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A tool to analyze and debug neural networks in pytorch. Use a GUI to traverse the computation graph and view the data from many different angles at the click of a button.
| Date | Stars |
|---|---|
| 2026-07-31 | 301 |
| 2026-08-04 | 301 |
| 2026-08-05 | 301 |
| 2026-08-06 | 301 |
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# Comgra: Computation Graph Analysis <p align="center"> <img src="src/assets/brandcrowd_logos/FullLogo.png" title="ComgraLogo" height="300" width="300"/> </p> - [Overview](#overview) - [Installation](#installation) - [Usage](#usage) - [Tutorial](#tutorial---debugging-an-example-network) - [The Task and the Architecture](#the-task-and-the-architecture) - [Initial Exploration](#initial-exploration) - [Finding the Bug](#finding-the-bug) - [Other Features](#other-features) - [Custom Visualization](#custom-visualization) - [Dynamic Recordings](#dynamic-recordings) - [Known Issues](#known-issues) - [Future Development: Anomaly Detection and Correlation Analysis](#future-development-anomaly-detection-and-correlation-analysis) ## Overview Comgra helps you analyze and debug neural networks in pytorch. It records your network internals, visualizes the computation graph, and provides a GUI to investigate any part of your network from a variety of viewpoints. Move along the computation graph, check for outliers, investigate both individual data points and summary statistics, compare gradients, automatically record special cases, and more. Comgra records everything that could be relevant to you, and allows you to inspect your network's behavior from many different angles. Suitable both for novices and for professional neural architecture designers: Create a simple visualization of your network to understand what is happening under the hood, or perform advanced analyses and trace anomalies through the computation graph. | <img src="src/assets/screenshots_for_tutorial/main_overview.png" width="100%"/> | - Comgra's GUI has three parts: * A dependency graph that visualizes how the tensors in your network depend on each other * Selectors that let you choose under what lens you want to inspect the tensors * An output that lists both summary statistics and the values of individual neurons for the selected tensors <details> <summary><b>Notes on the dependency graph</b></summary> Each rectangle in the dependency graph is a node that represents a named tensor. The colors indicate the roles of the tensor in the network, such as input, intermediate result, parameter, etc. When you select a node it becomes highlighted, along with all nodes that it depends on (to the left) and that depend on it (to the right). Only the links for the selected node are shown by default to avoid visual clutter, but by clicking on one node after the other you can explore the entire dependency graph. If a node has a dotted border on one side, it indicates that it does not have any dependency (left) or dependent (right) on that iteration. If a connection is drawn with a thinner line, it indicates that some of the tensors in the node have this connection, but the currently selected one does not. In the example network of the tutorial, this is the case for the node 'subnet_pre', which summarizes all four parameters of the module with that name. You can use the "Role of Tensor" selector to switch to another parameter in that module, which will change the connections. The dependency graph is generated automatically based on the computation graph used by pytorch and the names you assign to tensors through comgra. It is a subgraph of the computation graph, but it is much easier to understand because it is smaller and skips all the distracting details. This cutting away of details also makes it easier to compare different variants of architectures: Their computation graphs may look different, but the simplified dependency graphs are the same. </details> ## Installation Install with pip: ```bash pip install comgra ``` ## Usage To use comgra, modify your python code with the following commands in the appropriate places. Most of it just tells comgra what you are currently doing so that it knows how to associate the tensors you register. The file `src/scripts/run.py` ([found here](https://github.com/FlorianDietz/comgra/blob/main/src/scripts/run.p
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matched fp:f30cf4a8b9dd9c5a, llm:Repository description: 'A tool to analyze and debug neural networks in pytorch. Use a GUI to traverse the computation graph and view the data from many different angles at the click of a button.'
matched fp:f30cf4a8b9dd9c5a, llm:Repository description: 'A tool to analyze and debug neural networks in pytorch. Use a GUI to traverse the computation graph and view the data from many different angles at the click of a button.'