https://github.com/openmined/pysyft-tensorflow
SOON TO BE DEPRECATED - The TensorFlow bindings for PySyft
https://github.com/openmined/pysyft-tensorflow
pysyft python tensorflow
Last synced: 3 months ago
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SOON TO BE DEPRECATED - The TensorFlow bindings for PySyft
- Host: GitHub
- URL: https://github.com/openmined/pysyft-tensorflow
- Owner: OpenMined
- License: apache-2.0
- Created: 2019-07-29T18:32:51.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-05-30T17:40:28.000Z (over 5 years ago)
- Last Synced: 2025-10-14T05:40:03.754Z (3 months ago)
- Topics: pysyft, python, tensorflow
- Language: Python
- Homepage:
- Size: 119 KB
- Stars: 57
- Watchers: 11
- Forks: 11
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# DEPRECATION NOTICE
Warning, this repository will soon be deprecated in favor of [PySyft](https://github.com/OpenMined/PySyft).
# PySyft-TensorFlow
TensorFlow bindings for [PySyft](https://github.com/openmined/pysyft).
PySyft is a Python framework for secure, private deep learning. PySyft-TensorFlow brings
secure, private deep learning to [TensorFlow](https://tensorflow.org).
[](https://travis-ci.org/OpenMined/PySyft-TensorFlow)
[](https://openmined.slack.com/messages/team_pysyft)
## Installation
PySyft-TensorFlow is available on pip
```
pip install syft-tensorflow
```
NOTE: We aren't yet on a proper release schedule. Until then, we recommend building the code from source. The master branch is intended to be kept in line with [this branch](https://github.com/dropoutlabs/PySyft/tree/dev) on the [DropoutLabs](https://github.com/dropoutlabs/PySyft) fork of PySyft. If you have any trouble, please open an issue or reach out on Slack via the #team_tensorflow or #team_pysyft channels.
## Usage
See the [PySyft tutorials](https://github.com/OpenMined/PySyft/tree/master/examples/tutorials)
if you are unfamiliar with any Syft paradigms.
```python
import tensorflow as tf
import syft
hook = sy.TensorFlowHook(tf)
# Simulates a remote worker (ie another computer)
remote = sy.VirtualWorker(hook, id="remote")
# Send data to the other worker
x = tf.constant(5).send(remote)
y = tf.constant(10).send(remote)
z = x * y
print(z.get())
# => 50
```
## Developing PySyft-TensorFlow
See [CONTRIBUTING](./CONTRIBUTING.md).
## Project Support
PySyft-Tensorflow was contributed by and continues to be maintained by the team at [Dropout Labs](https://dropoutlabs.com).
Please reach out to contact@dropoutlabs.com for support.
[
](https://dropoutlabs.com/)
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