Installation¶
Shorkie’s model code lives in two pinned forks — baskerville-yeast and westminster — which ship as submodules of the
shorkie-paper repository. That repo
also provides an installable helper package (shorkie), the release catalogue,
the examples, and the figure notebooks.
Requirements¶
Component |
Requirement |
|---|---|
OS |
Linux |
Python |
3.9 (pinned by |
TensorFlow |
~2.15 |
CPU |
Enough for inference, variant scoring, and reproducing the figures from released data |
GPU |
Needed only for training / fine-tuning and a few GPU-marked figure panels.
Those also need |
Note
Everything on this site that is labelled CPU was verified on CPU from a clean download — no GPU and no cluster access required.
Install¶
git clone --recurse-submodules https://github.com/calico/shorkie-paper.git
cd shorkie-paper
conda env create -f environment.yml
conda activate yeast_ml
# model code first, then the helper package
pip install -e external/baskerville-yeast -e external/westminster -e .
If you cloned without --recurse-submodules:
git submodule update --init
Configure paths¶
Every filesystem path in the repository resolves through config/paths.yaml —
there are no hardcoded machine paths in the pipeline code.
cp config/paths.example.yaml config/paths.yaml
The defaults already point at the layout data/download.sh creates, so for
normal use you only need to set release_root (where downloads land). Resolution
order is $SHORKIE_CONFIG → config/paths.yaml → config/paths.example.yaml.
from shorkie import config
config.path("models.shorkie_finetuned") # -> Path to the 8-fold ensemble
config.path("genome.fasta") # -> Path to the R64 FASTA
Containers¶
containers/ ships a Dockerfile and an apptainer.def that build the
environment and both submodules, for a scheduler-free run.
Verify the install¶
bash scripts/00_setup/verify_install.sh # imports shorkie, resolves keys, runs pytest
pytest -q # release-integrity + smoke tests
Next: Quick start.