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Record execution graphs of PyTorch neural networks

Project description


A small package to record execution graphs of neural networks in PyTorch. The package uses hooks and the grad_fn attribute to record information.
This can be used to generate visualizations at different scope depths.

Licensed under MIT License. View documentation at



Install this package:

$ pip install torchrec


Consider the below example network:

import sys
import torch
import torchrec

class SampleNet(torch.nn.Module):
    def __init__(self):

        self.linear_1 = torch.nn.Linear(in_features=3, out_features=3, bias=True)
        self.linear_2 = torch.nn.Linear(in_features=3, out_features=3, bias=True)
        self.linear_3 = torch.nn.Linear(in_features=6, out_features=1, bias=True)
        self.my_special_relu = torch.nn.ReLU()

    def forward(self, inputs):
        x = self.linear_1(inputs)
        y = self.linear_2(inputs)
        z =[x, y], dim=1)
        z = self.my_special_relu(self.linear_3(z))
        return z

def main():
    i = int(sys.argv[1])
    net = SampleNet()
        name="Sample Net",
        input_shapes=(1, 3),

if __name__ == "__main__":

And visualizations like these can be produced:


This is inspired from szagoruyko/pytorchviz. This package differs from pytorchviz as it provides rendering at multiple depths.

Note that for rendering a network via TensorBoard during training, you can use torch.utils.tensorboard.SummaryWriter.add_graph, which records and renders to a protobuf in a single step. The intended usage of pytorchrec is for presentation purposes.

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