# My journey with ONNX and Go - Running the graph

In the previous post, I made an introduction and a POC to interact with ONNX models and Go.
I have decoded the information to reconstruct a graph. Now I propose to expand the principle and to create a proper execution backend based on Gorgonia. This post is a bit more technical than the previous one because all the concepts needed to work should be present in the last article.
Decoding the tensor In machine learning, the fundamental element of a computation graph is a Tensor.

# My journey with ONNX and Go - The beginning

This year has started with a lot of deep thoughts about the software 2.0. My conclusion (which is slightly different from Andrej Karpathy’s consideration) is that a software 2.0 is a combination of a Neural network model and its associated weights. This is a concept; now the question is: how to materialize the idea? What artifact represents a software 2.0.
I emitted several ideas and tried one of them: to serialize the mathematical model and the weights.