https://github.com/mehdi342/memoraith
Memoraith: A lightweight, versatile model profiler for deep learning frameworks. Optimize your neural networks with detailed insights on memory usage, computation time, and performance bottlenecks.
https://github.com/mehdi342/memoraith
deep-learning machine-learning memory-profiling model-profiling optimization performance-analysis pytorch tensorflow
Last synced: 11 months ago
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Memoraith: A lightweight, versatile model profiler for deep learning frameworks. Optimize your neural networks with detailed insights on memory usage, computation time, and performance bottlenecks.
- Host: GitHub
- URL: https://github.com/mehdi342/memoraith
- Owner: MEHDI342
- License: mit
- Created: 2024-09-29T04:02:31.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-30T23:07:47.000Z (over 1 year ago)
- Last Synced: 2024-10-31T00:18:13.198Z (over 1 year ago)
- Topics: deep-learning, machine-learning, memory-profiling, model-profiling, optimization, performance-analysis, pytorch, tensorflow
- Language: Python
- Homepage:
- Size: 56.6 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Memoraith
[](https://badge.fury.io/py/memoraith)
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://memoraith.readthedocs.io/en/latest/?badge=latest)
**Advanced Lightweight Model Profiler for Deep Learning**
## Overview
Memoraith is a cutting-edge, lightweight model profiler for deep learning frameworks, providing unparalleled insights into neural network performance. Developed with precision and efficiency in mind, it helps developers and researchers optimize their models through detailed performance analysis.
## ✨ Key Features
- � **Advanced Profiling**
- High-precision memory tracking (CPU & GPU)
- Microsecond-accurate computation timing
- Layer-by-layer performance analysis
- � **Intelligent Analysis**
- Sophisticated bottleneck detection
- Anomaly identification
- Optimization recommendations
- 📊 **Rich Visualization**
- Interactive dashboards
- Real-time monitoring
- Comprehensive reports
- 🛠**Framework Support**
- PyTorch integration
- TensorFlow support
- Extensible architecture
## 🚀 Installation
Basic installation:
```bash
pip install memoraith
```
Full installation with GPU support and extra features:
```bash
pip install memoraith[full]
```
## � Quick Start
Here's a simple example using PyTorch:
```python
from memoraith import profile_model, set_output_path
import torch
import torch.nn as nn
# Set output directory for profiling results
set_output_path('profiling_results/')
# Define your model
class AdvancedNet(nn.Module):
def __init__(self):
super(AdvancedNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.fc = nn.Linear(128 * 8 * 8, 10)
def forward(self, x):
x = torch.relu(self.conv1(x))
x = torch.relu(self.conv2(x))
x = x.view(x.size(0), -1)
return self.fc(x)
# Add profiling decorator
@profile_model(memory=True, computation=True, gpu=True)
def train_model(model):
optimizer = torch.optim.Adam(model.parameters())
for _ in range(100):
input_data = torch.randn(32, 3, 32, 32)
output = model(input_data)
loss = output.sum()
loss.backward()
optimizer.step()
if __name__ == "__main__":
model = AdvancedNet()
train_model(model)
```
## 📚 Documentation
Visit our [comprehensive documentation](https://memoraith.readthedocs.io) for:
- Detailed API reference
- Advanced usage examples
- Best practices
- Troubleshooting guides
## � Contributing
We welcome contributions! See our [Contributing Guide](CONTRIBUTING.md) for:
- Code of conduct
- Development setup
- Submission guidelines
- Testing procedures
## � License
Memoraith is released under the MIT License. See [LICENSE](LICENSE) file for details.
## 🆘 Support
Need help?
- 📋 [GitHub Issues](https://github.com/mehdi342/Memoraith/issues)
- 📚 [Documentation](https://memoraith.readthedocs.io)
- 📧 [Email Support](mailto:midojouhfi@gmail.com)
## 📖 Citation
If you use Memoraith in your research, please cite:
```bibtex
@software{memoraith,
author = {El Jouhfi, Mehdi},
title = {Memoraith: Advanced Lightweight Model Profiler for Deep Learning},
year = {2024},
url = {https://github.com/mehdi342/Memoraith},
version = {0.5.0}
}
```
## 📬 Contact
For inquiries, reach out to [Mehdi El Jouhfi](mailto:midojouhfi@gmail.com)
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Made with �� and sweat by Mehdi El Jouhfi