# Hogwild!? Implementing Async SGD in Python

Hogwild! is asynchronous stochastic gradient descent algorithm. The Hogwild! approach utilizes “lock-free” gradient updates. For a machine learning model, this means that the weights of a model are updated by multiple processes at the same time with the possibility of overwriting each other. In this post, we will use the multiprocessing library to implement Hogwild! in Python for training a linear regression model.