Pyronear: Machine Learning Pipeline for Wildfire Detection 🔥

Machine Learning Pipeline for Wildfire Detection.

Setup

Dependencies

• Poetry: Python packaging and dependency management - Install it with something like pipx
• Git LFS: Git Large File Storage replaces large files such as jupyter notebooks with text pointers inside Git while storing the file contents on a remote server like github.com
• DVC: Data Version Control - This will get installed automatically
• MLFlow: ML Experiment Tracking - This will get installed automatically

Install

Poetry

Follow the official documentation to install poetry.

Git LFS

Make sure git-lfs is installed on your system.

Run the following command to check:

git lfs install

If not installed, one can install it with the following:

Linux

sudo apt install git-lfs
git-lfs install

Mac

brew install git-lfs
git-lfs install

Windows

Download and run the latest windows installer.

Project Dependencies

Create a virtualenv and install python version with conda - or use a combination of pyenv and venv:

conda create -n pyronear-mlops python=3.12

Activate the virtual environment:

conda activate pyronear-mlops

Install python dependencies

poetry install

Data Dependencies

To get the data dependencies one can use DVC - To fully use this repository you would need access to our DVC remote storage which is currently reserved for Pyronear members. On request, you will be provided with AWS credentials to access our remote storage.

Once setup, run the following command:

dvc pull

Setup S3 access

Create the following file ~/.aws/config:

[profile pyronear]
region = eu-west-3

Add your credentials in the file ~/.aws/credentials - replace XXX with your access key id and your secret access key:

[pyronear]
aws_access_key_id = XXX
aws_secret_access_key = XXX

Make sure you use the AWS pyronear profile:

export AWS_PROFILE=pyronear

Project structure and conventions

The project is organized following mostly the cookie-cutter-datascience guideline.

Data

All the data lives in the data folder and follows some data engineering conventions.

Library Code

The library code is available under the pyronear_mlops folder.

Notebooks

The notebooks live in the notebooks folder. They are automatically synced to the Git LFS storage. Please follow this convention to name your Notebooks.

<step>-<ghuser>-<description>.ipynb - e.g., 0.3-mateo-visualize-distributions.ipynb.

Scripts

The scripts live in the scripts folder, they are commonly CLI interfaces to the library code.

DVC

DVC is used to track and define data pipelines and make them reproducible. See dvc.yaml.

To get an overview of the pipeline DAG:

dvc dag

To run the full pipeline:

dvc repro

MLFlow

An MLFlow server is running when running ML experiments to track hyperparameters and performances and to streamline model selection.

To start the mlflow UI server, run the following command:

make mlflow_start

To stop the mlflow UI server, run the following command:

make mlflow_stop

To browse the different runs, open your browser and navigate to the URL: http://localhost:5000

Contribute to the project

New ML experiments

Follow the steps:

1. Work on a separate git branch: git checkout -b "<user>/<experiment-name>"
2. Modify and iterate on the code, then run dvc repro. It will rerun parts of the pipeline that have been updated.
3. Commit your changes and open a Pull Request to get your changes approved and merged.

Run Random Hyperparameter Search

YOLOv8

Use the following commands to run random hyperparameter search:

make run_yolov8_hyperparameter_search

It will run 100 random training runs with hyperparameters drawn from the hyperparameter space defined in pyronear_mlops/model/yolo/hyperparameters/yolov8.py

YOLOv9

Use the following commands to run random hyperparameter search:

make run_yolov9_hyperparameter_search

It will run 100 random training runs with hyperparameters drawn from the hyperparameter space defined in pyronear_mlops/model/yolo/hyperparameters/yolov9.py

YOLOv10

Use the following commands to run random hyperparameter search:

make run_yolov10_hyperparameter_search

It will run 100 random training runs with hyperparameters drawn from the hyperparameter space defined in pyronear_mlops/model/yolo/hyperparameters/yolov10.py

Generate a benchmark CSV file

YOLOv8

make yolov8_benchmark

YOLOv9

make yolov9_benchmark

YOLOv10

make yolov10_benchmark