When you’re choosing a base image for your Docker image, Alpine Linux is often recommended.Using Alpine, you’re told, will make your images smaller and speed up your builds.And if you’re using Go that’s reasonable advice.

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1. When you’re choosing a base image for your Docker image, Alpine Linux is often recommended. Using Alpine, you’re told, will make your images smaller and speed up your builds. And if you’re using Go that’s reasonable advice. But if you’re using Python, Alpine Linux will quite often: Make your builds much slower. Make your images bigger. Waste your time. On occassion, introduce obscure.
2. Install Docker on your machine and add it to the system path. On Linux, you should also enable Docker CLI for the non-root user account that will be used to run VS Code. To install the extension, open the Extensions view (⇧⌘X (Windows, Linux Ctrl+Shift+X)), search for docker to filter results and select Docker extension authored by Microsoft.
3. BetterCAP is containerized using Alpine Linux - a security-oriented, lightweight Linux distribution based on musl libc and busybox. The resulting Docker image is relatively small and easy to manage the dependencies. Since it is using a multi-stage build, a Docker version greater than 17.05 is required.

Example Dockerfile for your own Node.js project. If you're doing your npm install/npm ci or yarn install from your Dockerfile, then you'll probably want to add nodemodules to your.dockerignore file first, so that it doesn't get sent to the docker daemon.

But if you’re using Python, Alpine Linux will quite often:

1. Make your builds much slower.
2. Make your images bigger.
3. Waste your time.
4. On occassion, introduce obscure runtime bugs.

Let’s see why Alpine is recommended, and why you probably shouldn’t use it for your Python application.

Why people recommend Alpine

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Let’s say we need to install gcc as part of our image build, and we want to see how Alpine Linux compares to Ubuntu 18.04 in terms of build time and image size.

First, I’ll pull both images, and check their size:

As you can see, the base image for Alpine is much smaller.

Next, we’ll try installing gcc in both of them.First, with Ubuntu:

Note: Outside the very specific topic under discussion, the Dockerfiles in this article are not examples of best practices, since the added complexity would obscure the main point of the article.

To ensure you’re writing secure, correct, fast Dockerfiles, consider my Python on Docker Production Handbook, which includes a packaging process and >70 best practices.

We can then build and time that:

Now let’s make the equivalent Alpine Dockerfile:

And again, build the image and check its size:

As promised, Alpine images build faster and are smaller: 15 seconds instead of 30 seconds, and the image is 105MB instead of 150MB.That’s pretty good!

But when we switch to packaging a Python application, things start going wrong.

Let’s build a Python image

We want to package a Python application that uses pandas and matplotlib.So one option is to use the Debian-based official Python image (which I pulled in advance), with the following Dockerfile:

And when we build it:

The resulting image is 363MB.

Can we do better with Alpine? Let’s try:

And now we build it:

What’s going on?

Standard PyPI wheels don’t work on Alpine

If you look at the Debian-based build above, you’ll see it’s downloading matplotlib-3.1.2-cp38-cp38-manylinux1_x86_64.whl.This is a pre-compiled binary wheel.Alpine, in contrast, downloads the source code (matplotlib-3.1.2.tar.gz), because standard Linux wheels don’t work on Alpine Linux.

Why?Most Linux distributions use the GNU version (glibc) of the standard C library that is required by pretty much every C program, including Python.But Alpine Linux uses musl, those binary wheels are compiled against glibc, and therefore Alpine disabled Linux wheel support.

Most Python packages these days include binary wheels on PyPI, significantly speeding install time.But if you’re using Alpine Linux you need to compile all the C code in every Python package that you use.

Which also means you need to figure out every single system library dependency yourself.In this case, to figure out the dependencies I did some research, and ended up with the following updated Dockerfile:

And then we build it, and it takes…

… 25 minutes, 57 seconds! And the resulting image is 851MB.

Here’s a comparison between the two base images:

Base image	Time to build	Image size	Research required
python:3.8-slim	30 seconds	363MB	No
python:3.8-alpine	1557 seconds	851MB	Yes

Alpine builds are vastly slower, the image is bigger, and I had to do a bunch of research.

Can’t you work around these issues?

Build time

For faster build times, Alpine Edge, which will eventually become the next stable release, does have matplotlib and pandas.And installing system packages is quite fast.As of January 2020, however, the current stable release does not include these popular packages.

Even when they are available, however, system packages almost always lag what’s on PyPI, and it’s unlikely that Alpine will ever package everything that’s on PyPI.In practice most Python teams I know don’t use system packages for Python dependencies, they rely on PyPI or Conda Forge.

Image size

Some readers pointed out that you can remove the originally installed packages, or add an option not to cache package downloads, or use a multi-stage build.One reader attempt resulted in a 470MB image.

So yes, you can get an image that’s in the ballpark of the slim-based image, but the whole motivation for Alpine Linux is smaller images and faster builds.With enough work you may be able to get a smaller image, but you’re still suffering from a 1500-second build time when they you get a 30-second build time using the python:3.8-slim image.

But wait, there’s more!

Alpine Linux can cause unexpected runtime bugs

While in theory the musl C library used by Alpine is mostly compatible with the glibc used by other Linux distributions, in practice the differences can cause problems.And when problems do occur, they are going to be strange and unexpected.

Some examples:

1. Alpine has a smaller default stack size for threads, which can lead to Python crashes.
2. One Alpine user discovered that their Python application was much slower because of the way musl allocates memory vs. glibc.
3. I once couldn’t do DNS lookups in Alpine images running on minikube (Kubernetes in a VM) when using the WeWork coworking space’s WiFi.The cause was a combination of a bad DNS setup by WeWork, the way Kubernetes and minikube do DNS, and musl’s handling of this edge case vs. what glibc does.musl wasn’t wrong (it matched the RFC), but I had to waste time figuring out the problem and then switching to a glibc-based image.
4. Another user discovered issues with time formatting and parsing.

Most or perhaps all of these problems have already been fixed, but no doubt there are more problems to discover.Random breakage of this sort is just one more thing to worry about.

Don’t use Alpine Linux for Python images

Unless you want massively slower build times, larger images, more work, and the potential for obscure bugs, you’ll want to avoid Alpine Linux as a base image.For some recommendations on what you should use, see my article on choosing a good base image.

Visual Studio Code can create and start containers for you but that may not match your workflow and you may prefer to 'attach' VS Code to an already running Docker container - regardless of how it was started. Once attached, you can install extensions, edit, and debug like you can when you open a folder in a container using devcontainer.json.

Attach to a Docker container

To attach to a Docker container, either select Remote-Containers: Attach to Running Container.. from the Command Palette (F1) or use the Remote Explorer in the Activity Bar and from the Containers view, select the Attach to Container inline action on the container you want to connect to.

Note: When using Alpine Linux containers, some extensions may not work due to glibc dependencies in native code inside the extension.

Attached container configuration files

VS Code supports image or container name-level configuration files to speed up setup when you repeatedly connect to a given Docker container. Once attached, anytime you open a folder, install an extension, or forward a port, a local image-specific configuration file will automatically be updated to remember your settings so that when you attach again, everything is back to the right place.

• By default, an image-level configuration is used. To view or update it after attaching, select Remote-Containers: Open Container Configuration from the Command Palette (F1).

• If you would prefer to tie your configuration to a container name, select Remote-Containers: Open Named Configuration File from the Command Palette (F1) after attaching. Any updates from this point forward will apply to this name-level configuration rather than at the image level.

Both of these files support a subset of devcontainer.json properties:

See the attached container config reference for a complete list of properties and their uses.

Once saved, whenever you open a container for the first time with the same image / container name, these properties will be used to configure the environment.

Mac games dmg. Tip: If something is wrong with your configuration, you can also edit it when not attached to the container by selecting Remote-Containers: Open Attached Container Configuration File.. from the Command Palette (F1) and then picking the image / container name from the presented list.

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Finally, if you have extensions you want installed regardless of the container you attach to, you can update settings.json to specify a list of extensions that should always be installed.

Attach to a container in a Kubernetes cluster

To attach to a container in a Kubernetes cluster, first install the Kubernetes extension and kubectl along with the Remote - Containers extension. Then select the Kubernetes explorer from the Activity bar and expand the cluster and Pod where the container you want to attach to resides. Finally, right-click on the container and select Attach Visual Studio Code from context menu.

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Note: Attached container configuration files are not yet supported for containers in a Kubernetes cluster.

Next steps

Install Gcc In Docker Container

• Create a Development Container - Create a custom container for your work environment.
• Advanced Containers - Find solutions to advanced container scenarios.
• devcontainer.json reference - Review the devcontainer.json schema.