File pom0 = new File(localRepo, "p0/pom.xml");

        File pom0Basedir = pom0.getParentFile();

        File pom1 = new File(pom0Basedir, "p1/pom.xml");

        // load everything...
        MavenProject project0 = getProjectWithDependencies(pom0);
        MavenProject project1 = getProjectWithDependencies(pom1);

        assertEquals(pom0Basedir, project1.getParent().getBasedir());

    // Round down to the nearest power of 2.
    expectedEntries = max(expectedEntries, 2);
    int tableSize = Integer.highestOneBit(expectedEntries);
    // Check to make sure that we will not exceed the maximum load factor.
    if (expectedEntries > (int) (loadFactor * tableSize)) {
      tableSize <<= 1;
      return (tableSize > 0) ? tableSize : MAX_TABLE_SIZE;
    }
    return tableSize;
  }

        Properties systemProperties = new Properties();

        // ----------------------------------------------------------------------
        // Load environment and system properties
        // ----------------------------------------------------------------------

        EnvironmentUtils.addEnvVars(systemProperties);

        probably silently use `float32` in TensorFlow2, and a warning will be
        issued that starts with Layer "layer-name" is casting an input tensor
        from dtype float64 to the layer's dtype of float32. To fix, either set
        the default dtype to float64 with
        `tf.keras.backend.set_floatx('float64')`, or pass `dtype='float64'` to

& ⤴️ ⚫️ 🔜 ❎ 🚮 📨 👆 Uvicorn 🏃‍♂ 🔛 `http://127.0.0.1:8000`.

🔜 ▶️ Traefik:

<div class="termy">

```console
$ ./traefik --configFile=traefik.toml

INFO[0000] Configuration loaded from file: /home/user/awesomeapi/traefik.toml
```

</div>

&amp; 🔜 ▶️ 👆 📱 ⏮️ Uvicorn, ⚙️ `--root-path` 🎛:

<div class="termy">

```console
$ uvicorn main:app --root-path /api/v1

  private GraphConstants() {}

  static final int EXPECTED_DEGREE = 2;

  static final int DEFAULT_NODE_COUNT = 10;
  static final int DEFAULT_EDGE_COUNT = DEFAULT_NODE_COUNT * EXPECTED_DEGREE;

  // Load factor and capacity for "inner" (i.e. per node/edge element) hash sets or maps
  static final float INNER_LOAD_FACTOR = 1.0f;
  static final int INNER_CAPACITY = 2; // ceiling(EXPECTED_DEGREE / INNER_LOAD_FACTOR)

### SIG Azure

* The Azure cloud provider now supports standard SKU load balancer and public IP.
`excludeMasterFromStandardLB` defaults to true, which means master nodes are excluded from the standard load balancer. Also note that because all nodes (except master) are added as loadbalancer backends, the standard load balancer doesn't work with the `service.beta.kubernetes.io/azure-load-balancer-mode` annotation.

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

    @PostConstruct
    public void init() {
        if (logger.isDebugEnabled()) {
            logger.debug("Initialize {}", this.getClass().getSimpleName());
        }
        load();
    }

    @Override
    public int load() {
        final List<LabelType> labelTypeList = ComponentUtil.getComponent(LabelTypeService.class).getLabelTypeList();
        buildLabelTypeItems(labelTypeList);

        File localRepo = getLocalRepositoryPath();
        File pom0 = new File(localRepo, "p0/pom.xml");
        File pom0Basedir = pom0.getParentFile();
        File pom1 = new File(pom0Basedir, "p1/pom.xml");

        // load the child project, which inherits from p0...
        MavenProject project0 = getProjectWithDependencies(pom0);
        MavenProject project1 = getProjectWithDependencies(pom1);