memoraith 0.1.0

A lightweight model profiler for deep learning frameworks

Memoraith

Memoraith is a lightweight model profiler for deep learning frameworks, designed to help you optimize your neural network models by providing detailed insights into their performance characteristics.

Features

• Supports PyTorch and TensorFlow models
• Profiles memory usage (CPU and GPU)
• Measures computation time for each layer
• Detects bottlenecks and anomalies
• Generates comprehensive reports with visualizations
• Provides real-time visualization capabilities
• Offers both programmatic and command-line interfaces

Installation

You can install Memoraith using pip:

pip install memoraith

For GPU support, install with:

pip install memoraith[gpu]

Quick Start

Here's a simple example of how to use Memoraith with a PyTorch model:

from memoraith import profile_model, set_output_path
import torch
import torch.nn as nn

set_output_path('profiling_results/')

class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc = nn.Linear(10, 5)

    def forward(self, x):
        return self.fc(x)

@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, 10)
        output = model(input_data)
        loss = output.sum()
        loss.backward()
        optimizer.step()

if __name__ == "__main__":
    model = SimpleNet()
    train_model(model)

This will generate a profiling report in the 'profiling_results/' directory.

Documentation

For more detailed information on how to use Memoraith, please refer to our documentation.

Contributing

We welcome contributions! Please see our Contributing Guide for more details.

License

Memoraith is released under the MIT License. See the LICENSE file for more details.

Support

If you encounter any issues or have questions, please file an issue on the GitHub issue tracker.

Citing Memoraith

If you use Memoraith in your research, please cite it as follows:

@software{memoraith,
  author = {Your Name},
  title = {Memoraith: A Lightweight Model Profiler for Deep Learning},
  year = {2023},
  url = {https://github.com/yourusername/memoraith}
}