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).

fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,
# so I am not sure how to verify that the dependencies themselves are valid for
# the moment.
if [[ "$TFCI_BAZEL_COMMON_ARGS" =~ gpu|cuda ]]; then

                                    + now
                                    + ", lastUpdated = " + lastUpdated + "). Please verify that the clocks of all"
                                    + " deploying machines are reasonably synchronized.");
                    versioning.setLastUpdated(now);
                    changed = true;
                }
            }
        }

        if (changed) {

### 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. 🚨

🍵 ⚙️ 📦, ⚒ 🈸 🏃 🔛 🕴 & ⏮️ ⏏ 💪 ⚠ & ⚠. ✋️ 🕐❔ **👷 ⏮️ 📦** 🌅 💼 👈 🛠️ 🔌 🔢. 👶

## 🧬 - 🔢 🛠️

🚥 👆 ✔️ <abbr title="A group of machines that are configured to be connected and work together in some way.">🌑</abbr> 🎰 ⏮️ **☁**, ☁ 🐝 📳, 🖖, ⚖️ ➕1️⃣ 🎏 🏗 ⚙️ 🛠️ 📎 📦 🔛 💗 🎰, ⤴️ 👆 🔜 🎲 💚 **🍵 🧬** **🌑 🎚** ↩️ ⚙️ **🛠️ 👨‍💼** (💖 🐁 ⏮️ 👨‍🏭) 🔠 📦.

컨테이너를 사용하지 않고서는, 어플리케이션을 구동하고 재시작하는 것이 매우 번거롭고 어려울 수 있습니다. 하지만 **컨테이너를 사용한다면** 대부분의 경우에 이런 기능은 기본적으로 포함되어 있습니다. ✨

## 복제 - 프로세스 개수

만약 여러분이 **쿠버네티스**와 머신 <abbr title="A group of machines that are configured to be connected and work together in some way.">클러스터</abbr>, 도커 스왐 모드, 노마드, 또는 다른 여러 머신 위에 분산 컨테이너를 관리하는 복잡한 시스템을 다루고 있다면, 여러분은 각 컨테이너에서 (워커와 함께 사용하는 Gunicorn 같은) **프로세스 매니저** 대신 **클러스터 레벨**에서 **복제를 다루**고 싶을 것입니다.

message NodeSystemInfo {
  // MachineID reported by the node. For unique machine identification
  // in the cluster this field is preferred. Learn more from man(5)
  // machine-id: http://man7.org/linux/man-pages/man5/machine-id.5.html
  optional string machineID = 1;

  // SystemUUID reported by the node. For unique machine identification
  // MachineID is preferred. This field is specific to Red Hat hosts

### Serverspeicher

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**.

Linux-Container werden mit demselben Linux-Kernel des Hosts (Maschine, virtuellen Maschine, Cloud-Servers, usw.) ausgeführt. Das bedeutet einfach, dass sie sehr leichtgewichtig sind (im Vergleich zu vollständigen virtuellen Maschinen, die ein gesamtes Betriebssystem emulieren).

Auf diese Weise verbrauchen Container **wenig Ressourcen**, eine Menge vergleichbar mit der direkten Ausführung der Prozesse (eine virtuelle Maschine würde viel mehr verbrauchen).

### SIG API Machinery

[SIG API Machinery][] is responsible for all aspects of the API server: API registration and discovery, generic API CRUD semantics, admission control, encoding/decoding, conversion, defaulting, persistence layer (etcd), OpenAPI, third-party resources, garbage collection, and client libraries.