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TensorFlow vs PySyft: What are the differences?

Developers describe TensorFlow as "Open Source Software Library for Machine Intelligence". TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. On the other hand, PySyft is detailed as "A library for encrypted, privacy preserving machine learning". It is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Multi-Party Computation (MPC) within the main Deep Learning frameworks like PyTorch and TensorFlow.

TensorFlow and PySyft can be primarily classified as "Machine Learning" tools.

TensorFlow is an open source tool with 140K GitHub stars and 79.6K GitHub forks. Here's a link to TensorFlow's open source repository on GitHub.

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Pros of PySyft
Pros of TensorFlow
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    • 25
      High Performance
    • 16
      Connect Research and Production
    • 13
      Deep Flexibility
    • 9
      True Portability
    • 9
      Auto-Differentiation
    • 2
      Easy to use
    • 2
      High level abstraction
    • 1
      Powerful

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    Cons of PySyft
    Cons of TensorFlow
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      • 9
        Hard
      • 6
        Hard to debug
      • 1
        Documentation not very helpful

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      What is PySyft?

      It is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Multi-Party Computation (MPC) within the main Deep Learning frameworks like PyTorch and TensorFlow.

      What is TensorFlow?

      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

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      What companies use PySyft?
      What companies use TensorFlow?
      See which teams inside your own company are using PySyft or TensorFlow.
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      What tools integrate with PySyft?
      What tools integrate with TensorFlow?

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      What are some alternatives to PySyft and TensorFlow?
      PyTorch
      PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      CUDA
      A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
      Kubeflow
      The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
      See all alternatives