README.md

metadata

license: apache-2.0

Set the Cookiecutter-MLOps in Hugging Face

1. Create Model repository in Hugging Face (e.g. myHFrepo)
2. In your local directory run:

'''bash cd /path/to/parent directory of project folder git clone git@hf.co:USERNAME/myHFrepo '''

For ssh connection check here
cd myHFrepo
python -m venv jointvenv

source jointvenv/bin/activate

Follow instructions on DagsHub/Cookiecutter-MLOps | DagsHub git clone https://dagshub.com/DagsHub/Cookiecutter-MLOps.git rm -r /path/to/myHFrepo/Cookiecutter-MLOps/.git cat /path/to/myHFrepo/Cookiecutter-MLOps/.gitattributes >> /path/to/myHFrepo/.gitattributes rm /path/to/myHFrepo/Cookiecutter-MLOps/.gitattributes cat /path/to/myHFrepo/Cookiecutter-MLOps/README >> /path/to/myHFrepo/README rm /path/to/myHFrepo/Cookiecutter-MLOps/README git add README.md git commit -m "Paste README info from DagsHub/Cookiecutter-MLOps" git add .gitattributes git commit -m "Paste .gitattributes info from DagsHub/Cookiecutter-MLOps" cd /path/to/myHFrepo/Cookiecutter-MLOps mv * .[^.]* .. cd /path/to/myHFrepo rm -r /path/to/myHFrepo/Cookiecutter-MLOps echo '' >> .gitignore echo '#'Virtual Environment >> .gitignore echo jointvenv/ >> .gitignore git add . git commit -m "add remaining DagsHub/Cookiecutter-MLOps repo content" make dirs make requirements mv requirements.txt requirementsCookiecutter-MLOps.txt git add requirementsCookiecutter-MLOps.txt git commit -m "external requirements from Cookiecutter- MLOps" pip freeze > requirements.txt git add requirements.txt git commit -m "First report venv requirements" git push origin main

Create Model repository in your Hugging Face organization (e.g. myHFrepo)

git remote add dcc git@hf.co:MYORG/mywslHFrepo git pull dcc main --allow-unrelated-histories Resolve conflicts in .gitattributes and README.md git add . git commit -m "Merge HuggingFace individual and organization repos" git push dcc main

Cookiecutter-MLOps

A cookiecutter template employing MLOps best practices, so you can focus on building machine learning products while having MLOps best practices applied.

Instructions

1. Clone the repo.
2. Run make dirs to create the missing parts of the directory structure described below.
3. Optional: Run make virtualenv to create a python virtual environment. Skip if using conda or some other env manager.
   1. Run source env/bin/activate to activate the virtualenv.
4. Run make requirements to install required python packages.
5. Put the raw data in data/raw.
6. To save the raw data to the DVC cache, run dvc add data/raw
7. Edit the code files to your heart's desire.
8. Process your data, train and evaluate your model using dvc repro or make reproduce
9. To run the pre-commit hooks, run make pre-commit-install
10. For setting up data validation tests, run make setup-setup-data-validation
11. For running the data validation tests, run make run-data-validation
12. When you're happy with the result, commit files (including .dvc files) to git.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make dirs` or `make clean`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump
│
├── 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
│   └── metrics.txt    <- Relevant metrics after evaluating the model.
│   └── training_metrics.txt    <- Relevant metrics from training the model.
│
├── 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
│   │   ├── great_expectations  <- Folder containing data integrity check files
│   │   ├── make_dataset.py
│   │   └── data_validation.py  <- Script to run data integrity checks
│   │
│   ├── 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
│
├── .pre-commit-config.yaml  <- pre-commit hooks file with selected hooks for the projects.
├── dvc.lock           <- constructs the ML pipeline with defined stages.
└── dvc.yaml           <- Traing a model on the processed data.

---

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

---

To create a project like this, just go to https://dagshub.com/repo/create and select the Cookiecutter DVC project template.

Made with 🐶 by DAGsHub.