{"id":18237489,"url":"https://github.com/mehdi342/memoraith","last_synced_at":"2025-04-04T05:33:31.985Z","repository":{"id":258055964,"uuid":"864749774","full_name":"MEHDI342/Memoraith","owner":"MEHDI342","description":"Memoraith: A lightweight, versatile model profiler for deep learning frameworks. 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Developed with precision and efficiency in mind, it helps developers and researchers optimize their models through detailed performance analysis.\r\r\n\r\r\n## ✨ Key Features\r\r\n\r\r\n- � **Advanced Profiling**\r\r\n    - High-precision memory tracking (CPU \u0026 GPU)\r\r\n    - Microsecond-accurate computation timing\r\r\n    - Layer-by-layer performance analysis\r\r\n\r\r\n- � **Intelligent Analysis**\r\r\n    - Sophisticated bottleneck detection\r\r\n    - Anomaly identification\r\r\n    - Optimization recommendations\r\r\n\r\r\n- 📊 **Rich Visualization**\r\r\n    - Interactive dashboards\r\r\n    - Real-time monitoring\r\r\n    - Comprehensive reports\r\r\n\r\r\n- 🛠 **Framework Support**\r\r\n    - PyTorch integration\r\r\n    - TensorFlow support\r\r\n    - Extensible architecture\r\r\n\r\r\n## 🚀 Installation\r\r\n\r\r\nBasic installation:\r\r\n```bash\r\r\npip install memoraith\r\r\n```\r\r\n\r\r\nFull installation with GPU support and extra features:\r\r\n```bash\r\r\npip install memoraith[full]\r\r\n```\r\r\n\r\r\n## � Quick Start\r\r\n\r\r\nHere's a simple example using PyTorch:\r\r\n\r\r\n```python\r\r\nfrom memoraith import profile_model, set_output_path\r\r\nimport torch\r\r\nimport torch.nn as nn\r\r\n\r\r\n# Set output directory for profiling results\r\r\nset_output_path('profiling_results/')\r\r\n\r\r\n# Define your model\r\r\nclass AdvancedNet(nn.Module):\r\r\n    def __init__(self):\r\r\n        super(AdvancedNet, self).__init__()\r\r\n        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)\r\r\n        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\r\r\n        self.fc = nn.Linear(128 * 8 * 8, 10)\r\r\n\r\r\n    def forward(self, x):\r\r\n        x = torch.relu(self.conv1(x))\r\r\n        x = torch.relu(self.conv2(x))\r\r\n        x = x.view(x.size(0), -1)\r\r\n        return self.fc(x)\r\r\n\r\r\n# Add profiling decorator\r\r\n@profile_model(memory=True, computation=True, gpu=True)\r\r\ndef train_model(model):\r\r\n    optimizer = torch.optim.Adam(model.parameters())\r\r\n    for _ in range(100):\r\r\n        input_data = torch.randn(32, 3, 32, 32)\r\r\n        output = model(input_data)\r\r\n        loss = output.sum()\r\r\n        loss.backward()\r\r\n        optimizer.step()\r\r\n\r\r\nif __name__ == \"__main__\":\r\r\n    model = AdvancedNet()\r\r\n    train_model(model)\r\r\n```\r\r\n\r\r\n## 📚 Documentation\r\r\n\r\r\nVisit our [comprehensive documentation](https://memoraith.readthedocs.io) for:\r\r\n- Detailed API reference\r\r\n- Advanced usage examples\r\r\n- Best practices\r\r\n- Troubleshooting guides\r\r\n\r\r\n## � Contributing\r\r\n\r\r\nWe welcome contributions! See our [Contributing Guide](CONTRIBUTING.md) for:\r\r\n- Code of conduct\r\r\n- Development setup\r\r\n- Submission guidelines\r\r\n- Testing procedures\r\r\n\r\r\n## � License\r\r\n\r\r\nMemoraith is released under the MIT License. See [LICENSE](LICENSE) file for details.\r\r\n\r\r\n## 🆘 Support\r\r\n\r\r\nNeed help?\r\r\n- 📋 [GitHub Issues](https://github.com/mehdi342/Memoraith/issues)\r\r\n- 📚 [Documentation](https://memoraith.readthedocs.io)\r\r\n- 📧 [Email Support](mailto:midojouhfi@gmail.com)\r\r\n\r\r\n## 📖 Citation\r\r\n\r\r\nIf you use Memoraith in your research, please cite:\r\r\n\r\r\n```bibtex\r\r\n@software{memoraith,\r\r\n    author = {El Jouhfi, Mehdi},\r\r\n    title = {Memoraith: Advanced Lightweight Model Profiler for Deep Learning},\r\r\n    year = {2024},\r\r\n    url = {https://github.com/mehdi342/Memoraith},\r\r\n    version = {0.5.0}\r\r\n}\r\r\n```\r\r\n\r\r\n## 📬 Contact\r\r\n\r\r\nFor inquiries, reach out to [Mehdi El Jouhfi](mailto:midojouhfi@gmail.com)\r\r\n\r\r\n---\r\r\n\u003cdiv align=\"center\"\u003e\r\r\n  Made with �� and sweat by Mehdi El Jouhfi\r\r\n\u003c/div\u003e\r\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehdi342%2Fmemoraith","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmehdi342%2Fmemoraith","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmehdi342%2Fmemoraith/lists"}