### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

     * @param argTypes
     *            The parameter types array
     * @return The {@link Constructor} object for the {@code public} constructor matching the specified {@code argTypes}
     * @throws NoSuchConstructorRuntimeException
     *             If a matching constructor is not found
     * @see Class#getConstructor(Class...)
     */

                foundTestMethod = true;
                break;
            }
        }
        assertTrue(foundTestMethod);
    }

    public void test_throwAndCatch() {
        // Test throwing and catching the exception
        String expectedMessage = "Offset limit exceeded: 10000";

        try {
            throw new ResultOffsetExceededException(expectedMessage);
        } catch (ResultOffsetExceededException e) {

    if (url)
      links[i].addEventListener("click", function () {
        openURL(this.getAttribute("url"));
      });
  }
}

window.addEventListener("load", function () {
  addLinks();
  console.log("hacking gopher pop-up loaded.");
  document.getElementById("inputbox").focus();
});

window.addEventListener("submit", function () {
  console.log("submitting form");
  var box = document.getElementById("inputbox");

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

-postgres-and-aws-app-runner-at-any-scale title: Deploy a Serverless FastAPI App with Neon Postgres and AWS App Runner at any scale - author: Kurtis Pykes - NVIDIA link: https://developer.nvidia.com/blog/building-a-machine-learning-microservice-with-fastapi/ title: Building a Machine Learning Microservice with FastAPI - author: Ravgeet Dhillon - Twilio link: https://www.twilio.com/en-us/blog/booking-appointments-twilio-notion-fastapi title: Booking Appointments with Twilio, Notion, and FastAPI -...

  // ExtendSessionGraphHelper manually.
  std::atomic<bool> extend_before_run;
};

struct TF_ImportGraphDefOptions {
  tensorflow::ImportGraphDefOptions opts;

  // Backing memory for TensorId fields in opts.
  // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this.
  std::vector<tensorflow::string> tensor_id_data;
};

struct TF_ImportGraphDefResults {

     * mirror, its URL and ID will be updated to match the values from the mirror specification. Repositories without a
     * matching mirror will pass through unchanged. Note: This method must be called before
     * {@link #injectAuthentication(List, List)} or the repositories will end up with the wrong credentials.
     *

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande planilha com números e em multiplicar todos eles juntos e ao mesmo tempo.

  }

  public void testHashCode_equalsAndSerializable() throws Exception {
    sanityTester().testEqualsAndSerializable();
  }

  public void testRoundTripHashCodeUsingBaseEncoding() {
    HashCode hash1 = Hashing.sha1().hashString("foo", US_ASCII);
    HashCode hash2 = HashCode.fromBytes(BaseEncoding.base16().lowerCase().decode(hash1.toString()));
    assertEquals(hash1, hash2);
  }

  public void testObjectHashCode() {