ttperf 0.1.0

A streamlined CLI tool for profiling Tenstorrent's TT-Metal tests and extracting device kernel performance metrics

🚀 ttperf - TT-Metal Performance Profiler

A streamlined CLI tool for profiling Tenstorrent's TT-Metal tests and extracting device kernel performance metrics

✨ Features

• 🔍 Automated Profiling: Seamlessly runs Tenstorrent's TT-Metal profiler with pytest
• 📊 CSV Analysis: Automatically extracts and parses performance CSV files
• ⚡ Real-time Output: Shows profiling progress in real-time
• 📈 Performance Metrics: Calculates total DEVICE KERNEL DURATION
• 🎯 Simple CLI: Easy-to-use command-line interface
• 🛠️ Flexible: Supports named profiles and various test paths

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/Aswintechie/ttperf.git
cd ttperf

# Install the package
pip install -e .

Basic Usage

# Run profiling on a specific test
ttperf test_performance.py

# Run with a custom profile name
ttperf my_profile pytest test_performance.py

# Run on a specific test method
ttperf tests/test_ops.py::test_matmul

📋 Usage Examples

Simple Test Profiling

ttperf test_conv.py

Named Profile

ttperf conv_benchmark pytest test_conv.py

Specific Test Method

ttperf tests/ops/test_matmul.py::test_basic_matmul

Output Example

▶️ Running: ./tt_metal/tools/profiler/profile_this.py -n conv_benchmark -c "pytest test_conv.py"

... (profiling output) ...

📁 Found CSV path: /path/to/profile_results.csv
⏱️ DEVICE KERNEL DURATION [ns] total: 1234567.89 ns

🛠️ How It Works

1. Command Parsing: Analyzes input arguments to determine profile name and test path
2. Profile Execution: Runs the Tenstorrent's TT-Metal profiler with the specified test
3. Output Monitoring: Streams profiling output in real-time
4. CSV Extraction: Parses the output to find the generated CSV file path
5. Performance Analysis: Reads the CSV and calculates total device kernel duration

📊 Performance Metrics

The tool extracts the following key metrics:

• DEVICE KERNEL DURATION [ns]: Total time spent in device kernels
• CSV Path: Location of the detailed profiling results
• Real-time Progress: Live output during profiling

🔧 Requirements

• Python 3.7+
• pandas
• Tenstorrent's TT-Metal development environment
• pytest

📁 Project Structure

ttperf/
├── ttperf.py          # Main CLI implementation
├── pyproject.toml     # Project configuration
├── README.md          # This file
└── .gitignore         # Git ignore rules

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🐛 Issues

If you encounter any issues, please create an issue on GitHub.

👨‍💻 Author

Aswin Z

• GitHub: @Aswintechie

🌟 Acknowledgments

• Tenstorrent's TT-Metal development team for the profiling tools
• Python community for excellent libraries like pandas

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Made with ❤️ for the Tenstorrent TT-Metal community