Introducing PyMilo: A New Way to Transport Pre-trained ML Models
Dear all, We are thrilled to introduce PyMilo, an open-source Python package that can revolutionize the way you transport pre-trained machine-learning models. PyMilo offers an efficient, secure, and transparent method that aims to eliminate the risks associated with binary or pickle formats. Why PyMilo? The motivation behind developing this package is simple but significant: to provide a safer and more reliable way to share machine learning models. As we embark on this journey, we acknowledge that PyMilo is still in its early stages of development. Currently, it supports only a limited number of machine learning models provided by Scikit-learn. Your Feedback Matters We firmly believe in the power of community collaboration. This is why we're reaching out to you, the Scikit-learn users, to ask for your support in utilizing PyMilo and providing us with your invaluable feedback. Your insights can help us enhance the package's interface and prioritize future developments. How You Can Contribute - Try PyMilo with your Scikit-learn models and let us know about your experience.- Share your thoughts on improving PyMilo's functionality and usability.- Report any issues or bugs you encounter. Your cooperation would be precious to us as we work towards making PyMilo a robust and indispensable tool for the machine learning community. Get Started To start using PyMilo, you can find detailed documentation and installation instructions on our GitHub repository: GitHub - openscilab/pymilo: PyMilo: Python for ML I/O | | | | | | | | | | | GitHub - openscilab/pymilo: PyMilo: Python for ML I/O PyMilo: Python for ML I/O. Contribute to openscilab/pymilo development by creating an account on GitHub. | | | Join us in shaping the future of model transportation with Pymilo! Thank you for your time and support. We look forward to your active participation in this exciting endeavor.
Hi, This seems somewhat similar but less complete than `skops.io`. You might wanna have a look there: https://github.com/skops-dev/skops/ On Thu, Sep 28, 2023 at 3:22 PM Sepand Haghighi via scikit-learn < scikit-learn@python.org> wrote:
Dear all,
We are thrilled to introduce PyMilo, an open-source Python package that can revolutionize the way you transport pre-trained machine-learning models. PyMilo offers an efficient, secure, and transparent method that aims to eliminate the risks associated with binary or pickle formats.
*Why PyMilo?*
The motivation behind developing this package is simple but significant: to provide a safer and more reliable way to share machine learning models. As we embark on this journey, we acknowledge that PyMilo is still in its early stages of development. Currently, it supports only a limited number of machine learning models provided by Scikit-learn.
*Your Feedback Matters*
We firmly believe in the power of community collaboration. This is why we're reaching out to you, the Scikit-learn users, to ask for your support in utilizing PyMilo and providing us with your invaluable feedback. Your insights can help us enhance the package's interface and prioritize future developments.
*How You Can Contribute*
- Try PyMilo with your *Scikit-learn* models and let us know about your experience. - Share your thoughts on improving PyMilo's functionality and usability. - Report any issues or bugs you encounter.
Your cooperation would be precious to us as we work towards making PyMilo a robust and indispensable tool for the machine learning community.
*Get Started*
To start using PyMilo, you can find detailed documentation and installation instructions on our GitHub repository:
GitHub - openscilab/pymilo: PyMilo: Python for ML I/O <https://github.com/openscilab/pymilo>
GitHub - openscilab/pymilo: PyMilo: Python for ML I/O
PyMilo: Python for ML I/O. Contribute to openscilab/pymilo development by creating an account on GitHub. <https://github.com/openscilab/pymilo>
Join us in shaping the future of model transportation with Pymilo!
Thank you for your time and support. We look forward to your active participation in this exciting endeavor.
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participants (2)
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Adrin -
Sepand Haghighi