A simple to use node-based GUI for creating multipurpose flows, such as those for training models to perform computer vision tasks.
(Linux)
sudo apt update
sudo apt install Node.js
sudo apt install npm
(Windows)
Download and install https://nodejs.org/en/download
(Linux)
sudo apt update
sudo apt-get install python3
sudo apt-get install python3-pip python-dev
(Windows)
Download and install https://www.python.org/downloads/windows/
(Linux)
sudo apt-get install git
(Windows)
Download and install https://git-scm.com/download/win
If you want to download and install the SDK to control the uFactory xArm, run the following command:
git clone https://github.com/xArm-Developer/xArm-Python-SDK.git
cd xArm-Python-SDK
python setup.py install
cd ..
In cmd on Windows or Terminal on Linux:
git clone https://github.com/msf4-0/SimpleFlowFinalDesign
cd SimpleFlow
npm create vite@latest my-react-flow-app -- --template react
npm install reactflow --force
cd my-react-flow-app
npm install
(Linux)
cd ..
sudo chmod +x setup.sh
./setup.sh
(Windows)
cd ..
setup.cmd
If you have a CUDA-capable GPU, you can install the GPU version of PyTorch. You can verify that you have a CUDA-capable GPU through the Display Adapters section in the Windows Device Manager. Here you will find the vendor name and model of your graphics card(s). If you have an NVIDIA card that is listed in https://developer.nvidia.com/cuda-gpus, that GPU is CUDA-capable. To install the GPU version of PyTorch along with other packages, run the following command:
install_gpu.cmd
Otherwise, install the CPU version of PyTorch along with other packages:
install.cmd
(Linux)
simpleflow
(Windows) In cmd in SimpleFlow directory paste the following:
simpleflow.cmd
or double-click on simpleflow.cmd file
For all nodes guide Nodes Guide
For creating your first flow Flow Guide
For how to get started with MQTT MQTT Guide
For how to create a custom library Custom Library Guide
For tutorial videos Simple Flow Youtube Channel