Have you come across TorchServe and TorchElastic anywhere in the near present? Maybe you would have heard of PyTorch, the very famous Python framework developed by the tech giant Facebook.
Yup, TorchServe and TorchElastic are the newcomers in the PyTorch library module. Sounds interesting? Then read through this definitive guide on the basic idea behind TorchServe and TorchElastic.
First of all, let's see what it is all about Torch now.
What is Torch in Machine Learning?
Torch is an open-source library function for machine learning algorithms. It is a scientific computing framework, also a scripting language based on LuaJIT programming language. However, from 2018, Torch is not directly deployed in the development environment. But PyTorch is actively used in the machine learning production environment.
Now, Let's have a brief overview of PyTorch.
What is PyTorch?
Facebook A.I and Research Lab (FAIR) had developed an open-source machine learning library based on Torch with Python. This is called PyTorch.
PyTorch is primarily used for deep learning on Graphical User Interface, natural language processing applications and computer vision concepts. It is free, open-source software.
Now, What is TorchServe
TorchServe is a tool or framework designed to serve PyTorch models. TorchServe makes the process easy while deploying PyTorch models in the production environment with low latency. That consequently results in high-performance solutions. Torch Serve is a flexible tool. AWS built it along with Facebook.
We have a caveat for this tool. TorchServe is developed for experimental usage and is subject to change in any future.
Now, Let's Look into Basics of TorchElastic
TorchElastic, does it sound like elasticity and resilience? Yup, its primary purpose is fault tolerance. As we all know, machine learning involves training and testing with the gamut of data sets. While executing a shared PyTorch training task, TorchElastic ascertains the resilience to a fault.
It has got two use cases one is fault tolerance to changing nodes. Secondly, dynamic capacity management, that would support the model execution on changing shared memory resources.
Windfalls of TorchServe and TorchElastic in PyTorch Framework
It was always hard to deploy machine learning models in the production environment. Hence these two supporting library modules, TorchServe and TorchElastic are so created to reduce the time taken for simulation and the response. These library functions are so powerful, therefore, facilitates features like that of a custom code that eases the chores of machine learning developers. Also, these tools secure utmost compatibility in any production environment.