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alt=\"logo\" /\u003e\n\u003c/center\u003e\n\n# OpenSpiking\n\n![GitHub last commit](https://img.shields.io/github/last-commit/densechen/eve-mli) [![Documentation Status](https://readthedocs.org/projects/eve-mli/badge/?version=latest)](https://eve-mli.readthedocs.io/en/latest/?badge=latest) [![PyPI](https://img.shields.io/pypi/v/eve-mli)](https://pypi.org/project/eve-mli) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/eve-mli)](https://pypi.org/project/eve-mli)\n\nOpenSpiking is an open-source deep learning framework used to devise and modify a network architecture in a flexible and interesting way.\n\nWe provide several jupyter notebooks under `./examples` to demonstrate how to build various network structures.\n\nThe most features of OpenSpiking are that: it provides a well defined framework which make your network structure can be upgraded along with the learning of weights.\n\n**Any contributions to OpenSpiking is welcome!**\n\n## Installation\n\nInstall from [PyPI](https://pypi.org/project/eve-mli/):\n\n```bash\npip install eve-mli\n# pip install git+https://github.com/densechen/eve-mli.git\n```\n\nDevelopers can download and install the latest version from:\n\n[GitHub](https://github.com/densechen/eve-mli):\n\n```bash\ngit clone https://github.com/densechen/eve-mli.git\ncd eve-mli\npython setup.py install\n```\n\n[Gitee](https://gitee.com/densechen/eve-mli.git):\n\n```bash\ngit clone https://gitee.com/densechen/eve-mli.git\ncd eve-mli\npython setup.py install\n```\n\n\nVailidate installation:\n\n```bash\npython -c \"import eve; print(eve.__version__)\"\n```\n\n\n## Quick Start\n\nThe core module of eve-mli is `eve.core.Eve`, this module is a wrapper of `torch.nn.Module`. \n\nIn `Eve`, the parameter ended with `_eve` will be treated as an eve parameters, and we call the rest as torch parameters. In the same way, we also define eve buffers and torch buffers. \n\nAs for eve parameters, you can fetch and attach an `.obs` properties via `eve.core.State` class, and assign an upgrade\nfunction to modify the eve parameter. As for eve buffers, it is useful to cache the hidden states, all the eve buffers will be cleared\nonce we call `Eve.reset()`. \n\nIn default, the model defined by `Eve` is the same with `nn.Module`. You can train it directly for obtaining a baseline model. Then, `Eve.spike()` will turn it into a spiking neural network module, and `Eve.quantize()` will trun it into a quantization neural network model.\n\n## About the project\n\nThe documentation can be found [here](https://eve-mli.readthedocs.io).\n(Auto-building of documentation fails sometimes, you can build it manually via ```cd docs; make html```).\n\nThe project remains in development. We encourage more volunteers to come together!\n\n**eve-mli-v0.1.0 is released!**\n\n## Next to do\n\nAdd CUDA support for speeding up, refer to [here](https://github.com/tudelft/cuSNN).\n\n## About the authors\n\n[Dengsheng Chen](https://densechen.github.io)\nMaster @ National University of Defense Technology\ndensechen@foxmail.com\n\n## References\n\n[PyTorch](https://github.com/pytorch/pytorch)\n\n[stable-baselines3](https://github.com/DLR-RM/stable-baselines3)\n\n[spikingjelly](https://github.com/fangwei123456/spikingjelly)\n\n[Model-Compression-Deploy](https://github.com/666DZY666/Model-Compression-Deploy)\n\n[Awesome-Deep-Neural-Network-Compression](https://github.com/csyhhu/Awesome-Deep-Neural-Network-Compression)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdensechen%2Feve-mli","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdensechen%2Feve-mli","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdensechen%2Feve-mli/lists"}