TorchServe is a tool for serving neural net models for inference
Project description
TorchServe (PyTorch mdoel server) is a flexible and easy to use tool for serving deep learning models exported from PyTorch.
Use the TorchServe CLI, or the pre-configured Docker images, to start a service that sets up HTTP endpoints to handle model inference requests.
Prerequisites
java 8: Required. TorchServe use java to serve HTTP requests. You must install java 8 (or later) and make sure java is on available in $PATH environment variable before installing torchserve. If you have multiple java installed, you can use $JAVA_HOME environment vairable to control which java to use.
PyTorch: Required. Latest version of PyTorch will be installed as a part of TorchServe installation.
For ubuntu:
sudo apt-get install openjdk-8-jdk
For centos
sudo yum install java-1.8.0-openjdk
For Mac:
brew tap caskroom/versions brew update brew cask install java8
Install PyTorch:
pip install torch torchvision torchtext
Installation
pip install torchserve
Source code
You can check the latest source code as follows:
git clone https://github.com/pytorch/serve.git
Citation
If you use torchserve in a publication or project, please cite torchserve: https://github.com/pytorch/serve
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