- Create a separate branch for your usecase
- Rebase with main branch
- Raise PR (DO NOT commit to main)
- Clone the repo
- Create the env using the above commands
- Install the package
- All the tests goes into the
tests
directory - Any test experiments eg-
Data wraggling/inital exploration notebooks
goes underexperiments
directory - All modelling and related details into
analysis_framework
, create subdirectories as required - Under forecasting_framework there are three submodules
utils
,data
andpipelines
utils
reusable code componentsdata
raw dataset are storedpipeline
dagster pipeline and related computation output
- Set up authentication using OAuth, SSH or push token
# for the first time
git clone <repo>
# change working directory to root of repo cloned
cd <repo location>
conda env create --file environment.yml
conda activate capstone
conda list
conda info
pip install -e .
conda env update --file environment.yml --prune
conda deactivate
Just like the name suggests, precommit-hooks are designed to format the code based on PEP standards before committing. More details 🗒
pip install pre-commit
pre-commit install
"""---commit your changes---
- check for errors, hooks format the code by deafult
- add the files
- commit it again
- push the changes
"""
- Built using
Dagster
, an open source orchestration tool
Commands to trigger the pipeline
- Command to run the pipeline from root
capstone_isb
python analysis_framework/pipeline/process.py
- Command to view the dagit UI
dagit -f analysis_framework/pipeline/process.py
- Tools used - Panel, Holoviews
- Main files
app.py
(panel serve requirements and layout components) andbase.py
(contains all the logic) - Command to trigger the dashboard (subject to change) -
python analysis_framework/dashboard/app.py