pkg debug/elf, const EM_C166 = 116
pkg debug/elf, const EM_C166 Machine
pkg debug/elf, const EM_CDP = 215
pkg debug/elf, const EM_CDP Machine
pkg debug/elf, const EM_CE = 119
pkg debug/elf, const EM_CE Machine
pkg debug/elf, const EM_CLOUDSHIELD = 192
pkg debug/elf, const EM_CLOUDSHIELD Machine
pkg debug/elf, const EM_COGE = 216
pkg debug/elf, const EM_COGE Machine
pkg debug/elf, const EM_COOL = 217
pkg debug/elf, const EM_COOL Machine

  Remove-Item $zulu_zip; \
  $env:PATH = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\") + \";${zulu_root}\\bin\"; \
  [Environment]::SetEnvironmentVariable(\"PATH\", $env:PATH, \"Machine\"); \
  $env:JAVA_HOME = $zulu_root; \
  [Environment]::SetEnvironmentVariable(\"JAVA_HOME\", $env:JAVA_HOME, \"Machine\")

# Point to the LLVM installation.
# The Bazel Windows guide claims it can find LLVM automatically,

### 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.

Eso, más el simple hecho de que Python es el lenguaje principal para **Data Science**, Machine Learning y especialmente Deep Learning, hacen de FastAPI una muy buena opción para APIs web de Data Science / Machine Learning y aplicaciones (entre muchas otras).

### Server Memory { #server-memory }

For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

        assertNotNull(defaultConfig.getRandom(), "Random should not be null");
        assertNotNull(defaultConfig.getLocalTimezone(), "Local timezone should not be null");
        assertNotNull(defaultConfig.getMachineId(), "Machine ID should not be null");
    }

    @Test
    @DisplayName("Test network configuration getters")
    void testNetworkConfigurationGetters() {
        assertEquals(0, config.getLocalPort());

 * Windows command prompt you can see
 * what names a host registers with the nbtstat command.
 *
 *  * <blockquote>
 *
 * <pre>
 * C:\&gt;nbtstat -a 192.168.1.15
 *
 *        NetBIOS Remote Machine Name Table
 *
 *    Name               Type         Status
 * ---------------------------------------------
 * JMORRIS2        &lt;00&gt;  UNIQUE      Registered
 * BILLING-NY      &lt;00&gt;  GROUP       Registered

 |
 |                 -s /path/to/user/settings.xml
 |
 |  2. Installation Level.
 |                 This settings.xml file provides configuration for all Maven
 |                 users on a machine (assuming they're all using the same Maven
 |                 installation). It's normally provided in
 |                 ${maven.installation.conf}/settings.xml.
 |

* <abbr title="Eine Gruppe von Maschinen, die so konfiguriert sind, dass sie verbunden sind und in irgendeiner Weise zusammenarbeiten.">Cluster</abbr>
* <abbr title="Eine Methode des Machine Learning, die künstliche neuronale Netze mit zahlreichen versteckten Schichten zwischen Eingabe- und Ausgabeschicht verwendet und so eine umfassende interne Struktur entwickelt">Deep Learning</abbr>