What Parameter Server Meaning, Applications & Example
System for distributing model parameters across machines.
What is Parameter Server?
A Parameter Server is a distributed system architecture designed for handling the parameters of machine learning models during training. It allows the model parameters (weights and biases) to be shared and updated across multiple workers, enabling efficient distributed training for large-scale machine learning models.
Key Features of Parameter Server
- Distributed Model Training: Enables parallel training by distributing the model’s parameters across multiple machines.
- Centralized Parameter Storage: Stores and updates parameters centrally, while workers perform computations and send gradients to update the parameters.
- Scalability: Supports scaling to large datasets and models by distributing the computation and storage load.
Applications of Parameter Server
- Large-scale Deep Learning: Used for training large models on massive datasets, where a single machine can’t handle the computation or storage.
- Distributed Machine Learning: In multi-machine setups, helps coordinate updates between workers in real-time.
- Cloud-based Training: Enables efficient use of cloud resources for machine learning at scale.
Example of Parameter Server
In a deep learning model, the parameter server could be used to store the parameters (weights) of a neural network , while multiple workers perform forward and backward propagation on different parts of the dataset and send gradients to the parameter server to update the model’s weights.