setup.py

from setuptools import setup

setup(
   name='torch_maml',
   version='1.0',
   description='Gradient checkpointing technique for Model Agnostic Meta Learning',
   long_description='PyTorch implementation of Model Agnostic Meta Learning with gradient checkpointing. Allows you to perform way (~10-100x) more MAML steps with the same GPU memory budget.',
   author='Dmitry Baranchuk',
   author_email='dmitry.baranchuk@graphics.cs.msu.ru',
   packages=['torch_maml'],
   license='MIT',
   install_requires=['torch>=1.1.0'],
   classifiers=[
     'Development Status :: 5 - Production/Stable',
      'Intended Audience :: Developers',
      'Intended Audience :: Education',
      'Intended Audience :: Science/Research',
      'License :: OSI Approved :: MIT License',
      'Programming Language :: Python :: 3',
      'Programming Language :: Python :: 3.5',
      'Programming Language :: Python :: 3.6',
      'Programming Language :: Python :: 3.7',
      'Topic :: Scientific/Engineering',
      'Topic :: Scientific/Engineering :: Mathematics',
      'Topic :: Scientific/Engineering :: Artificial Intelligence',
      'Topic :: Software Development',
      'Topic :: Software Development :: Libraries',
      'Topic :: Software Development :: Libraries :: Python Modules',
    ],

    # What does your project relate to?
    keywords='meta-learning, maml, pytorch, torch, deep learning, machine learning, gradient checkpointing, gpu',
)