This code lets you set up your own Github repo to run pyperformance benchmarks on your own self-hosted Github Action runners.
Add bench_runner to your requirements. Since there are no PyPI releases yet, you can install it from a tag in the git repo:
git+https://github.com/faster-cpython/bench_runner@v0.2.2#egg=bench_runner
Create a virtual environment and install your requirements to it.
Run the install script to generate the files to make the Github Actions work (from the root of your repo):
$ python -m bench_runner.scripts.install
This will create some files in .github/workflows
as well as some configuration files at the root of your repo.
Commit them to your repository.
Provision the machine according to the provisioning instructions.
Then, add it to the pool of runners by following the instructions on Github's
Settings -> Actions -> Runners -> Add New Runner
to add a new runner.
The default responses to all questions should be fine except pay careful attention to set the labels correctly. Each runner must have the following labels:
- One of
linux
,macos
orwindows
. bare-metal
(to distinguish it from VMs in the cloud).$os-$arch-$nickname
, where:$os
is one oflinux
,macos
,windows
$arch
is one ofx86_64
orarm64
(others may be supported in future)$nickname
is a unique nickname for the runner.
Once the runner is set up, enable it as a service so it will start automatically on boot.
In addition, the metadata about the runner must be added to runners.ini
, for example:
[linux]
os = linux
arch = x86_64
hostname = pyperf
TODO: Describe the special pystats runner
By default, all of the benchmarks in pyperformance
and python-macrobenchmarks
are run. To configure the set of benchmarks, or add more, edit the benchmarks.manifest
file.
The format of this file is documented with pyperformance
.
All benchmarked commits are automatically compared to key "reference" versions, as well as their merge base, if available.
The reference versions are defined in the bases.txt
file.
Don't forget to actually collect benchmark data for those tags -- it's doesn't happen automatically.
TODO: The longitudinal plot isn't currently configurable.
To learn how to hack on this project, see the full developer documentation.