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Putting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters).
| Date | Stars |
|---|---|
| 2026-07-24 | 646 |
| 2026-07-25 | 646 |
| 2026-07-28 | 646 |
| 2026-07-30 | 646 |
| 2026-08-06 | 646 |
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# TfPyTh
[](https://travis-ci.com/BlackHC/tfpyth) [](https://codecov.io/gh/BlackHC/tfpyth)
> Putting TensorFlow back in PyTorch, back in TensorFlow (with differentiable TensorFlow PyTorch adapters).
Do you have a codebase that uses TensorFlow and one that uses PyTorch and want to train a model that uses both end-to-end?
This library makes it possible without having to rewrite either codebase!
It allows you to wrap a TensorFlow graph to make it callable (and differentiable) through PyTorch, and vice-versa, using simple functions.
The only caveat is that tensors have to be copied and routed through the CPU until TensorFlow supports `__cuda_array_interface` (please star the [GitHub issue](https://github.com/tensorflow/tensorflow/issues/29039)).
## Install
```
pip install tfpyth
```
### Example
```python
import tensorflow as tf
import torch as th
import numpy as np
import tfpyth
session = tf.Session()
def get_torch_function():
a = tf.placeholder(tf.float32, name='a')
b = tf.placeholder(tf.float32, name='b')
c = 3 * a + 4 * b * b
f = tfpyth.torch_from_tensorflow(session, [a, b], c).apply
return f
f = get_torch_function()
a = th.tensor(1, dtype=th.float32, requires_grad=True)
b = th.tensor(3, dtype=th.float32, requires_grad=True)
x = f(a, b)
assert x == 39.
x.backward()
assert np.allclose((a.grad, b.grad), (3., 24.))
```
## What it's got
### `torch_from_tensorflow`
Creates a PyTorch function that is differentiable by evaluating a TensorFlow output tensor given input placeholders.
### `eager_tensorflow_from_torch`
Creates an eager Tensorflow function from a PyTorch function.
### `tensorflow_from_torch`
Creates a TensorFlow op/tensor from a PyTorch function.
## Future work
- [ ] support JAX
- [ ] support higher-order derivatives
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Andreas Kirsch · United Kingdom
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:805d62e90cd20c1b, topic:pytorch, topic:tensorflow