Extends scikit-learn with a couple of new models, transformers, metrics, plotting.
onnxcustom: custom ONNX
Examples, tutorial on how to convert machine learned models into ONNX, implement your own converter or runtime, or even train with ONNX / onnxruntime.
The function check or the command line python -m onnxcustom check checks the module is properly installed and returns processing time for a couple of functions or simply:
import onnxcustom onnxcustom.check()
The documentation also introduces onnx, onnxruntime for inference and training. The tutorial related to scikit-learn has been merged into sklearn-onnx documentation. Among the tools this package implements, you may find:
- a tool to convert NVidia Profilder logs into a dataframe,
- a SGD optimizer similar to what scikit-learn implements but based on onnxruntime-training and able to train an CPU and GPU,
- functions to manipulate onnx graph.
Hashes for onnxcustom-0.4.293-py3-none-any.whl