EasyFL_with_KDE/docs/en/get_started.md

Prerequisites

• Linux or macOS (Windows is in experimental support)
• Python 3.6+
• PyTorch 1.3+
• CUDA 9.2+ (If you run using GPU)

Installation

Prepare environment

1. Create a conda virtual environment and activate it.

   conda create -n easyfl python=3.7 -y
   conda activate easyfl
   

   1. Install PyTorch and torchvision following the official instructions, e.g.,

   conda install pytorch torchvision -c pytorch
   

   or

   pip install torch==1.10.1 torchvision==0.11.2
   

   1. You can skip the following CUDA-related content if you plan to run it on CPU. Make sure that your compilation CUDA version and runtime CUDA version match.

   Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

   E.g., 1. If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

   conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
   

   E.g., 2. If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

   conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
   

   If you build PyTorch from source instead of installing the prebuilt package, you can use more CUDA versions such as 9.0.

   Install EasyFL

   pip install easyfl
   

A from-scratch setup script

Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMDetection with conda.

conda create -n easyfl python=3.7 -y
conda activate easyfl

# Without GPU
conda install pytorch==1.6.0 torchvision==0.7.0 -c pytorch -y

# With GPU
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y

# install easyfl
git clone https://github.com/EasyFL-AI/easyfl.git
cd easyfl
pip install -v -e .

Verification

To verify whether EasyFL is installed correctly, we can run the following sample code to test.

import easyfl

easyfl.init()

The above code is supposed to run successfully after you finish the installation.