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 parameter servers, and vice versa, depending on the specific bottlenecks in the training process. This architecture is particularly useful for deep learning models with a very large number of parameters, where storing all parameters on each worker node would be infeasible. Different communication strategies can be employed, such as synchronous or asynchronous updates, each with its own trade-offs in terms of convergence speed and accuracy. Synchronous updates ensure all workers use the same parameter version for a given iteration, leading to more consistent but potentially slower training. Asynchronous updates allow workers to proceed with their own parameter versions, which can speed up training but might introduce staleness and affect convergence. Parameter Server has been widely adopted in various distributed deep learning frameworks.