ML-examples
CNTK
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| ML-examples | CNTK | |
|---|---|---|
| 2 | 1 | |
| 405 | 17,435 | |
| 2.2% | 0.0% | |
| 5.0 | 0.0 | |
| 9 months ago | about 1 year ago | |
| C++ | C++ | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ML-examples
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[D] Run Pytorch model inference on Microcontroller
CMSIS-NN. ARM centric. Examples. They also have an example for a pytorch to tflite converter via onnx
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Machine Learning on ARM
Well there's something, https://github.com/ARM-software/ML-examples
CNTK
What are some alternatives?
MNN - MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba
Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor
oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
tensorflow - An Open Source Machine Learning Framework for Everyone
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
onnx2c - Open Neural Network Exchange to C compiler.
Caffe - Caffe: a fast open framework for deep learning.
tinyengine - [NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory
Keras - Deep Learning for humans
serving - A flexible, high-performance serving system for machine learning models
scikit-learn - scikit-learn: machine learning in Python