Implementing the parameter server

Previously, we discussed the parameter server architecture and its variations. Now, let's dive into the implementation of the data pipeline for the parameter server architecture. The technical requirements for this section are as follows:

• The PyTorch and TorchVision libraries
• The MNIST dataset

For the environment setup, we have used CUDA 11.0 and PyTorch 3.7. We have trained a simple convolution neural network (CNN) on the MNIST dataset. First, we must define our model structure. Then, we will highlight the key functions of the parameter server and workers.

Defining model layers

First, we will define a simple CNN model, as shown in the following snippet:

class MyNet(nn.Module):
  def __init__(self):
    ...
  def forward(self, x):
    x = self.conv1(x)
    x = self.dropout1(x)
    x = F.relu(x)
    ...