parameterserver
Parameter Server is a distributed system architecture designed for training machine learning models on large datasets. It separates model parameters from the computational nodes that perform the training operations. The architecture consists of two main components: worker nodes and parameter nodes. Worker nodes are responsible for fetching data, computing gradients, and sending these gradients to the parameter server. Parameter nodes store and manage the model's parameters. They receive gradients from the workers, update the parameters accordingly, and then serve the updated parameters back to the workers.
This separation allows for efficient scaling of distributed training. Workers can be scaled independently of the