When you check the benchmarks, it is common to see several tools of different types compared as equivalent.

Specifically, to see Uvicorn, Starlette and FastAPI compared together (among many other tools).

The simpler the problem solved by the tool, the better performance it will get. And most of the benchmarks don't test the additional features provided by the tool.

The hierarchy is like:

* **Uvicorn**: an ASGI server

your changes by running the command:

```bash
tensorflow/tools/ci_build/ci_build.sh CPU tensorflow/tools/ci_build/ci_sanity.sh
```

This will catch most license, Python coding style and BUILD file issues that
may exist in your changes.

#### Running unit tests

There are two ways to run TensorFlow unit tests.

1.  Using tools and libraries installed directly on your system.

    Refer to the

Aber in den meisten Fällen, wenn wir so etwas machen, wollen wir nur, dass das Modell einige der Daten **filtert/entfernt**, so wie in diesem Beispiel.

   *
   * [rfc_7468]: https://tools.ietf.org/html/rfc7468
   */
  fun certificatePem(): String = certificate.certificatePem()

  /**
   * Returns the private key encoded in [PKCS #8][rfc_5208] [PEM format][rfc_7468].
   *
   * [rfc_5208]: https://tools.ietf.org/html/rfc5208
   * [rfc_7468]: https://tools.ietf.org/html/rfc7468
   */
  fun privateKeyPkcs8Pem(): String =

 *   <li>Project-specific configuration</li>
 *   <li>Goal execution and phase mapping</li>
 *   <li>Multi-module build coordination</li>
 * </ul>
 *
 * @see org.apache.maven.api.cli.Tools#MVN_CMD
 * @see org.apache.maven.api.cli.Tools#MVN_NAME
 * @since 4.0.0
 */

  rm -rf "${tmp_dir}"
}

DIR=$1
mkdir -p "$DIR"
TARBALL_SUFFIX=$2

if [[ $(uname -s) != MSYS_NT* ]]; then
  cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz "${DIR}/libtensorflow${TARBALL_SUFFIX}.tar.gz"
  cp bazel-bin/tensorflow/tools/lib_package/libtensorflow_jni.tar.gz "${DIR}/libtensorflow_jni${TARBALL_SUFFIX}.tar.gz"
  cp bazel-bin/tensorflow/java/libtensorflow.jar "${DIR}"

fi

tfrun bazel $TFCI_BAZEL_BAZELRC_ARGS build $TFCI_BAZEL_COMMON_ARGS --config=cuda_wheel //tensorflow/tools/pip_package:wheel $TFCI_BUILD_PIP_PACKAGE_BASE_ARGS $TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG --verbose_failures

tfrun "$TFCI_FIND_BIN" ./bazel-bin/tensorflow/tools/pip_package -iname "*.whl" -exec cp {} $TFCI_OUTPUT_DIR \;
tfrun mkdir -p ./dist
tfrun cp $TFCI_OUTPUT_DIR/*.whl ./dist

///

## **FastAPI** Tools

FastAPI stellt für jedes dieser Sicherheitsschemas im Modul `fastapi.security` verschiedene Tools bereit, die die Verwendung dieser Sicherheitsmechanismen vereinfachen.

[pip-compile](https://pypi.org/project/pip-tools/) under the hood):

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

where `3.12` is the `Python` version you wish to update.

Note, since it is still `pip` and `pip-compile` tools used under the hood, so
most of the command line arguments and features supported by those tools will be

And there has to be something in charge of **renewing the HTTPS certificates**, it could be the same component or it could be something different.

### Example Tools for HTTPS { #example-tools-for-https }

Some of the tools you could use as a TLS Termination Proxy are:

* Traefik
    * Automatically handles certificates renewals ✨
* Caddy
    * Automatically handles certificates renewals ✨
* Nginx