate that the S3 client hasn't sent

                     TF_Status* status) {
  using tensorflow::NodeDef;
  using tensorflow::OpRegistrationData;
  using tensorflow::Tensor;
  using tensorflow::shape_inference::DimensionHandle;
  using tensorflow::shape_inference::InferenceContext;
  using tensorflow::shape_inference::ShapeAndType;
  using tensorflow::shape_inference::ShapeHandle;

  const int num_inputs = input_shapes->num_items;

# Using Dataclasses

FastAPI is built on top of **Pydantic**, and I have been showing you how to use Pydantic models to declare requests and responses.

But FastAPI also supports using <a href="https://docs.python.org/3/library/dataclasses.html" class="external-link" target="_blank">`dataclasses`</a> the same way:

```Python hl_lines="1  7-12  19-20"
{!../../../docs_src/dataclasses/tutorial001.py!}
```

	if err != nil {
		logger.LogOnceIf(ctx, fmt.Errorf("%s, using default of 10s", err.Error()), "refresh-kms-master-key")
		cacheDuration = defaultCacheDuration
	}
	if cacheDuration < time.Second {
		logger.LogOnceIf(ctx, errors.New("cache duration is less than 1s, using default of 10s"), "refresh-kms-master-key")
		cacheDuration = defaultCacheDuration
	}

use the Browser to create buckets, upload objects, and browse the contents of the MinIO server. You can also connect using any S3-compatible tool, such as the MinIO Client `mc` commandline tool. See [Test using MinIO Client `mc`](#test-using-minio-client-mc) for more information on using the `mc` commandline tool. For application developers, see <https://min.io/docs/minio/linux/developers/minio-drivers.html> to view MinIO SDKs for supported languages. > NOTE: Standalone MinIO servers are best suited...

   * SipHash-2-4 algorithm</a> using a seed value of {@code k = 00 01 02 ...}.
   *
   * @since 15.0
   */
  public static HashFunction sipHash24() {
    return SipHashFunction.SIP_HASH_24;
  }

  /**
   * Returns a hash function implementing the <a href="https://131002.net/siphash/">64-bit
   * SipHash-2-4 algorithm</a> using the given seed.
   *
   * @since 15.0
   */