MindsDB's is an Explainable AutoML framework for developers.

MindsDB Native

With MindsDB Native you can build, train and use state of the art ML models in as simple as one line of code.

• Installing MindsDB Native
• Learning from Examples
• MindsDB Explainability GUI
• Frequently Asked Questions
• Provide Feedback to Improve MindsDB

Try it out

Installation

• Desktop: You can use MindsDB on your own computer in under a minute, if you already have a python environment setup, just run the following command, otherwise simply follow the installation instructions Complete Manual.

 pip3 install mindsdb --user

• Docker: If you would like to run it all in a container simply:

sh -c "$(curl -sSL https://raw.githubusercontent.com/mindsdb/mindsdb/master/distributions/docker/build-docker.sh)"

Usage

Once you have MindsDB installed, you can use it as follows:

Import MindsDB:

from mindsdb import Predictor

One line of code to train a model:

# tell mindsDB what we want to learn and from what data
Predictor(name='home_rentals_price').learn(
    to_predict='rental_price', # the column we want to learn to predict given all the data in the file
    from_data="https://s3.eu-west-2.amazonaws.com/mindsdb-example-data/home_rentals.csv" # the path to the file where we can learn from, (note: can be url)
)

One line of code to use the model:

# use the model to make predictions
result = Predictor(name='home_rentals_price').predict(when={'number_of_rooms': 2,'number_of_bathrooms':1, 'sqft': 1190})

# you can now print the results
print('The predicted price is ${price} with {conf} confidence'.format(price=result[0]['rental_price'], conf=result[0]['rental_price_confidence']))

Visit the documentation to learn more

• Google Colab: You can also try MindsDB straight here

Video Tutorial

Please click on the image below to load the tutorial:

(Note: Please manually set it to 720p or greater to have the text appear clearly)

MindsDB Lightwood: Machine Learning Lego Blocks

Under the hood of mindsdb there is lightwood, a Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamleasly. More info about MindsDB lightwood's on GITHUB.

Contributing

In order to make changes to mindsdb, the ideal approach is to fork the repository than clone the fork locally PYTHONPATH.

For example: export PYTHONPATH=$PYTHONPATH:/home/my_username/mindsdb.

To test if your changes are working you can try running the CI tests locally: cd tests/ci_tests && python3 full_test.py

Once you have specific changes you want to merge into master, feel free to make a PR.

Report Issues

Please help us by reporting any issues you may have while using MindsDB.

https://github.com/mindsdb/mindsdb/issues/new/choose