denver

Development Environments as code — reproducible, flexible, simple and fast.

Development Environment Launcher — declares dev environments in a denver.yml: reproducible and layerable to fit your project's needs.

What problem does this solve?

Every project needs some setup before you can build it — and how much varies enormously. So rather than one big answer, here is the problem built up one step at a time. Each step below is a complete, working denver environment. Stop at whichever one matches your project; there is no requirement to reach the last one.

An environment is described in the denver-world by a denver.yml.

Step 1 — "first, run these commands"

Almost every project starts here: a README section, or a setup.sh, listing what a newcomer has to do before anything works. The trouble is that a script in the repo is only a suggestion. Nobody re-runs it after a git pull, everyone's shell ends up in a slightly different state, and whatever happens to be installed on your machine silently covers for the steps you forgot to write down.

The smallest useful denver environment is that script, declared rather than documented:

# my-project/denver.yml
stages:
- setup

setup:
  provider: custom
  source: setup.sh   # sourced, not executed, so what it exports stays in effect

denver my-project           # apply setup.sh, then drop into a shell that has it
denver my-project -- make   # ...or run a single command in that shell

One stage. No Docker, no Conan, no Python packaging. Everybody gets the same setup, applied the same way, every time — and the shell you land in is disposable: what the stage exported is gone again once you exit.

Step 2 — "...and a Python virtualenv with the right packages in it"

Now the manual part is python -m venv, activate, pip install -r, and remembering to redo it whenever requirements.txt changes. A uv stage hands that whole job to denver:

stages:
- uv

uv:
  provider: uv
  python: "3.12.3"      # a pinned interpreter, not "whatever python3 happens to be"
  requirements:
  - requirements.txt

denver my-project now gives you a shell with that venv active: created on the first run, reused afterwards, and re-installed only when requirements.txt actually changed. examples/zephyr-uv/ is precisely this and nothing more.

Step 3 — "...but half our tools aren't Python at all"

Compilers, cmake, ninja, a vendor SDK, a flashing tool. This is what "install these six things first, versions X.Y" READMEs are made of, and no Python virtualenv can help. If a package manager can fetch them, denver can drive it as a further stage — conan ships built in:

stages:
- uv      # provides the 'conan' executable itself, via requirements.txt
- conan   # ...which then fetches the native tools

uv:
  provider: uv
  requirements:
  - requirements.txt

conan:
  provider: conan
  conanfiles:
  - path: conanfile.py

The order of stages: is the whole point: each stage leaves behind PATH entries, environment variables and files on disk that the next stage — and finally your shell — can use. Here that ordering is load-bearing in a way worth noticing: conan is itself a Python package, so the uv stage has to run first to put the conan binary on PATH for the stage named after it. examples/raspberry-pico/ is exactly this two-stage stack.

Step 4 — "...and it only builds on Ubuntu 22.04"

Some things cannot be papered over from inside a venv: glibc, system libraries, the distribution itself. A docker stage runs the stages after it inside a container, so the layers above stop depending on which laptop you are sitting at:

stages:
- docker   # everything below this line happens inside the container
- conan
- uv

docker is a wrapper: it installs nothing itself, it relocates the rest of the pipeline. Which is also why you can drop it whenever you like — denver my-project --skip docker runs the very same conan/uv stack straight on your host.

Step 5 — "...and five repositories need that same base"

Copy-pasting a stack into every repository is how it rots. import: lets one env inherit another's entire stack and restate only what differs:

import:
- ../our-shared-base   # its stages, its docker config, its variables

uv:                    # ...with only this project's packages layered on top
  requirements:
  - requirements.txt

A new project, or the next SDK version, then becomes a folder with a handful of lines in it instead of another copy of everything.

You only pay for the steps you need

Nothing above is mandatory, and denver has no opinion about which tools you should use. A stage exists only because your denver.yml lists it:

Those five built-in providers are the code that knows how to run a kind of stage; custom is the escape hatch for everything else, and a one-stage custom env is as legitimate as the five-stage one walked through below.

Run denver --help for every flag, see doc/architecture.md for the full stages:/import:/-c schema, or browse examples/ — there is one worked environment per step above, each with a README of its own explaining what it does and why.

Documentation

Install denver

denver is a small pure-Python package — install it from PyPI or straight from GitHub:

# with pip
pip install denver-tool
pip install git+https://github.com/thorsten-klein/denver.git

# or with uv
uv tool install denver-tool
uv tool install git+https://github.com/thorsten-klein/denver.git

This installs the denver script/entry point.

On a machine with no Python at all, take the standalone executable attached to every release instead — it bundles denver, its providers and a Python interpreter in one file:

curl -sSL https://github.com/thorsten-klein/denver/releases/latest/download/denver_x64_Linux.tar.xz | tar -xJf -
./denver --version

x86_64 Linux with glibc 2.28 or newer (Ubuntu 20.04+, Debian 10+, Fedora 29+, RHEL/Alma/Rocky 8+, Arch, Mint 20+); musl-based distros such as Alpine are not covered. Build it yourself with scripts/create-python-exe.sh. Note this only replaces denver's own installation — the tools its providers drive (see pre-conditions) still have to be there.

To hack on denver itself (or pin it to a specific commit/tag/branch), clone it and install in editable mode instead:

pip install -e .

If you'd rather vendor denver straight into your own monorepo instead of depending on it as an installed package, add it via git-nested:

git-nested clone https://github.com/thorsten-klein/denver.git

With that approach nothing needs installing — just call src/denver.py (or the vendored copy's equivalent path) directly, as in the quick start below.

Pre-conditions

denver itself only needs Python — it never installs the tools its providers drive. This table is a lookup, not a checklist: only the rows for the providers your own denver.yml actually lists apply to you. Each of those expects its tool to already be available wherever that stage runs (on the host, or inside the container once a docker stage relocated into it):

A stage whose tool is missing fails up front with a clear message. Each provider's page under doc/providers/ has the details, including how to point at a specific binary via exe:. Anything that must be installed on the host (docker itself, udev rules, ...) typically lives in an env's scripts: setup:, so an env can bring its own host tools along — run those once with:

denver <env> --run setup

See One-time host setup.

Getting started with a bundled example environment

The chapter above built an environment up from one stage. This one goes the other way and walks through the biggest bundled example end to end — examples/zephyr-devshell-4.3.1, a complete Zephyr RTOS 4.3.1 development setup — to show what the same mechanism looks like at full size. It is deliberately the extreme case, not the typical one.

If you would rather start from the small end, read examples/zephyr-uv/denver.yml (a venv, nothing else) or examples/simple-env/denver.yml (a couple of shell snippets) instead — both are a screenful, and the flags in The handful of options you'll actually use work identically for them.

Note that every command below works as denver <env> ... or src/denver.py <env> ....

The one command you need

denver examples/zephyr-devshell-4.3.1

That's it. A minute or two later (much less on repeat runs) you are sitting in a shell where west, cmake, ninja, the Zephyr SDK compilers and all required Python packages are installed and ready:

$ denver examples/zephyr-devshell-4.3.1
... denver builds/enters each layer ...
dev@container ~/workspace> west build -b nrf52840dk/nrf52840 samples/hello_world
dev@container ~/workspace> exit      # back to your normal shell

You did not install a compiler. You did not create a virtualenv. You did not write a bootstrap script. denver did all of it, and it will do exactly the same on your colleague's machine.

The vocabulary, in one place

• An environment is a folder containing a denver.yml, and you point denver at that folder. denver.yml is the recipe; denver is the cook that follows it. If you want to know what an environment does, you read its denver.yml.
• A stage is one step of the setup, listed in stages:. Order matters: each stage leaves behind PATH entries, environment variables and files that the next stage — and finally your shell or command — can use. Think of getting dressed: underwear before trousers before shoes.
• A provider is the code that knows how to run a kind of stage. You never write provider code; you configure it from denver.yml.

Every stage names its provider explicitly (provider: uv), so the stage id itself is just a label. That is what lets a single env run the same provider twice — this example has two uv stages, uv and uv-zephyr.

<env> also accepts a path straight to a YAML file instead of a folder — handy when a folder holds several variants side by side (e.g. denver.debug.yml, denver.release.yml):

denver examples/zephyr-devshell-4.3.1/denver.debug.yml

The 5 stages of this example

stages:
- docker      # 1. get into the right operating system
- conan       # 2. install native tools (compilers, cmake, ninja, ...)
- uv          # 3. install Python packages into a venv
- zephyr      # 4. download the Zephyr source repositories
- uv-zephyr   # 5. install the Python packages Zephyr itself asks for

Steps 1–3 are the same docker/conan/uv layers built up earlier; steps 4 and 5 are what a Zephyr workspace adds on top. In plain words:

At the end, denver hands control over to your shell — fish, in this environment — with everything from steps 1–5 active. When you type exit, you are back on your host machine and nothing was installed on it.

One-time host setup

A few things cannot be done from inside a container — installing Docker itself, or the udev rules that let you flash a board over USB without sudo. Those live in scripts: setup: and are not run on every start. Run them once, explicitly:

denver examples/zephyr-devshell-4.3.1 --run setup

First run vs. every run after

The first run is slow: the image is built, packages are downloaded, the Zephyr repos are cloned. Later runs are fast, because every stage checks whether its inputs changed (via checksums and fingerprints) and skips its expensive work if they didn't. So starting the environment again is normally just a few seconds.

Two useful flags around this:

# don't build anything, only activate what already exists (fastest)
denver examples/zephyr-devshell-4.3.1 --fast

# ignore all "nothing changed" shortcuts and redo the expensive work
denver examples/zephyr-devshell-4.3.1 --force

The handful of options you'll actually use

# run ONE command inside the environment instead of opening a shell.
# everything after '--' is passed through untouched.
denver examples/zephyr-devshell-4.3.1 -- echo 123

# don't use docker; run the same stack directly on your host
denver examples/zephyr-devshell-4.3.1 --skip docker

# stop after a given stage: that stage and every stage before it runs.
# (there is no "run just this one stage" -- a stage practically always
# needs its predecessors: uv needs conan's tools, zephyr needs uv's west)
denver examples/zephyr-devshell-4.3.1 --until uv

# print the final, fully merged configuration and exit -- the best way to
# understand what an environment really does, imports included
denver examples/zephyr-devshell-4.3.1 --show-config

# quieter output (-q keeps stage banners, -qq silences denver completely)
denver examples/zephyr-devshell-4.3.1 -qq -- west --version

Why is zephyr-devshell-4.3.1/denver.yml so short?

If you open it, you'll find under 60 lines — half of them comments, and no stages: list or docker config at all. That's because of import:, denver's inheritance mechanism:

import:
- ../zephyr-devshell     # inherit that env's entire setup as a base

The chain looks like this:

examples/zephyr-docker/          "how to build & enter the container"
        ▲ imported by
examples/zephyr-devshell/        the shared base: the 5-stage pipeline,
        ▲ imported by        shared conan recipes, common env variables
examples/zephyr-devshell-4.3.1/  ONLY the 4.3.1-specific bits:
                             pinned requirements, conanfile, blob list

So a Zephyr 4.4.0 environment would be a new folder whose denver.yml imports the very same base and changes only the pinned versions — no copy-pasting of the docker/conan/uv setup. (The shared base sets runnable: false, so starting it directly is rejected: it is ingredients, not a meal.)

Where to go next

• Curious what a minimal environment looks like? examples/zephyr-uv/ is nothing but a Python venv, and examples/simple-env/ just runs a shell script.
• Writing your first own env? Start from Step 1 above and add a stage only when you hit the problem it solves — most environments never need all five.
• Want to write your own denver.yml? doc/how-to.md walks through building one from an empty folder, stage by stage; doc/architecture.md documents every key of the schema; or copy the closest examples/*/denver.yml.
• Want details on one stage type's config keys? Each provider has its own reference under doc/providers/ — e.g. doc/providers/docker.md for every docker: key — and a terser lookup table in its module docstring next to the code (src/providers/docker.py).

You wanna set up your own environment?

Then start with doc/how-to.md. It documents how a denver.yml is created from scratch for a common use case.

Helpful: The result of this is stored under: examples/howto-env/

Environment variables

denver reads exactly one environment variable of its own:

• DENVER_STATE_DIR — where denver writes its per-env state (venvs, conan caches, performance.jsonl, ...) when running from an installed package rather than a checkout. Defaults to ~/.denver. Running from a source checkout (or an editable install) instead uses the checkout root, matching every example above — this variable only matters once denver is installed as a regular (non-editable) package.

Every other flag (--force, --ci, ...) is set purely by its own CLI flag, never inherited from a same-named real environment variable — see denver --help.

denver also exports a handful of built-in variables into the environment it builds (DENVER_ENV_DIR, DENVER_ENV_NAME, ...), usable in ${...} interpolation inside a denver.yml — see "Variable interpolation" in doc/architecture.md.

Full flag reference

denver --help lists every flag; the notes below are for the ones whose behavior isn't obvious from a one-line description.

• -c/--config KEY.PATH=VALUE overrides a single value in the merged denver.yml (e.g. -c uv.python=3.13); any missing parent section is created as an empty mapping. KEY.PATH+=VALUE appends to an existing list/string/number instead of replacing it (behaves like = if the path doesn't exist yet). VALUE is parsed as YAML, so "true"/"3"/"[a, b]" become their real type, not a string. Repeatable; later -cs win when they target the same path.
• -cf/--config-file FILE overlays a whole YAML file on top of the env's denver.yml, using the same merge rules as import:. Repeatable, applied in the order given; -c overrides are applied last, on top of every -cf file.
• --until <stage> truncates the pipeline: every stage up to and including <stage> runs, everything after it is dropped — there's no "run only this one stage" flag, since a stage practically always needs the ones before it. The command (if any) still runs afterwards, in whatever partial environment those stages built.
• --skip <stage> removes individual stages from whatever --until left; repeatable. Skipping a wrapper stage (--skip docker) is how you run the stack directly on the host instead of relocating into a container.
• --run <name> runs every (filtered) stage's own scripts: <name>: list, then exits without running the rest of the pipeline — see "One-time host setup" above for an example. <name> is open-ended, not a fixed set of flags: a project can declare scripts: migrate: and run denver <env> --run migrate without denver itself changing.
• -q/-qq are two quiet levels. -q silences info lines, + cmd echoes, and build-tool subprocess output, but leaves each stage's own banner and "stage finished" summary visible. -qq additionally silences those too, so only the launched command's own output reaches the terminal. Errors are always reported.
• --fast skips every provider's (re-)build step and only activates what a previous full run already built (each provider's own page under doc/providers/ documents exactly what that means for it). Run once without --fast first — a provider dies with a clear message if what it needs isn't there yet.
• --force forces a provider to redo expensive work it would otherwise skip because nothing looked like it changed (again, see each provider's own page for specifics). Like --ci below, this is only ever set by the flag itself, never inherited from a same-named real environment variable.
• --ci swaps in narrower/faster args a provider judges appropriate for a CI runner (currently just zephyr's west update, adding a shallow-clone strategy on top of whatever update-args: already configures).
• --version prints the running denver's version and exits — derived from the checkout's git tags when denver runs from a checkout (script or editable install), otherwise from the installed package's metadata. A checkout ahead of its last tag reports as a development build of the release it is heading for (1.1.0-17-gabc1234). A denver.yml can require a minimum with denver-version: ">=1.1.0", and is rejected up front by a denver older than that (see doc/architecture.md).

Each stage's runtime is also appended to <DENVER_DIR>/.envs/<env>/performance.jsonl as JSON Lines of Chrome Trace Event Format events — concatenate them into a {"traceEvents": [...]} document to load at chrome://tracing or https://ui.perfetto.dev.

Quick start

The same commands as above, as a cheat sheet:

# run the according setup scripts to install (host) requirements for this env
denver examples/zephyr-devshell-4.3.1 --run setup

# run the according login scripts from this env
denver examples/zephyr-devshell-4.3.1 --run login

# start the env (default command, which is fish)
denver examples/zephyr-devshell-4.3.1

# run a specific command in the development environment instead
denver examples/zephyr-devshell-4.3.1 -- echo 1

# same stack, but on the host instead of in docker
denver examples/zephyr-devshell-4.3.1 --skip docker

(If denver is vendored via git-nested, call src/denver.py directly instead.)

Known limitations

Every place denver runs needs import yaml to work. denver is a Python program with exactly one runtime dependency, PyYAML, so installing it from PyPI covers the host automatically. What it cannot cover is a wrapped environment: a docker stage relocates the rest of the stack into the container and re-invokes denver in there with the image's own bare python3 — an interpreter that knows nothing about the install on your host. That image must supply PyYAML itself:

RUN apt-get update && apt-get install -y --no-install-recommends \
      python3 python3-yaml \
    && rm -rf /var/lib/apt/lists/*

If it doesn't, denver stops with an error naming the interpreter that failed to import yaml.

Which PyYAML that is, is deliberately not pinned. The host copy and the image copy are resolved by two different package managers and will drift apart in general. denver accepts that: it calls exactly two functions from the library, yaml.safe_load and yaml.safe_dump, whose behaviour has been stable across PyYAML releases for years. So any reasonably recent PyYAML is fine, and denver imports whatever it finds instead of demanding a particular version and forcing every image to track it. The declared dependency is a floor (pyyaml>=6), not a pin — and it constrains only the host install anyway, never the image's.

Contributing

Bug reports, feature requests and pull requests are welcome — see doc/development.md for the workflow (uv run poe all runs lint, format, mypy and the test suite; denver keeps 100% coverage).

License

MIT — see LICENSE.