http://192.168.1.100:11434 or the docker host machine IP address, e.g., http://172.17.0.1:11434.For local source code deployment, use http://localhost:11434

一个 Docker Registry 中可以包含多个仓库（Repository）；每个仓库可以包含多个标签（Tag）；每个标签对应一个镜像。 通常，一个仓库会包含同一个软件不同版本的镜像，而标签就常用于对应该软件的各个版本。我们可以通过 <仓库名>:<标签> 的格式来指定具体是这个软件哪个版本的镜像。如果不给出标签，将以 latest 作为默认标签。

容器不应该向其存储层内写入任何数据，容器存储层要保持无状态化。所有的文件写入操作，都应该使用 数据卷（Volume）、或者绑定宿主目录，在这些位置的读写会跳过容器存储层，直接对宿主（或网络存储）发生读写，其性能和稳定性更高。

什么是 Docker Docker 属于 Linux 容器的一种封装，提供简单易用的容器使用接口。 它是目前最流行的 Linux 容器解决方案。 Docker 将应用程序与该程序的依赖，打包在一个文件里面。运行这个文件，就会生成一个虚拟容器。

["bash", "-i", "-c", "read -p 'Type a message: ' -t 10 && echo Attach $REPLY"],

Consequently, it’s one of the only commands that consistently allows interactions with users.

There are six available lifecycle script hooks

How can I wait for container X before starting Y? This is a common problem and in earlier versions of docker-compose requires the use of additional tools and scripts such as wait-for-it and dockerize. Using the healthcheck parameter the use of these additional tools and scripts is often no longer necessary.

docker inspect --format='{{.State.Health.Status}}'

Running Docker inside Docker lets you build images and start containers within an already containerized environment.

If your use case means you absolutely require dind, there is a safer way to deploy it. The modern Sysbox project is a dedicated container runtime that can nest other runtimes without using privileged mode. Sysbox containers become VM-like so they're able to support software that's usually run bare-metal on a physical or virtual machine. This includes Docker and Kubernetes without any special configuration.

Bind mounting your host's daemon socket is safer, more flexible, and just as feature-complete as starting a dind container.

Docker-in-Docker via dind has historically been widely used in CI environments. It means the "inner" containers have a layer of isolation from the host. A single CI runner container supports every pipeline container without polluting the host's Docker daemon.

While it often works, this is fraught with side effects and not the intended use case for dind. It was added to ease the development of Docker itself, not provide end user support for nested Docker installations.

This means containers created by the inner Docker will reside on your host system, alongside the Docker container itself. All containers will exist as siblings, even if it feels like the nested Docker is a child of the parent.

return &container.HostConfig{ DNS: e.Config.Docker.DNS, DNSSearch: e.Config.Docker.DNSSearch, RestartPolicy: neverRestartPolicy, ExtraHosts: e.Config.Docker.ExtraHosts, Privileged: e.Config.Docker.Privileged, NetworkMode: e.networkMode, Binds: e.volumesManager.Binds(), ShmSize: e.Config.Docker.ShmSize, Tmpfs: e.Config.Docker.ServicesTmpfs, LogConfig: container.LogConfig{ Type: "json-file", },

For Linux systems, you can – starting from major version 20.04 of the Docker engine – now also communicate with the host via host.docker.internal. This won't work automatically, but you need to provide the following run flag: --add-host=host.docker.internal:host-gateway

Podman commands are the same as Docker’s When building Podman, the goal was to make sure that Docker users could easily adapt. So all the commands you are familiar with also exist with Podman. In fact, the claim is made that if you have existing scripts that run Docker you can create a docker alias for podman and all your scripts should work (alias docker=podman). Try it.

This article does not get into the detailed pros and cons of the Docker daemon process.  There is much to be said in favor of this approach and I can see why, in the early days of Docker, it made a lot of sense.  Suffice it to say that there were several reasons why Docker users were concerned about this approach as usage went up. To list a few: A single process could be a single point of failure. This process owned all the child processes (the running containers). If a failure occurred, then there were orphaned processes. Building containers led to security vulnerabilities. All Docker operations had to be conducted by a user (or users) with the same full root authority.

docker composeを用いる場合、docker-compose.ymlの設定項目の、「stdin_open」と「tty」をtrueにすることで設定を有効にできます。

docker init will scan your project and ask you to confirm and choose the template that best suits your application. Once you select the template, docker init asks you for some project-specific information, automatically generating the necessary Docker resources for your project.

The result? Our runtime image just got 6x smaller! Six times! From > 1.1 GB to 170 MB.

This final trick is not known to many as it’s rather newer compared to the other features I presented. It leverages Buildkit cache mounts, which basically instruct Buildkit to mount and manage a folder for caching reasons. The interesting thing is that such cache will persist across builds!By plugging this feature with Poetry cache (now you understand why I did want to keep caching?) we basically get a dependency cache that is re-used every time we build our project. The result we obtain is a fast dependency build phase when building the same image multiple times on the same environment.

We’ve (painstakingly) manually reviewed 310 live MLOps positions, advertised across various platforms in Q4 this year

docker-spacemacs from Harmonic Software Systems

Docker overviewDocker is an open platform for developing, shipping, and running applications. Docker enables you to separate your applications from your infrastructure so you can deliver software quickly. With Docker, you can manage your infrastructure in the same ways you manage your applications. By taking advantage of Docker's methodologies for shipping, testing, and deploying code, you can significantly reduce the delay between writing code and running it in production.

Rebuild your images regularly

Disabling caching

As long as you’re relying on caching, you’ll still get the old, insecure packages distributed in your images

caching means no updates

caching can lead to insecure images.

RUN poetry install --without dev && rm -rf $POETRY_CACHE_DIR

Rosetta is now Generally Available for all users on macOS 13 or later. It provides faster emulation of Intel-based images on Apple Silicon. To use Rosetta, see Settings. Rosetta is enabled by default on macOS 14.1 and later.

PHP would serve WordPress when it's run as a standalone Wasm application.

WebAssembly brings true portability to the picture. You can build a binary once and run it everywhere.

However, on top of the big image size, traditional containers are also bound to the architecture of the platform on which they run.

Wasm container images are much smaller than the traditional ones. Even the alpine version of the php container is bigger than the Wasm one.

With WASI SDK we can build a Wasm module out of PHP's codebase, written in C. After that, it takes a very simple Dockerfile based on scratch for us to make an OCI image that can be run with Docker+Wasm.

Docker Desktop now includes support for WebAssembly. It is implemented with a containerd shim that can run Wasm applications using a Wasm runtime called WasmEdge. This means that instead of the typical Windows or Linux containers which would run a separate process from a binary in the container image, you can now run a Wasm application in the WasmEdge runtime, mimicking a container. As a result, the container image does not need to contain OS or runtime context for the running application - a single Wasm binary suffices.

If WASM+WASI existed in 2008, we wouldn't have needed to create Docker. That's how important it is. WebAssembly on the server is the future of computing.

the new Docker+Wasm integration allows you to run a Wasm application alongside your Linux containers at much faster speed.

Docker Desktop and CLI can now manage both Linux containers and Wasm containers side by side.

What is the Docker host filesystem owner matching problem?

This is the script, which I’ve named docker and put before the real Docker CLI in my PATH

// save to tar filedocker save nodeversion > nodeversion.tar// load from tar filedocker load < nodeversion.tar

Host machine: docker run -it -p 8888:8888 image:version Inside the Container : jupyter notebook --ip 0.0.0.0 --no-browser --allow-root Host machine access this url : localhost:8888/tree‌

If you install a package with pip’s --user option, all its files will be installed in the .local directory of the current user’s home directory.

Using pex in combination with S3 for storing the pex files, we built a system where the fast path avoids the overhead of building and launching Docker images.Our system works like this: when you commit code to GitHub, the GitHub action either does a full build or a fast build depending on if your dependencies have changed since the previous deploy. We keep track of the set of dependencies specified in setup.py and requirements.txt.For a full build, we build your project dependencies into a deps.pex file and your code into a source.pex file. Both are uploaded to Dagster cloud. For a fast build we only build and upload the source.pex file.In Dagster Cloud, we may reuse an existing container or provision a new container as the code server. We download the deps.pex and source.pex files onto this code server and use them to run your code in an isolated environment.

This could be because the size can be misleading, there is on disk size, push/pull payload size, and sum of ungzipped tars. The size of the ungzipped tars is often used to represent the size of the image in Docker but the actual size on disk is dependent on the graph driver. From the registry perspective, the sum of the gzipped layers is most important because it represent what the registry is storing and what needs to be transferred.

depending on how smart the framework is, you might find yourself installing Conda packages over and over again on every run. This is inefficient, even when using a faster installer like Mamba.

there’s the bootstrapping problem: depending on the framework you’re using, you might need to install Conda and the framework driver before you can get anything going. A Docker image would come prepackaged with both, in addition to your code and its dependencies. So even if your framework supports Conda directly, you might want to use Docker anyway.

Mlflow supports both Conda and Docker-based projects.

The only thing that will depend on the host operating system is glibc, pretty much everything else will be packaged by Conda. So a pinned environment.yml or conda-lock.yml file is a reasonable alternative to a Docker image as far as having consistent dependencies.

To summarize, for the use case of Python development environments, here’s how you might choose alternatives to Docker:

Conda packages everything but the standard C library, from C libraries to the Python interpreter to command-line tools to compilers.

Solution #3: Switch to Conda-Forge

In general, the environment variable is too heavy-handed and should be avoided, since it will impact all images you build or run. Given the speed impact, you don’t for example want to run your postgres image with emulation, to no benefit.

The problem with this approach is that the emulation slows down your runtime. How much slower it is? Once benchmark I ran was only 6% of the speed of the host machine!

The other option is to run x86_64 Docker images on your ARM64 Mac machine, using emulation. Docker is packaged with software that will translate or emulate x86_64 machine code into ARM64 machine code on the fly; it’s slow, but the code will run.

Third, you can pre-compile wheels, store them somewhere, and install those directly instead of downloading the packages from PyPI.

If you have a compiler installed in your Docker image and any required native libraries and development headers, you can compile a native package from the source code. Basically, you add a RUN apt-get upgrade && apt-get install -y gcc and iterate until the package compiles successfully.

First, it’s possible the relevant wheels are available in newer versions of the libraries.

When you pip install murmurhash==1.0.6 on a M1/M2 Mac inside Docker, again it looks at the available files

When you pip install filprofiler==2022.05.0 on a M1/M2 Mac inside Docker, pip will look at the available files for that version, and then

In either case, pure Python will Just Work, because it’s interpreted at runtime: there’s no CPU-specific machine code, it’s just text that the Python interpreter knows how to run. The problems start when we start using compiled Python extensions. These are machine code, and therefore you need a version that is specific to your particular CPU instruction set.

on an Intel/AMD PC or Mac, docker pull will pull the linux/amd64 image. On a newer Mac using M1/M2/Silicon chips, docker pull will the pull the linux/arm64/v8 image.

In order to meet its build-once-run-everywhere promise, Docker typically runs on Linux. Since macOS is not Linux, on macOS this is done by running a virtual machine in the background, and then the Docker images run inside the virtual machine. So whether you’re on a Mac, Linux, or Windows, typically you’ll be running linux Docker images.

Docker Desktop is an easy-to-install application and includes Docker Engine, Docker CLI client, Docker Compose, Docker Content Trust, Kubernetes, and Credential Helper

Docker Hub is to Docker what GitHub is to Git

A container packages code and all its dependencies into a single unit, thus letting an application run quickly and reliably from one computing environment to another

Compose V2 has been re-written in Go, which improves integration with other Docker command-line features, and allows it to run natively on macOS on Apple silicon, Windows, and Linux, without dependencies such as Python.

Introduction of the Compose specification makes a clean distinction between the Compose YAML file model and the docker-compose implementation.

# Due to lack of "expressivity" in Compose, we define our own couple of service # "pseudo-types": # # - image-only services (name: *-image) # # The only goal of these is to build images. No other services build images. # # These have entrypoint overridden to exit immediately. # # - base services (name: *-base) # # These contain common configuration and are intended to be extended. # # Their command (not entrypoint, to keep the original one) is overridden to # exit immediately. Service must support a command to exit immediately. # # - task services (name: *-task) # # These are intended for running one-off commands. # # Their default command is overridden to exit immediately. Service must # support a command to exit immediately. # # - "real" services # # These are actual services that stay up and running.

使用Docker-Compose部署单节点ELK-Stack

Docker容器无法连接外部网络原因排查

JAVA大军，开始把目光从spring cloud转向k8s甚至k8s+istio了么？

Java8在Docker里性能不好是真的吗？

为什么游戏公司的server不愿意微服务化？

Docker Desktop is a free, easy-to-install, downstream application for a Mac or Windows environment. The application lets you build and share containerized applications and microservices. Docker consists of Docker Engine, Docker Compose, Docker CLI client, Docker Content Trust,  Kubernetes, and Credential Helper.

virtual environment has a hypervisor layer, whereas Docker has a Docker engine layer

A container is a runtime instance of an image

Docker engine is the layer on which Docker runs. It’s a lightweight runtime and tooling that manages containers, images, builds, and more

A Dockerfile is where you write the instructions to build a Docker image

While a full dive into container orchestration is beyond the scope of this article, two prominent players are Docker with Docker Compose and Docker Swarm mode, and Kubernetes. In roughly order of complexity, Docker Compose is a container orchestration solution that deals with multi-container deployments on a single host. When there are multiple hosts involved, Docker Swarm mode is required.

Docker is an open-source project based on Linux containers. It uses Linux Kernel features like namespaces and control groups to create containers on top of an operating system.

it’s important to understand some of the fundamental concepts around what a “container” is and how it compares to a Virtual Machine (VM)

Requirements for rpi_gpio not found

passenger-docker images contain an Ubuntu 20.04 operating system. You may want to update this OS from time to time, for example to pull in the latest security updates. OpenSSL is a notorious example. Vulnerabilities are discovered in OpenSSL on a regular basis, so you should keep OpenSSL up-to-date as much as you can. While we release passenger-docker images with the latest OS updates from time to time, you do not have to rely on us. You can update the OS inside passenger-docker images yourself, and it is recommend that you do this instead of waiting for us.

the problem with docker builds is the made-up concept of "context". Dockerfiles are not sufficient to define a build, unless they are placed under a strategic directory (aka context), i.e. "/" as an extreme, so you can access any path (note that that's not the right thing to do in a sane project either..., plus it makes docker builds very slow because docker scans the entire context at start).

I would not change the project structure to accommodate Docker (or any build tools).

docker-compose搭建kong和konga，开箱即用

The following command connects an already-running my-nginx container to an already-existing my-net network

/opt/rabbitmq_delayed_message_exchange-3.10.2.ez复制到容器里去：

docker cp c:\path\to\local\file container_name:/path/to/target/dir/

By default, this function reads template files in /etc/nginx/templates/*.template and outputs the result of executing envsubst to /etc/nginx/conf.d.

It is "guaranteed" as long as you are on the default network. 172.17.0.1 is no magic trick, but simply the gateway of the network bridge, which happens to be the host. All containers will be connected to bridge unless specified otherwise.

For example I use docker on windows, using docker-toolbox (OG) so that it has less conflicts with the rest of my setup and I don't need HyperV.

# This ensures that the pid namespace is shared between the host # and the container. It's not necessary to be able to run spring # commands, but it is necessary for "spring status" and "spring stop" # to work properly. pid: host

This is a neat Docker trick for those who have an ARM development machine (Apple M1), but sometimes need to build x86/amd64 images locally to push up to a registry.

So, we can create this builder on our local machine. The nice part about this creation is that it is idempotent, so you can run this command many times without changing the result. All we have to do is to create a builder profile and in this case I have named it amd64_builder.

Vous connaissez maintenant la différence entre conteneur et machine virtuelle ; vous avez ainsi pu voir les différences entre la virtualisation lourde et la virtualisation légère.Un conteneur doit être léger, il ne faut pas ajouter de contenu superflu dans celui-ci afin de le démarrer rapidement, mais il apporte une isolation moindre. À contrario, les machines virtuelles offrent une très bonne isolation, mais elle sont globalement plus lentes et bien plus lourdes.

A normal Makefile for building projects with Docker would look more or less like this:

One of the main benefits from tagging your container images with the corresponding commit hash is that it's easy to trace back who a specific point in time, know how the application looked and behaved like for that specifc point in history and most importantly, blame the people that broke the application ;)

Software Bill Of Materials (SBOM) is analogous to a packing list for a shipment; it’s all the components that make up the software, or were used to build it. For container images, this includes the operating system packages that are installed (e.g.: ca-certificates) along with language specific packages that the software depends on (e.g.: log4j). The SBOM could include only some of this information or even more details, like the versions of components and where they came from.

Included in Docker Desktop 4.7.0 is a new, experimental docker sbom CLI command that displays the SBOM (Software Bill Of Materials) of any Docker image.

As of today, the Docker Engine is to be intended as an open source software for Linux, while Docker Desktop is to be intended as the freemium product of the Docker, Inc. company for Mac and Windows platforms. From Docker's product page: "Docker Desktop includes Docker Engine, Docker CLI client, Docker Build/BuildKit, Docker Compose, Docker Content Trust, Kubernetes, Docker Scan, and Credential Helper".

The diagram below tries to summarise the situation as of today, and most importantly to clarify the relationships between the various moving parts.

Without accounting for what we install or add inside, the base python:3.8.6-buster weighs 882MB vs 113MB for the slim version. Of course it's at the expense of many tools such as build toolchains3 but you probably don't need them in your production image.4 Your ops teams should be happier with these lighter images: less attack surface, less code that can break, less transfer time, less disk space used, ... And our Dockerfile is still readable so it should be easy to maintain.

scratch is a special empty image with no operating system.

Have you ever built an image only to realize that you actually need it on a user account other than root, requiring you to rebuild the image again in rootless mode? Or have you built an image on one machine but run containers on the image using multiple different machines? Now you need to set up an account on a registry, push the image to the registry, Secure Shell (SSH) to each device you want to run the image on, and then pull the image. The podman image scp command solves both of these annoying scenarios as quickly as they occur.

But the problem with Poetry is arguably down to the way Docker’s build works: Dockerfiles are essentially glorified shell scripts, and the build system semantic units are files and complete command runs. There is no way in a normal Docker build to access the actually relevant semantic information: in a better build system, you’d only re-install the changed dependencies, not reinstall all dependencies anytime the list changed. Hopefully someday a better build system will eventually replace the Docker default. Until then, it’s square pegs into round holes.

Third, you can use poetry-dynamic-versioning, a plug-in for Poetry that uses Git tags instead of pyproject.toml to set your application’s version. That way you won’t have to edit pyproject.toml to update the version. This seems appealing until you realize you now need to copy .git into your Docker build, which has its own downsides, like larger images unless you’re using multi-stage builds.

But if you’re doing some sort of continuous deployment process where you’re continuously updating the version field, your Docker builds are going to be slow.

21 containers running .. docker compose 👍 Create a docker user for easy permissions.4AntwortenTeilenMeldenSpeichernFolgenLevel 2Reiep · vor 2 JahrenCould you give us more info on that? I have some weird permissinos issues sometimes, that I can't figure out how to resolve.And how do you use compose on a Synology NAS, also. Thanks!1AntwortenAuszeichnenTeilenMeldenSpeichernFolgenLevel 3[deleted] · vor 2 JahrenMost of the containers from linuxserver on dockerhub have a PGID and PUID env, if you fill in the created docker user, it can access the volumes you want.Just make sure to add the created docker user to synology permissions on the dirs.3AntwortenTeilenMeldenSpeichernFolgen

The ENTRYPOINT specifies a command that will always be executed when the container starts. The CMD specifies arguments that will be fed to the ENTRYPOINT.

LXC, is a serious contender to virtual machines. So, if you are developing a Linux application or working with servers, and need a real Linux environment, LXC should be your go-to. Docker is a complete solution to distribute applications and is particularly loved by developers. Docker solved the local developer configuration tantrum and became a key component in the CI/CD pipeline because it provides isolation between the workload and reproducible environment.

Jeżeli masz dylemat czy użyć CMD, czy ENTRYPOINT jako punkt startowy twojego kontenera, odpowiedz sobie na następujące pytanie.Czy zawsze moje polecenie MUSI się wykonać? Jeśli odpowiedź brzmi tak, użyj ENTRYPOINT. Co więcej, jeśli potrzebujesz przekazać dodatkowe parametry, które mogą być nadpisane podczas uruchomienia kontenera — użyj również instrukcji CMD.

I was seeing this same issue. Updating values for: hub: containerSecurityContext: privileged: true Seems to have been the fix for me. At least things are a lot more stable now. I changed it based on the explanation for --privileged in the README.

This basic example compiles a simple Go program. The naive way on the left results in a 961 MB image. When using a multi-stage build, we copy just the compiled binary which results in a 7 MB image.

# Image size: 7 MB

FROM golang:1.17.5 as builder

WORKDIR /workspace
COPY . .
ENV CGO_ENABLED=0
RUN go get && go build -o main .

FROM scratch
WORKDIR /workspace
COPY --from=builder \
     /workspace/main \
     /workspace/main

CMD ["/workspace/main"]

Docker introduced multi-stage builds starting from Docker Engine v17.05. This allows us to perform all preparations steps as before, but then copy only the essential files or output from these steps.

Making a small change to a file or moving it will create an entire copy of the file. Deleting a file will only hide it from the final image, but it will still exist in its original layer, taking up space. This is all a result of how images are structured as a series of read-only layers. This provides reusability of layers and efficiencies with regards to how images are stored and executed. But this also means we need to be aware of the underlying structure and take it into account when we create our Dockerfile.

In this example, we created 3 copies of our file throughout different layers of the image. Despite removing the file in the last layer, the image still contains the file in other layers which contributes to the overall size of the image.

FROM debian:bullseye

COPY somefile.txt . #1

# Small change but entire file is copied
RUN echo "more data" >> somefile.txt #2

# File moved but layer now contains an entire copy of the file
RUN mv somefile.txt somefile2.txt #3

# File won't exist in this layer,
# but it still takes up space in the previous ones.
RUN rm somefile2.txt

We’re just chmod'ing an existing file, but Docker can’t change the file in its original layer, so that results in a new layer where the file is copied in its entirety with the new permissions.In newer versions of Docker, this can now be written as the following to avoid this issue using Docker’s BuildKit:

FROM debian:bullseye

COPY somefile.txt .

RUN chmod 777 somefile.txt

FROM debian:bullseye

COPY --chmod=777 somefile.txt .

when you make changes to files that come from previous layers, they’re copied into the new layer you’re creating.

Many processes will create temporary files, caches, and other files that have no benefit to your specific use case. For example, running apt-get update will update internal files that you don’t need to persist because you’ve already installed all the packages you need. So we can add rm -rf /var/lib/apt/lists/* as part of the same layer to remove those (removing them with a separate RUN will keep them in the original layer, see “Avoid duplicating files”). Docker recognize this is an issue and went as far as adding apt-get clean automatically for their official Debian and Ubuntu images.

An important way to ensure you’re not bringing in unintended files is to define a .dockerignore file.

# Ignore git and caches
.git
.cache

# Ignore logs
logs

# Ignore secrets
.env

# Ignore installed dependencies
node_modules

...

You can save any local image as a tar archive and then inspect its contents.

bash-3.2$ docker save <image-digest> -o image.tar
bash-3.2$ tar -xf image.tar -C image
bash-3.2$ cd image
bash-3.2$ tar -xf <layer-digest>/layer.tar
bash-3.2$ ls

同一宿主机下不同docker之间的通信

docker scan elastic/logstash:7.13.3 | grep 'Arbitrary Code Execution'

I’d probably choose the official Docker Python image (python:3.9-slim-bullseye) just to ensure the latest bugfixes are always available.

So which should you use? If you’re a RedHat shop, you’ll want to use their image. If you want the absolute latest bugfix version of Python, or a wide variety of versions, the official Docker Python image is your best bet. If you care about performance, Debian 11 or Ubuntu 20.04 will give you one of the fastest builds of Python; Ubuntu does better on point releases, but will have slightly larger images (see above). The difference is at most 10% though, and many applications are not bottlenecked on Python performance.

There are three major operating systems that roughly meet the above criteria: Debian “Bullseye” 11, Ubuntu 20.04 LTS, and RedHat Enterprise Linux 8.

You probably shouldn't use Alpine for Python projects, instead use the slim Docker image versions.

Running containers without Docker is possible with Podman.

daemonless container engine

Simply put: alias docker=podman.

What is Podman? Podman is a daemonless container engine for developing, managing, and running OCI Containers on your Linux System.

You can attach Visual Studio Code to this container by right-clicking on it and choosing the Attach Visual Studio Code option. It will open a new window and ask you which folder to open.

You don’t have to download them manually, as a docker-compose.yml will do that for you. Here’s the code, so you can copy it to your machine:

In 2020, 35% of respondents said they used Docker. In 2021, 48.85% said they used Docker. If you look at estimates for the total number of developers, they range from 10 to 25 million. That's 1.4 to 3 million new users this year.

kind, microk8s, or k3s are replacements for Docker Desktop. False. Minikube is the only drop-in replacement. The other tools require a Linux distribution, which makes them a non-starter on macOS or Windows. Running any of these in a VM misses the point – you don't want to be managing the Kubernetes lifecycle and a virtual machine lifecycle. Minikube abstracts all of this.

docker compose up

There are multiple tools for running Kubernetes on your local machine, but it basically boils down to two approaches on how it is done

there is a drawback, docker-compose runs on a single node which makes scaling hard, manual and very limited. To be able to scale services across multiple hosts/nodes, orchestrators like docker-swarm or kubernetes comes into play.

We had to build the image for our python API every-time we changed the code, we had to run each container separately and manually insuring that out database container is running first. Moreover, We had to create a network before hand so that we connect the containers and we had to add these containers to that network and we called it mynet back then. With docker-compose we can forget about all of that.

Even though Kubernetes is moving away from Docker, it will always support the OCI and Docker image formats. Kubernetes doesn’t pull and run images itself, instead the Kubelet relies on container engines like CRI-O and containerd to pull and run the images. These are the two main container engines used with CRI-O and they both support the Docker and OCI image formats, so no worries on this one.