chuchichaestli
chuchichaestli is a collection of model architectures and other
useful bits of code in use at the Intelligent Vision Systems group at
the University of Applied Sciences Zurich (ZHAW).
Installation
Tagged releases are available as PyPI packages. To install the latest package, run:
pip install chuchichaestli
For the bleeding-edge package directly from a git branch, clone the repository and run the following command in the root directory of the repository:
pip install -e .
Alternatively, you can install the package directly from GitHub:
pip install git+https://github.com/CAIIVS/chuchichaestli.git
Note: we recommend uv for running
examples in
examples/
or benchmarks in
benches/.
Native kernels
chuchichaestli provides optional, optimized, custom CPU and GPU
kernels. Without them everything still works, just a little slower.
They are compiled on first use, against the installed torch, which
takes a C++ compiler (gcc on Linux, clang on macOS, MSVC on
Windows) plus:
pip install chuchichaestli[jit]
That yields the CPU kernels. The custom GPU kernels additionally need
nvcc or hipcc; without one the build quietly settles for CPU.
Run
chuchichaestli-build-kernels
to compile up front instead, or set C3LI_JIT_KERNELS=0 to skip them
entirely.
Benchmarks
The native kernels are benchmarked against the pure-torch path and against external packages, on both CPU and GPU, with every backend checked against a reference before it is timed; see Benchmarks for how to run them, the measured results, and the profiler tracing.
Development
Releases and packages are automatically created with various GitHub action workflows. The general development workflow is as follows
- checkout to a non-default branch to apply patches, features, etc.
- each commit to a non-default branch triggers an install test (on latest Python versions) and a package build
- open a PR
- at opening and each subsequent commit to that PR, the build is uploaded to TestPyPI and a dev version is incremented (e.g. v1.2.0 -> v1.2.0-dev0)
- once the PR is reviewed, it can be merged
- at merge, the micro version is incremented on the main branch and tagged (e.g. v1.2.0-dev7 -> v1.2.1)
- subsequently, a package is built, published to PyPI, and released on GitHub with the latest version tag
- every once in a while it is necessary to bump minor or major versions
- minor and major version can be triggered, by manually dispatching the
on_dispatch.ymlworkflow - on the web interface (using the option
minorormajor), or - on CLI with
gh workflow run on_dispatch.yml -f type=minor(or-f type=major)
Reusable workflows
In .github/workflows/ there are several reusable workflows which
provide the basic utility for the triggered jobs:
test-install.yml- test install on the supported Python endpoints, 3.10 and 3.13, by default;
pass
full-matrix: trueto sweep every supported version (the release workflow does) - ruff linting checks (stop build if error occurs)
- run unit tests with pytest and collect coverage (fails below the
coverage-thresholdinput) - upload test results (for prosperity)
- test install on the supported Python endpoints, 3.10 and 3.13, by default;
pass
build-package.yml- build package with
uv buildon Python 3.13 by default; passversions(a JSON array) to build on several, if wheels are ABI-specific - upload one dist artifact per Python version, named
<artifact-name>-<version>
- build package with
github-release.yml- download all dist artifacts matching
<artifact-name>-*and merge them - sign package dist with Sigstore
- create and upload GitHub release
- download all dist artifacts matching
publish-to-pypi.yml- download all dist artifacts matching
<artifact-name>-*and merge them - publish to PyPI, or to TestPyPI when called with
test: true
- download all dist artifacts matching
deploy-docs.yml- build the docs with MkDocs and publish them to GitHub Pages
- uses the Pages artifact flow (
upload-pages-artifact+deploy-pages), not agh-pagespush - can be run manually via
gh workflow run deploy-docs.yml
phdenzel/pyverto@v*- use pyverto to increment a version
- commit and push changes
Triggered workflows
on_branches.yml- triggers on commit to any branch (except
main) not in a PR (pull request) - runs
test-install-python-versionandbuild-package
- triggers on commit to any branch (except
on_pr.yml- triggers on commit to a PR
- runs
test-install-python-version,build-package,publish-to-testpypi, andversion-bump-dev
on_merge.yml- triggers on PR merge
- runs
version-bump-on-merge(increments micro version on main)
on_push.yml- triggers on push to the main branch upon automatic version change
- runs
test-install-python-version,build-package,publish-to-pypi,github-release,deploy-docs
on_dispatch.yml- triggers on dispatch (e.g. by running
gh workflow run on-dispatch.yml -f type=minor) - runs
version-bump(increments chosen type of version on main)
- triggers on dispatch (e.g. by running
Metadata
Release files for chuchichaestli 0.4.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| chuchichaestli-0.4.4.tar.gz | 265.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| chuchichaestli-0.4.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 612.6 kB
Release files / chuchichaestli-0.4.4.tar.gz
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| Size | 265.3 kB |
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Release files / chuchichaestli-0.4.4-py3-none-any.whl
| Download URL | chuchichaestli-0.4.4-py3-none-any.whl |
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| Uploaded via |
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