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Utilities of MLOps for INRIA

Project description


This is a set of utilities for end-to-end lifecycle of Machine Learning applications in Python for Inria-Chile. Could be used like a framework and tooling for rapid development of data science and MLOps functions.

Here’s the folder structure for the repository:

├── data/
│   ├── prepared/
│   └── raw/
│   └── metrics/
├── model/
├── mlops/
|   └── *.py
├── test/
|   └── test_*.py
├── venv/
├── requirements.txt
├── s3*.py

There are the folloging folders in our repository:

  • data/ is all versions of the dataset.
  • data/raw/ is the data obtained from an external source.
  • data/prepared/ is for data modified internally.
  • data/metrics/ is for tracking the performance metrics of our models.
  • model/ is for machine learning models.
  • mlops/ is the source code. Python classes used by the Python scripts.
  • test/ is the pyunit of source code.
  • venv/ is the virtual environment.
  • bash for check the AWS credentials.
  • bash for check the installation.
  • requirements.txt the dependencies.
  • s3*.py Python scripts for bash executions.
  • the configuration of the package.

Installation Linux

Perform the following steps in order to install this program. Replace 'access_key_id' and 'secret_access_key' by the account AWS Credentials associated to poc-inriacl.

git clone
./common-mlops/ access_key_id secret_access_key
pip3 uninstall common-mlops
# Version of test
pip3 install -i common-mlops
# Version of production
pip3 install common-mlops

Updates in

Perform the following steps in order to publish new versions to

sudo rm -rf build common_mlops.egg-info dist
bumpversion --current-version 1.0.0 minor mlops/
python3 sdist bdist_wheel
# Version of test
twine check dist/*
twine upload --repository-url dist/*
# Version of production
twine upload dist/*


Run the unit tests:

python3 -m unittest


See the example of usage in:


Build the image:

docker build -t common-mlops .
docker pull common-mlops

Run the image:

docker run -v$HOME/.aws:/root/.aws:ro -e S3_ACCESS_KEY_ID=access_key_id -e S3_SECRET_ACCESS_KEY=secret_access_key -e AWS_ACCESS_KEY_ID=access_key_id -e AWS_SECRET_ACCESS_KEY=secret_access_key common-mlops .


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