BlackHC/tfpyth
Putting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters).
This library helps machine learning engineers or researchers integrate models built in TensorFlow with models built in PyTorch. It allows you to use a TensorFlow graph as a function within a PyTorch model, or vice-versa, making the entire combined system differentiable. This means you can train a single model that leverages components from both frameworks.
647 stars. No commits in the last 6 months. Available on PyPI.
Use this if you have existing model components or codebases in both TensorFlow and PyTorch and need to combine them into a single, end-to-end trainable system without rewriting either.
Not ideal if your entire workflow is confined to a single deep learning framework, or if you require direct GPU-to-GPU tensor transfers between frameworks (as data currently moves via CPU).
Stars
647
Forks
96
Language
Python
License
MIT
Category
Last pushed
Nov 30, 2020
Commits (30d)
0
Dependencies
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/BlackHC/tfpyth"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
keras-team/keras
Deep Learning for humans
Lightning-AI/torchmetrics
Machine learning metrics for distributed, scalable PyTorch applications.
Lightning-AI/pytorch-lightning
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
lanpa/tensorboardX
tensorboard for pytorch (and chainer, mxnet, numpy, ...)