A machine learning library written from scratch - with runtime switchable backend!

Provides a single interface to interact with single-core CPU operations (with NumPy backend), as well as thousands of cores on a GPU (with CuPy backend), in runtime!

NOTE: This is NOT an alternative to libraries like scikit-learn and cuML. Their interfaces are complete on their own!

swi-ml is built on bare Python and NumPy backbones, all other dependencies are optional!

• NumPy
• CuPy (Optional)
• Matplotlib (Optional)

1. (Optional) Setup a virtual environment using virtualenv or anaconda.
2. Install NumPy by following their insallation guide or simply via pip:

   pip install numpy

3. (Optional) For GPU-supported backend, setup a working installation of CuPy by following their installation guide.

   python -c 'import cupy; cupy.show_config()'

4. (Optional) Install Matplotlib to plot specific curves. (via their installation guide)
5. Install swi-ml:

   pip install swi-ml  # from PyPI
   pip install git+https://github.com/aitikgupta/swi-ml  # from GitHub

6. (Optional) To run the pre-defined tests, install pytest by following their installation guide or simply via pip:

   pip install pytest

from swi_ml import set_backend

# numpy backend (CPU)
set_backend("numpy")

# cupy backend (GPU)
set_backend("cupy")

Don't have a physical GPU, or don't know if you have a proper setup for a GPU-enabled backend?

Set automatic fallback (to NumPy - the only hard dependency):

from swi_ml import set_automatic_fallback

# this has been enabled by default for tests
# see https://github.com/aitikgupta/swi-ml/blob/master/tests/__init__.py
set_automatic_fallback(True)

from swi_ml.regression import LinearRegressionGD

data = [[1], [2], [3]]
labels = [2, 4, 6]

model = LinearRegressionGD(
    num_iterations=3,
    learning_rate=0.1,
    normalize=False,
    initialiser="uniform",
    verbose="DEBUG",
)

model.fit(data, labels)

print("Current MSE:", model.curr_loss)

INFO: Backend is not set, using default `numpy`
INFO: Setting backend: numpy
INFO: MSE (1/3): 13.93602
INFO: MSE (2/3): 0.22120
INFO: MSE (3/3): 0.05478
INFO: Training time: 0.00035 seconds
Current MSE: 0.054780625247184585

For more concrete examples, please refer to examples directory.

To run the testing suite, execute the following command in the root directory:

python -mpytest  # run the whole suite
python -mpytest tests/test_module.py  # run the specific test module

Contributions are what makes the open source community such an amazing place to learn, inspire, and create. Any contributions are much appreciated!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Distributed under the MIT License. See LICENSE for more information.

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