helios-ml 1.4.1

A light-weight system for training AI networks using PyTorch

What is Helios?

Named after Greek god of the sun, Helios is a light-weight package for training ML networks built on top of PyTorch. It is designed to abstract all of the “boiler-plate” code involved with training. Specifically, it wraps the following common patterns:

• Creation of the dataloaders.

• Initialization of CUDA, PyTorch, and random number states.

• Initialization for distributed training.

• Training, validation, and testing loops.

• Saving and loading checkpoints.

• Exporting to ONNX.

It is important to note that Helios is not a fully fledged training environment similar to Pytorch Lightning. Instead, Helios is focused on providing a simple and straight-forward interface that abstracts most of the common code patterns while retaining the ability to be easily overridden to suit the individual needs of each training scheme.

Main Features

Helios offers the following functionality out of the box:

1. Resume training: Helios has been built with the ability to resume training if it is paused. Specifically, Helios will ensure that the behaviour of the trained model is identical to the one it would’ve had if it had been trained without pauses.

2. Automatic detection of multi-GPU environments for distributed training. In addition, Helios also supports training using torchrun and will automatically handle the initialisation and clean up of the distributed state. It will also correctly set the devices and maps to ensure weights are mapped tot he correct location.

3. Registries for creation of arbitrary types. These include: networks, loss functions, optimizers, schedulers, etc.

4. Correct handling of logging when doing distributed training (even over multiple nodes).

5. Native integration with Optuna for hyper-parameter optimisation. Also supports resuming studies and generating checkpoints to ensure reproducibility.

Installation

Dependencies

Helios requires:

• Python (>= 3.11)

• TQDM (>= 4.66.0)

• OpenCV (>= 4.10.0.84)

• Tensorboard (>= 2.17.0)

• PyTorch (>= 2.5.0)

• Torchvision (>= 0.20.0)

• ONNX (>= 1.16.0)

• ONNXRuntime (>= 1.19.0)

• Matplotlib (>= 3.9.2)

• Numpy (>= 2.1.0)

User Installation

You can install Helios using pip:

pip install -U helios-ml

If you require a specific version of CUDA, you can install with:

pip install -U helios-ml --extra-index-url https://download.pytorch.org/whl/cu<version>

Documentation

Documentation available here.

Contributing

There are three ways in which you can contribute to Helios:

• If you find a bug, please open an issue. Similarly, if you have a question about how to use it, or if something is unclear, please post an issue so it can be addressed.

• If you have a fix for a bug, or a code enhancement, please open a pull request. Before you submit it though, make sure to abide by the rules written below.

• If you have a feature proposal, you can either open an issue or create a pull request. If you are submitting a pull request, it must abide by the rules written below. Note that any new features need to be approved by me.

If you are submitting a pull request, the guidelines are the following:

1. Ensure that your code follows the standards and formatting of Helios. The coding standards and formatting are enforced through the Ruff Linter and Formatter. Any changes that do not abide by these rules will be rejected. It is your responsibility to ensure that both Ruff and Mypy linters pass.

2. Ensure that all unit tests are working prior to submitting the pull request. If you are adding a new feature that has been approved, it is your responsibility to provide the corresponding unit tests (if applicable).

License

Helios is published under the BSD-3 license and can be viewed here.