A collection of Python-based tools and utilities built on PyTorch to streamline machine learning workflows.
Clone this repository and install dependencies from requirements.txt. Alternatively, install via pyproject.toml using pip.
There are no scripts in this project. Instead, import the modules and classes as needed.
mltools/
├── docker # Docker-related files
│ └── Dockerfile # Docker configuration file
├── LICENSE # License file
├── mltools # Main package directory
│ ├── attention.py # Attention mechanisms
│ ├── bayesian.py # Bayesian methods
│ ├── clustering.py # Clustering algorithms
│ ├── cnns.py # Convolutional Neural Networks
│ ├── deepset.py # Deep set operations
│ ├── diffusion.py # Diffusion models
│ ├── flows.py # Flow-based models
│ ├── hydra_utils.py # Hydra configuration utilities
│ ├── lightning_utils.py # PyTorch Lightning utilities
│ ├── loss.py # Loss functions
│ ├── mlp.py # Multi-Layer Perceptrons
│ ├── modules.py # Custom neural network modules
│ ├── numpy_utils.py # NumPy utilities
│ ├── optimisers.py # Optimizers
│ ├── plotting.py # Plotting utilities
│ ├── schedulers.py # Learning rate schedulers
│ ├── torch_utils.py # PyTorch utilities
│ ├── transformers.py # Transformer models
│ └── utils.py # General utilities
├── pyproject.toml # Project configuration file
├── README.md # Readme file
├── requirements.txt # Dependencies file
└── tests # Unit tests directory
├── test_cnns.py # Tests for CNNs
├── test_flows.py # Tests for flow-based models
├── test_loss.py # Tests for loss functions
├── test_mlp.py # Tests for MLPs
├── test_modules.py # Tests for custom modules
├── test_optimisers.py # Tests for optimizers
└── test_transformers.py # Tests for transformers
A ready-to-use container setup can be found in Dockerfile.
Unit tests are in tests. Run them via PyTest after installation:
pytestThis project is licensed under the MIT License.