https://github.com/fblgit/transviz
Debug and Visualise a Neural Deep Network internals suite
https://github.com/fblgit/transviz
Last synced: 9 months ago
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Debug and Visualise a Neural Deep Network internals suite
- Host: GitHub
- URL: https://github.com/fblgit/transviz
- Owner: fblgit
- Created: 2025-02-08T11:24:16.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-02-09T11:48:29.000Z (over 1 year ago)
- Last Synced: 2025-09-22T13:24:09.959Z (9 months ago)
- Language: JavaScript
- Size: 573 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TransViz - Neural Network Visualization Toolkit
## STILLS UNDER EARLY PHASES OF DEVELOPMENT
Work being done:
- Packaging and dependencies
- Tests & Examples of Usage
- Issues being found at usage
- Overall the python engine
Work not yet done:
- UI stills in just first draft
- CI/CD, etc
A real-time visualization and debugging toolkit for neural networks, featuring:
- 🎮 Interactive tensor inspection
- ⏯️ Training process time travel
- 🔍 Breakpoint debugging system
- 📊 Real-time metrics dashboard
- 🔗 Distributed training support
## Key Features
- **Real-time Visualization**: Monitor activations/gradients during forward/backward passes
- **Breakpoint System**: Set conditional breakpoints with tensor pattern matching
- **Metrics Dashboard**: Track loss, accuracy, and custom metrics in real-time
- **Differential Updates**: Efficient binary diffs for large tensor visualization
- **Multi-Framework Support**: Designed for PyTorch with extensibility for other frameworks
## Installation
```
# Clone repository
git clone https://github.com/fblgit/transviz.git
cd transviz
# Install dependencies
pip install -r requirements.txt
```
## Quick Start
```
from transviz import visualize, VizConfig
# Instrument your model
@visualize.trace(name="transformer_block")
class TransformerBlock(nn.Module):
def forward(self, x):
x = self.attention(x)
visualize.breakpoint("post_attention", x)
return self.ffn(x)
# Initialize visualizer
viz = visualize.ModelVisualizer(
VizConfig(
port=8080,
mode="hybrid",
capture_gradients=True
)
)
viz.start()
# Train your model normally
train(model, dataloader)
```
Access the visualization dashboard at `http://localhost:8080`
## Configuration
```
@dataclass
class VizConfig:
port: int = 8080 # Web server port
mode: str = "hybrid" # light|hybrid|debug
storage_path: str = "./viz_data"
tensor_sampling: float = 0.1 # Fraction of data to capture
capture_gradients: bool = False
breakpoint_timeout: int = 300 # seconds
```
## API Reference
### Decorators
```
@visualize.trace(name="layer", capture_shapes=True)
@visualize.breakpoint("attention_weights", condition=lambda t: t.max() > 1.0)
```
### Core Methods
```
visualize.log_metrics({"loss": 0.5})
visualize.set_breakpoint("gradient_check", condition=has_nan)
visualize.save_session("training_run_1")
```
### CLI Commands
```
transviz serve --port 8080 --model ./checkpoint.pth
transviz export --session latest --format hdf5
```
## Contributing
1. Fork the repository
2. Create feature branch (`git checkout -b feature/amazing-feature`)
3. Commit changes (`git commit -m 'Add amazing feature'`)
4. Push to branch (`git push origin feature/amazing-feature`)
5. Open Pull Request
**Roadmap**
[ ] TensorFlow/Keras support
[ ] Jupyter notebook integration
[ ] Pre-trained model zoo integration
[ ] 3D tensor visualization