dagster-mlflow-exemple

Multiple scikit learn model are laucnhed and tracked into mlflow instance

A short test for mlflow integration to dagster

I follow this simple tutorial from Jonathon Bechtel which test multiple models in a loop. I wanted to implement it with dagster and maybe at the end to play it together with mlflow.

Configuration of solids:

solids:
  decision_tree:
    config: "Decision Tree"
  gradient_boosting_classifier:
    config: "Gradient Boosting Classifier"
  linear_svm:
    config: "Linear SVM"
  logistic_regression:
    config: "Logistic Regression"
  naive_bayes:
    config: "Naive Bayes"
  nearest_neighbors:
    config: "Nearest Neighbors"
  random_forest:
    config: "Random Forest"

Sensor launch a solid when mlflow register a new model

Todo list:

• Add mlflow to log models
• Attached a VOLUME to dagster Docker image to refresh repo.py / config.yaml more dynamically.
• See if it is possible to integrate solid configuration dynamically, e.g. another solid can get the list of parameters from a file then pass it to dynamically launch multiple solids (models)
• Launch solids in parallel
• Trigger dagster when a new parameter is registered.
• Construct solid dynamically from an mlflow model signature. Ex? With papermill.
• Test sensors event based triggering

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

---

Project based on the cookiecutter data science project template. #cookiecutterdatascience