assertEquals("1.0 GB", format.format(_1000_megabytes));

        long _5500_megabytes = 5500L * 1000L * 1000L;
        assertEquals("5.5 GB", format.format(_5500_megabytes));

        long _10_gigabytes = 10L * 1000L * 1000L * 1000L;
        assertEquals("10 GB", format.format(_10_gigabytes));

        long _15_gigabytes = 15L * 1000L * 1000L * 1000L;
        assertEquals("15 GB", format.format(_15_gigabytes));

多个进程通常**不共享任何内存**。 这意味着每个正在运行的进程都有自己的东西、变量和内存。 如果您的代码消耗了大量内存，**每个进程**将消耗等量的内存。

### 服务器内存

例如，如果您的代码加载 **1 GB 大小**的机器学习模型，则当您使用 API 运行一个进程时，它将至少消耗 1 GB RAM。 如果您启动 **4 个进程**（4 个工作进程），每个进程将消耗 1 GB RAM。 因此，您的 API 总共将消耗 **4 GB RAM**。

如果您的远程服务器或虚拟机只有 3 GB RAM，尝试加载超过 4 GB RAM 将导致问题。 🚨


### 多进程 - 一个例子

在此示例中，有一个 **Manager Process** 启动并控制两个 **Worker Processes**。

Por exemplo, se seu código carrega um modelo de Machine Learning com **1 GB de tamanho**, quando você executa um processo com sua API, ele consumirá pelo menos 1 GB de RAM. E se você iniciar **4 processos** (4 trabalhadores), cada um consumirá 1 GB de RAM. Então, no total, sua API consumirá **4 GB de RAM**.

E se o seu servidor remoto ou máquina virtual tiver apenas 3 GB de RAM, tentar carregar mais de 4 GB de RAM causará problemas. 🚨

                </mxCell>
                <mxCell id="23" value="&lt;font face=&quot;Roboto&quot; data-font-src=&quot;https://fonts.googleapis.com/css?family=Roboto&quot; style=&quot;font-size: 24px&quot;&gt;1 GB&lt;/font&gt;" style="rounded=0;whiteSpace=wrap;html=1;strokeWidth=3;fillColor=#fff2cc;strokeColor=#d6b656;" parent="1" vertex="1">
                    <mxGeometry x="1130" y="490" width="150" height="150" as="geometry"/>

Wenn Ihr Code beispielsweise ein Machine-Learning-Modell mit **1 GB Größe** lädt und Sie einen Prozess mit Ihrer API ausführen, verbraucht dieser mindestens 1 GB RAM. Und wenn Sie **4 Prozesse** (4 Worker) starten, verbraucht jeder 1 GB RAM. Insgesamt verbraucht Ihre API also **4 GB RAM**.

Und wenn Ihr entfernter Server oder Ihre virtuelle Maschine nur über 3 GB RAM verfügt, führt der Versuch, mehr als 4 GB RAM zu laden, zu Problemen. 🚨

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. 🚨

### Multiple Processes - An Example

    val connection = server.url("/").toUrl().openConnection() as HttpURLConnection
    connection.setRequestMethod("POST")
    connection.setDoOutput(true)
    connection.setFixedLengthStreamingMode(1024 * 1024 * 1024) // 1 GB
    connection.connect()
    val out = connection.outputStream
    val data = ByteArray(1024 * 1024)
    var i = 0
    while (i < 1024) {
      try {
        out.write(data)
        out.flush()

    val connection = server.url("/").toUrl().openConnection() as HttpURLConnection
    connection.requestMethod = "POST"
    connection.doOutput = true
    connection.setFixedLengthStreamingMode(1024 * 1024 * 1024) // 1 GB
    connection.connect()
    val out = connection.outputStream
    val data = ByteArray(1024 * 1024)
    var i = 0
    while (i < 1024) {
      try {
        out!!.write(data)
        out.flush()

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