Introduction to Model Explorer
Model Explorer is a cutting-edge tool designed to improve the visualization and exploration of model graphs. This tool is especially useful for developers and researchers working with machine learning models, as it offers an intuitive way to interact with complex models across various formats.
Key Features
Model Explorer organizes model operations into nested layers, which can be easily expanded or collapsed. This hierarchical view allows users to navigate through the layers efficiently, facilitating a deeper understanding of how models work.
Some of the standout features include:
- Dynamic Layer Management: Users can highlight input and output operations, making it easier to trace data flow within the model.
- Metadata Overlay: For each node in the model graph, additional metadata can be displayed, enhancing the understanding of each operation.
- Interactive Visualization: Layers can be viewed in pop-ups that allow users to interact with the model for a more engaging exploration experience.
- Search and Comparison: The ability to perform searches and identify identical layers expedites the debugging process.
- GPU-Accelerated Rendering: The tool supports GPU acceleration, ensuring that graph rendering is both fast and smooth.
Supported Model Formats
Model Explorer is compatible with several popular model formats:
- TFLite
- TensorFlow (TF)
- TensorFlow.js (TFJS)
- MLIR
- PyTorch (Exported Program)
An extension framework is also available, allowing developers to add support for new formats easily.
Getting Started
To begin using Model Explorer, it can be installed via pip with the following command:
$ pip install ai-edge-model-explorer
$ model-explorer
For those looking to experiment without installation, Model Explorer is also accessible through Hugging Face for visualizing uploaded models.
Community and Contributions
The Model Explorer community encourages contributions, welcoming custom adapters to support additional model formats. Developers interested in creating these extensions can refer to the detailed adapter extension guide provided in the Wiki. Contributions are recognized through pull requests, where contributors can propose enhancements or new features.
Additional Resources
For further insights into the Model Explorer tool, there are several resources available:
- An introduction video on YouTube that provides a visual overview of the tool's capabilities.
- A blog post on the Google Research Blog that delves into the tool's development and applications.
Users are also invited to participate in ongoing research studies to help improve the Model Explorer tool.
By offering an intuitive interface and rich feature set, Model Explorer promises to be an invaluable asset for anyone dealing with complex machine learning models, streamlining the process of model visualization and debugging.