You can run these examples by launching them within Jupyter Notebook.The built distributions are uploaded to /conda-forgeĪnd can be installed with conda. These tutorials cover the major data science, machine and deep learning Python libraries. You can find a list of examples using PySyft here. If you don’t see any errors the installation has worked correctly. To test if the installation has worked correctly, open the Python interpreter and import PySyft: This will auto-install all dependencies required to run the tutorials and examples on the OpenMined website and PySyft GitHub page. The following command activates the environment: conda activate pysyft. Note: each time you need to use PySyft, remember to make sure you are in this virtual environment. If you’re on Linux or Mac, open the terminal. If you’re on Windows, open Anaconda Prompt. This is required to be able to install PySyft. When you see the following screen ensure you select Desktop development with C++. Open the installer and follow the instructions. You can find this at the bottom of the page under All Downloads -> Tools for Visual Studio 2019. Go to and download Build Tools for Visual Studio 2019. Install Microsoft Build Tools (only required for Windows users) After this the steps are the same for Windows, Linux and Mac.ġ. There is an extra step required if you are installing on Windows. Even though Miniconda works on Windows, use Anaconda as it includes the Anaconda prompt, which will allow you to work from the command line. If you are new to Conda and/or Python, I would recommend using Anaconda.
Or install Miniconda, if you want to minimise the space required by only downloading relevant packages when you need them. Note: you might prefer to just install conda using pip: To install it just follow the installer’s on screen instructions. If you don’t already have Anaconda you can download it here. Conda lets you set up virtual environments and is included with Anaconda, a toolkit for data science. This guide will show you how to install PySyft using Conda. It can be used within the major Deep Learning Frameworks such as TensorFlow and PyTorch. It uses Federated Learning, Differential Privacy, and Encrypyted Computation to decouple private and sensitive data. PySyft is a Python library for secure and private Deep Learning.
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