This repository makes use of Git
version control. Precommit
hooks have been used to provide checks to the developer on the heath and conformity of their code. Here, we make use of Black
for automatic formatting, flake8
for linting and jupytext
to automatically convert our notebooks to py:percent
files for better version control. Our environment has been setup using Anaconda
. You can find an explicit environment file in the src
directory. We have provideded a Dockerfile
and compose to aid in portabilit of our environment. This project makes use of kedro
for data versioning, project templating, data pipelining, automatic documentation, data catalogingm packaging and context management. Please refer to conf/base/catalog
for speific details on how our data and models were extracted, serialized and versioning. The majority of our analysis has relied on consumer hardware with 16GB of Micron MT53E1G32D4NQ-046 memory clocked at 4267MHz and a Quadcore Intel R Core TM i7-1065G7 processor clocked at 1.30GHz, running Ubuntu 20.04 using linux kernel 5.4.0-48-generic. At the release of our work, we are unaware of any known issues in the software and hardware used by our analysis which may affect the reliability of our results.
We have relied on MLFLOW (https://www.mlflow.org/docs/latest/models.html) and kedro-mlflow (https://github.com/Galileo-Galilei/kedro-mlflow) for model serving, which provide a simple specification for model serving on Azure, Sagemaker and Databricks. You can launch our baseline model as a REST API locally using mlflow models serve -m mlruns/1/.
and navigate tracked artefacts using kedru mlflow ui
.
This is your new Kedro project, which was generated using Kedro 0.16.5
.
Take a look at the Kedro documentation to get started.
In order to get the best out of the template:
- Don't remove any lines from the
.gitignore
file we provide - Make sure your results can be reproduced by following a data engineering convention
- Don't commit data to your repository
- Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in
conf/local/
Declare any dependencies in src/requirements.txt
for pip
installation and src/environment.yml
for conda
installation.
To install them, run:
kedro install
You can run your Kedro project with:
kedro run
Have a look at the file src/tests/test_run.py
for instructions on how to write your tests. You can run your tests as follows:
kedro test
To configure the coverage threshold, go to the .coveragerc
file.
To generate or update the dependency requirements for your project:
kedro build-reqs
This will copy the contents of src/requirements.txt
into a new file src/requirements.in
which will be used as the source for pip-compile
. You can see the output of the resolution by opening src/requirements.txt
.
After this, if you'd like to update your project requirements, please update src/requirements.in
and re-run kedro build-reqs
.
Further information about project dependencies
Note: Using
kedro jupyter
orkedro ipython
to run your notebook provides these variables in scope:context
,catalog
, andstartup_error
.Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run
kedro install
you will not need to take any extra steps before you use them.
To use Jupyter notebooks in your Kedro project, you need to install Jupyter:
pip install jupyter
After installing Jupyter, you can start a local notebook server:
kedro jupyter notebook
To use JupyterLab, you need to install it:
pip install jupyterlab
You can also start JupyterLab:
kedro jupyter lab
And if you want to run an IPython session:
kedro ipython
You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.
By adding the node
tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/
:
kedro jupyter convert <filepath_to_my_notebook>
Note: The name of the Python file matches the name of the original notebook.
Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:
kedro jupyter convert --all
To automatically strip out all output cell contents before committing to git
, you can run kedro activate-nbstripout
. This will add a hook in .git/config
which will run nbstripout
before anything is committed to git
.
Note: Your output cells will be retained locally.
Further information about building project documentation and packaging your project