Initial commit for Memoraith v0.1.0

.github/workflows/ci.yml

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+name: Memoraith CI
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8, 3.9, '3.10']
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+      - name: Run tests
+        run: |
+          pytest tests/
+      - name: Lint with flake8
+        run: |
+          pip install flake8
+          flake8 .
+      - name: Check type hints with mypy
+        run: |
+          pip install mypy
+          mypy memoraith/

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# Environment
+.env
+.venv
+env/
+venv/
+ENV/
+
+# IDE
+.vscode/
+.idea/
+
+# Memoraith specific
+memoraith_reports/
+*profiling_results/

.pypirc

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+[distutils]
+index-servers =
+    pypi
+
+[pypi]
+repository: https://upload.pypi.org/legacy/
+username: __token__
+password: AgEIcHlwaS5vcmcCJGM1MWFhNzNiLTEwYWMtNGY2NS05Y2IzLTY0ZWEwYWNiYmNjNAACKlszLCI5MjdjMzIwZC1mMzE3LTQ3N2YtYmQ2MC02OTQ4NjMyMWYwZTYiXQAABiDPAHUyjQvh2o5rbrII2gDJu6clMbqadbt3r0oCDy62Mw

CONTRIBUTING.md

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+# Contributing to Memoraith
+
+We welcome contributions to Memoraith! This document provides guidelines for contributing to the project.
+
+## Setting up the development environment
+
+1. Fork the repository on GitHub.
+2. Clone your fork locally:
+   ```
+   git clone https://github.com/your-username/memoraith.git
+   cd memoraith
+   ```
+3. Create a virtual environment and install dependencies:
+   ```
+   python -m venv venv
+   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+   pip install -r requirements.txt
+   ```
+
+## Making Changes
+
+1. Create a new branch for your changes:
+   ```
+   git checkout -b your-feature-branch
+   ```
+2. Make your changes and commit them with a clear commit message.
+3. Push your changes to your fork on GitHub:
+   ```
+   git push origin your-feature-branch
+   ```
+4. Open a pull request from your fork to the main Memoraith repository.
+
+## Coding Standards
+
+- Follow PEP 8 guidelines for Python code.
+- Use type hints for all function arguments and return values.
+- Write clear, concise docstrings for all classes and functions.
+- Add unit tests for new functionality.
+
+## Running Tests
+
+Run the test suite using pytest:
+
+```
+pytest

LICENSE

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+MIT License
+
+Copyright (c) 2023 Your Name
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Manifest.in

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+include README.md
+include LICENSE
+recursive-include memoraith/templates *.html
+recursive-include examples *.py

README.md

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+# 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:
+
+```bash
+pip install memoraith
+```
+
+For GPU support, install with:
+
+```bash
+pip install memoraith[gpu]
+```
+
+## Quick Start
+
+Here's a simple example of how to use Memoraith with a PyTorch model:
+
+```python
+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](https://memoraith.readthedocs.io).
+
+## Contributing
+
+We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for more details.
+
+## License
+
+Memoraith is released under the MIT License. See the [LICENSE](LICENSE) file for more details.
+
+## Support
+
+If you encounter any issues or have questions, please file an issue on the [GitHub issue tracker](https://github.com/yourusername/memoraith/issues).
+
+## 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}
+}
+```

blabla.py

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+import os
+import re
+
+def create_or_update_file(file_path, content):
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    with open(file_path, 'w', encoding='utf-8') as file:
+        file.write(content)
+    print(f"Created/Updated: {file_path}")
+
+def extract_classes(content):
+    class_pattern = r'(class\s+\w+[\s\S]*?(?=\n\n|$))'
+    return re.findall(class_pattern, content)
+
+def extract_functions(content):
+    function_pattern = r'(def\s+\w+[\s\S]*?(?=\n\n|$))'
+    return re.findall(function_pattern, content)
+
+def update_memoraith(base_path, content):
+    # Extract file contents
+    file_pattern = r'# (memoraith/.*?\.py)\n\n([\s\S]*?)(?=\n# memoraith/|\Z)'
+    matches = re.findall(file_pattern, content)
+
+    for file_path, file_content in matches:
+        full_path = os.path.join(base_path, file_path)
+
+        # Extract classes and functions
+        classes = extract_classes(file_content)
+        functions = extract_functions(file_content)
+
+        # Combine classes and functions
+        components = classes + functions
+
+        # Create or update the file
+        create_or_update_file(full_path, "\n\n".join(components))
+
+if __name__ == "__main__":
+    base_path = r"C:\Users\PC\Desktop\Leo-Major\Memoraith"
+    with open(r"C:\Users\PC\Desktop\Leo-Major\alop.txt", 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    update_memoraith(base_path, content)
+    print("Memoraith project has been updated successfully!")

examples/example_pytorch.py

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+# PyTorch example

examples/example_tensorflow.py

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+import tensorflow as tf
+from memoraith import profile_model, set_output_path
+
+def create_model():
+    model = tf.keras.Sequential([
+        tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),
+        tf.keras.layers.Dense(64, activation='relu'),
+        tf.keras.layers.Dense(1)
+    ])
+    return model
+
+@profile_model(memory=True, computation=True, gpu=True)
+def train_model(model, epochs=5):
+    model.compile(optimizer='adam', loss='mse')
+
+    # Generate some dummy data
+    x_train = tf.random.normal((1000, 10))
+    y_train = tf.random.normal((1000, 1))
+
+    model.fit(x_train, y_train, epochs=epochs, verbose=1)
+
+if __name__ == "__main__":
+    set_output_path('tensorflow_profiling_results/')
+    model = create_model()
+    train_model(model)

memoraith/__init__.py

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+"""
+Memoraith: Lightweight Model Profiler
+"""
+
+__version__ = '0.3.0'
+
+from .profiler import profile_model, set_output_path
+from .config import Config
+from .exceptions import MemoraithError
+from .visualization.real_time_visualizer import RealTimeVisualizer

memoraith/analysis/__init__.py

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+from .analyzer import Analyzer
+from .metrics import MetricsCalculator
+from .bottleneck import BottleneckDetector
+from .recommendations import RecommendationEngine
+from .anomaly_detection import AnomalyDetector