/**
     * A Hybrid node tries to resolve a name using the nameserver first. If
     * that fails use the broadcast address. This is the default if a nameserver
     * is provided. This is the behavior of Microsoft Windows machines.
     */

    public static final int H_NODE = 3;

    /**
     * Unknown MAC Address
     */

        NetbiosAddress mockHost = mock(NetbiosAddress.class);
        SecureRandom mockRandom = mock(SecureRandom.class);

        // Create a machine ID (32 bytes)
        byte[] machineId = new byte[32];
        mockRandom.nextBytes(machineId);

        lenient().when(mockConfig.getDefaultDomain()).thenReturn("TESTDOMAIN");
        lenient().when(mockConfig.isUseUnicode()).thenReturn(true);

Linux containers run using the same Linux kernel of the host (machine, virtual machine, cloud server, etc). This just means that they are very lightweight (compared to full virtual machines emulating an entire operating system).

This way, containers consume **little resources**, an amount comparable to running the processes directly (a virtual machine would consume much more).

        // Use reflection to set private field
        java.lang.reflect.Field machineIdField = BaseConfiguration.class.getDeclaredField("machineId");
        machineIdField.setAccessible(true);
        machineIdField.set(testConfig, customMachineId);

        testConfig.initDefaults();

        assertArrayEquals(customMachineId, testConfig.getMachineId());
    }

    @Test

### 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. 🚨

    /**
     * A Hybrid node tries to resolve a name using the nameserver first. If
     * that fails use the broadcast address. This is the default if a nameserver
     * is provided. This is the behavior of Microsoft Windows machines.
     */

    public static final int H_NODE = 3;

    static final InetAddress[] NBNS = Config.getInetAddressArray("jcifs.smb1.netbios.wins", ",", new InetAddress[0]);

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,

* **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.

### 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.