ttnn-visualizer 0.54.0

TT-NN Visualizer

TT-NN Visualizer

A tool for visualizing the Tenstorrent Neural Network model (TT-NN)

Buy Hardware | Install TT-NN | Discord | Join Us

Quick Start

TT-NN Visualizer can be installed from PyPI:

pip install ttnn-visualizer

After installation run ttnn-visualizer to start the application.

It is recommended to do this within a virtual environment. The minimum Python version is 3.10.

Please see the getting started guide for further information on getting up and running with TT-NN Visualizer.

If you want to test out TT-NN Visualizer you can try some of the sample data. See loading data for instructions on how to use this.

Features

For the latest updates and features, please see releases.

• Comprehensive list of all operations in the model
• Interactive graph visualization of operations
• Detailed and interactive L1, DRAM, and circular buffer memory plots
• Filterable list of tensor details
• Overview of all buffers for the entire model run
• Visualization of input and output tensors with core tiling and sharding details
• Visualize inputs/outputs per tensor or tensor allocation across each core
• Detailed insights into L1 peak memory consumption, with an interactive graph of allocation over time
• Navigate a tree of device operations with associated buffers and circular buffers
• Operation flow graph for a holistic view of model execution
• Load reports via the local file system or through an SSH connection
• Supports multiple instances of the application running concurrently
• BETA: Network-on-chip performance estimator (NPE) for Tenstorrent Tensix-based devices

Demo

Application demo

https://github.com/user-attachments/assets/4e51a636-c6d6-46df-bf34-a06bca13c0b3

L1 Summary with Tensor highlight	Operation inputs and outputs

Device operations with memory consumption	DRAM memory allocation

Operation graph view	Model buffer summary

Per core allocation details	Per core allocation details for individual tensors

Tensor details list	Performance report

Performance charts

NPE

Sample reports

You may test the application using the following sample reports.

Unzip the files into their own directories and select them with the local folder selector, or load the NPE data on the /npe route.

Segformer encoder memory report

Segformer decoder memory report

Llama mlp memory + performance report

N300 llama memory + performance report with NPE data + cluster description

NPE report

T3K synthetic synthetic_t3k_small.json.zip

Contributing

How to run TT-NN Visualizer from source.