5e0a761b87
(for upgrading users, please see the notes at the bottom) Bazel brought a lot of nice things to the table, such as rebuilds based on content changes instead of modification times, caching of build products, detection of incorrect build rules via a sandbox, and so on. Rewriting the build in Bazel was also an opportunity to improve on the Makefile-based build we had prior, which was pretty poor: most dependencies were external or not pinned, and the build graph was poorly defined and mostly serialized. It was not uncommon for fresh checkouts to fail due to floating dependencies, or for things to break when trying to switch to an older commit. For day-to-day development, I think Bazel served us reasonably well - we could generally switch between branches while being confident that builds would be correct and reasonably fast, and not require full rebuilds (except on Windows, where the lack of a sandbox and the TS rules would cause build breakages when TS files were renamed/removed). Bazel achieves that reliability by defining rules for each programming language that define how source files should be turned into outputs. For the rules to work with Bazel's sandboxing approach, they often have to reimplement or partially bypass the standard tools that each programming language provides. The Rust rules call Rust's compiler directly for example, instead of using Cargo, and the Python rules extract each PyPi package into a separate folder that gets added to sys.path. These separate language rules allow proper declaration of inputs and outputs, and offer some advantages such as caching of build products and fine-grained dependency installation. But they also bring some downsides: - The rules don't always support use-cases/platforms that the standard language tools do, meaning they need to be patched to be used. I've had to contribute a number of patches to the Rust, Python and JS rules to unblock various issues. - The dependencies we use with each language sometimes make assumptions that do not hold in Bazel, meaning they either need to be pinned or patched, or the language rules need to be adjusted to accommodate them. I was hopeful that after the initial setup work, things would be relatively smooth-sailing. Unfortunately, that has not proved to be the case. Things frequently broke when dependencies or the language rules were updated, and I began to get frustrated at the amount of Anki development time I was instead spending on build system upkeep. It's now about 2 years since switching to Bazel, and I think it's time to cut losses, and switch to something else that's a better fit. The new build system is based on a small build tool called Ninja, and some custom Rust code in build/. This means that to build Anki, Bazel is no longer required, but Ninja and Rust need to be installed on your system. Python and Node toolchains are automatically downloaded like in Bazel. This new build system should result in faster builds in some cases: - Because we're using cargo to build now, Rust builds are able to take advantage of pipelining and incremental debug builds, which we didn't have with Bazel. It's also easier to override the default linker on Linux/macOS, which can further improve speeds. - External Rust crates are now built with opt=1, which improves performance of debug builds. - Esbuild is now used to transpile TypeScript, instead of invoking the TypeScript compiler. This results in faster builds, by deferring typechecking to test/check time, and by allowing more work to happen in parallel. As an example of the differences, when testing with the mold linker on Linux, adding a new message to tags.proto (which triggers a recompile of the bulk of the Rust and TypeScript code) results in a compile that goes from about 22s on Bazel to about 7s in the new system. With the standard linker, it's about 9s. Some other changes of note: - Our Rust workspace now uses cargo-hakari to ensure all packages agree on available features, preventing unnecessary rebuilds. - pylib/anki is now a PEP420 implicit namespace, avoiding the need to merge source files and generated files into a single folder for running. By telling VSCode about the extra search path, code completion now works with generated files without needing to symlink them into the source folder. - qt/aqt can't use PEP420 as it's difficult to get rid of aqt/__init__.py. Instead, the generated files are now placed in a separate _aqt package that's added to the path. - ts/lib is now exposed as @tslib, so the source code and generated code can be provided under the same namespace without a merging step. - MyPy and PyLint are now invoked once for the entire codebase. - dprint will be used to format TypeScript/json files in the future instead of the slower prettier (currently turned off to avoid causing conflicts). It can automatically defer to prettier when formatting Svelte files. - svelte-check is now used for typechecking our Svelte code, which revealed a few typing issues that went undetected with the old system. - The Jest unit tests now work on Windows as well. If you're upgrading from Bazel, updated usage instructions are in docs/development.md and docs/build.md. A summary of the changes: - please remove node_modules and .bazel - install rustup (https://rustup.rs/) - install rsync if not already installed (on windows, use pacman - see docs/windows.md) - install Ninja (unzip from https://github.com/ninja-build/ninja/releases/tag/v1.11.1 and place on your path, or from your distro/homebrew if it's 1.10+) - update .vscode/settings.json from .vscode.dist |
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Dockerfile | ||
README.md |
README.md
Building and running Anki in Docker
This is an example Dockerfile contributed by an Anki user, which shows how Anki can be both built and run from within a container. It works by streaming the GUI over an X11 socket.
Building and running Anki within a container has the advantage of fully isolating the build products and runtime dependencies from the rest of your system, but it is a somewhat niche approach, with some downsides such as an inability to display natively on Wayland, and a lack of integration with desktop icons/filetypes. But even if you do not use this Dockerfile as-is, you may find it useful as a reference.
Anki's Linux CI is also implemented with Docker, and the Dockerfiles for that may
also be useful for reference - they can be found in .buildkite/linux/docker
.
Build the Docker image
For best results, enable BuildKit (export DOCKER_BUILDKIT=1
).
When in this current directory, one can build the Docker image like this:
docker build --tag anki --file Dockerfile ../../
When this is done, run docker image ls
to see that the image has been created.
If one wants to build from the project's root directory, use this command:
docker build --tag anki --file docs/docker/Dockerfile .
Run the Docker image
Anki starts a graphical user interface, and this requires some extra setup on the user's end. These instructions were tested on Linux (Debian 11) and will have to be adapted for other operating systems.
To allow the Docker container to pull up a graphical user interface, we need to run the following:
xhost +local:root
Once done using Anki, undo this with
xhost -local:root
Then, we will construct our docker run
command:
docker run --rm -it \
--name anki \
--volume $HOME/.local/share:$HOME/.local/share:rw \
--volume /etc/passwd:/etc/passwd:ro \
--user $(id -u):$(id -g) \
--volume /tmp/.X11-unix:/tmp/.X11-unix:rw \
--env DISPLAY=$DISPLAY \
anki
Here is a breakdown of some of the arguments:
-
Mount the current user's
~/.local/share
directory onto the container. Anki saves things into this directory, and if we don't mount it, we will lose any changes once the container exits. We mount this as read-write (rw
) because we want to make changes here.--volume $HOME/.local/share:$HOME/.local/share:rw
-
Mount
/etc/passwd
so we can enter the container as ourselves. We mount this as read-only because we definitely do not want to modify this.--volume /etc/passwd:/etc/passwd:ro
-
Enter the container with our user ID and group ID, so we stay as ourselves.
--user $(id -u):$(id -g)
-
Mount the X11 directory that allows us to open displays.
--volume /tmp/.X11-unix:/tmp/.X11-unix:rw
-
Pass the
DISPLAY
variable to the container, so it knows where to display graphics.--env DISPLAY=$DISPLAY
Running Dockerized Anki easily from the command line
One can create a shell function that executes the docker run
command. Then one can
simply run anki
on the command line, and Anki will open in Docker. Make sure to change
the image name to whatever you used when building Anki.
anki() {
docker run --rm -it \
--name anki \
--volume $HOME/.local/share:$HOME/.local/share:rw \
--volume /etc/passwd:/etc/passwd:ro \
--user $(id -u):$(id -g) \
--volume /tmp/.X11-unix:/tmp/.X11-unix:rw \
--env DISPLAY=$DISPLAY \
anki "$@"
}