### Federation

parts of the aggregate.

  6. Conveying Non-Source Forms.

  You may convey a covered work in object code form under the terms
of sections 4 and 5, provided that you also convey the
machine-readable Corresponding Source under the terms of this License,
in one of these ways:

    a) Convey the object code in, or embodied in, a physical product
    (including a physical distribution medium), accompanied by the

     *
     * @param hook The shutdown hook to add.
     */
    public void addShutdownHook(final Runnable hook) {
        shutdownHookList.add(hook);
    }

    /**
     * Gets the hostname of the local machine.
     *
     * @return The hostname.
     */
    public String getHostname() {
        final Map<String, String> env = getEnvMap();
        if (env.containsKey("COMPUTERNAME")) {

    reading the public suffix database would cause subsequent reads to hang when they should have
    crashed.

 *  Fix: Avoid `InetAddress.getCanonicalHostName()` in MockWebServer. This avoids problems if the
    host machine's IP address has additional DNS registrations.

 *  New: Create a JPMS-compatible artifact for `JavaNetCookieJar`. Previously, multiple OkHttp

Uygulamanız **basitse**, muhtemelen bu **bir sorun olmaz** ve katı memory limitleri belirlemeniz gerekmeyebilir. Ancak **çok memory kullanıyorsanız** (ör. **machine learning** modelleriyle), ne kadar memory tükettiğinizi kontrol edip **her makinede** çalışacak **container sayısını** ayarlamalısınız (ve gerekirse cluster'a daha fazla makine eklemelisiniz).

* **機器學習**: 通常需要大量的「矩陣」和「向量」運算。想像一個包含數字的巨大電子表格，並所有的數字同時相乘;
* **深度學習**: 這是機器學習的子領域，同樣適用。只不過這不僅僅是一張數字表格，而是大量的數據集合，並且在很多情況下，你會使用特殊的處理器來構建或使用這些模型。

### 並行 + 平行: Web + 機器學習 { #concurrency-parallelism-web-machine-learning }

使用 **FastAPI**，你可以利用並行的優勢，這在 Web 開發中非常常見（這也是 NodeJS 的最大吸引力）。

但你也可以利用平行與多行程 (multiprocessing)（讓多個行程同時運行） 的優勢來處理機器學習系統中的 **CPU 密集型**工作。

* **机器学习**: 它通常需要大量的"矩阵"和"向量"乘法。想象一个包含数字的巨大电子表格，并同时将所有数字相乘；
* **深度学习**: 这是机器学习的一个子领域，同样适用。只是没有一个数字的电子表格可以相乘，而是一个庞大的数字集合，在很多情况下，你需要使用一个特殊的处理器来构建和使用这些模型。

### 并发 + 并行: Web + 机器学习 { #concurrency-parallelism-web-machine-learning }

使用 **FastAPI**，你可以利用 Web 开发中常见的并发机制的优势（NodeJS 的主要吸引力）。

并且，你也可以利用并行和多进程（让多个进程并行运行）的优点来处理与机器学习系统中类似的 **CPU 密集型** 工作。

/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes](http://kubernetes.io) using the [Helm](https://helm.sh)...

### Nebenläufigkeit + Parallelität: Web + maschinelles Lernen { #concurrency-parallelism-web-machine-learning }

Mit **FastAPI** können Sie die Vorteile der Nebenläufigkeit nutzen, die in der Webentwicklung weit verbreitet ist (derselbe Hauptvorteil von NodeJS).

/minio/minio/blob/master/LICENSE) MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads. For more detailed documentation please visit [here](https://docs.minio.io/) Introduction ---------- This chart bootstraps MinIO Cluster on [Kubernetes](http://kubernetes.io) using the [Helm](https://helm.sh)...