Factual package intelligence from PyPI

pyTorchAutoForge 0.6.0 package intelligence

PyTorchAutoForge library is based on raw PyTorch and designed to automate DNN development, model tracking and deployment, tightly integrated with MLflow and Optuna. It supports Spiking networks libraries (WIP). Deployment can be performed using ONNX, pyTorch facilities or TensorR

pip install pytorchautoforge

Current version
0.6.0
Python requirement
>=3.10
License
MIT
Distribution type
Pure Python wheel
Release files
2 (1 wheels, 1 source)
Download size
470.5 KiB
Release history
18 releases with files
Median release cadence
5 days

pyTorchAutoForge dependencies

PyPI declares 28 unique dependency rules for this release. Environment markers are shown when supplied by the project.

  1. albumentations
  2. captum
  3. colorama
  4. dacite
  5. expelliarmus when platform_machine == "x86_64"
  6. inputimeout
  7. kornia
  8. matplotlib <=3.10.0
  9. mlflow <=3.4
  10. msgpack
  11. mypy
  12. norse when platform_machine == "x86_64"
  13. numpy
  14. onnx
  15. onnxruntime
  16. onnxscript
  17. optuna
  18. pytest <=8.3.5
  19. scikit-learn <=1.6.1
  20. scipy
  21. seaborn
  22. statsmodels
  23. tonic when platform_machine == "x86_64"
  24. torch-tb-profiler
  25. torch <2.10.0,>=2.7 when platform_machine == "x86_64"

The compact report shows 25 of 28 declarations. The interactive dependency graph loads the complete metadata.

Compatibility and release files

pyTorchAutoForge publishes 1 wheel and 1 source archive for version 0.6.0. Wheel platform tags: any.

Declared Python classifiers: 3.10, 3.11, 3.12.

Metadata completeness

PyPI does not currently declare: project URL. PyDeps marks these fields as unknown instead of guessing values.

Release activity and sources

PyPI lists 18 releases with files. The first dated release is ; 17 releases fall within the 365 days preceding the latest dated release. The current release files were uploaded on . The preceding dated release was .

The project does not publish external project URLs in its PyPI metadata.