Vel provides a highly modular and configurable deep learning framework for researchers, aiming to streamline the development and experimentation of new models and algorithms. It offers a declarative YAML-based system for defining and connecting components, reducing the need for extensive boilerplate code and facilitating reproducibility.

How It Works

Vel is built around a core philosophy of modularity, allowing users to compose complex deep learning pipelines by wiring together pre-tested components. This approach emphasizes flexibility and reusability, enabling rapid prototyping and experimentation. The framework supports declarative configuration via YAML files, which specify model architectures, hyperparameters, and training workflows, but also allows for direct Python scripting for greater control.

Quick Start & Requirements

• Install via pip: pip install vel or pip install vel[gym,mongo,visdom]
• Requires Python >= 3.6 and PyTorch >= 1.0.
• Optional dependencies for YAML config examples: MongoDB (localhost:27017), Visdom (localhost:8097). A dummy config is provided to avoid these.
• Docker image available: millionintegrals/vel.
• Official blog post: https://blog.millionintegrals.com/vel-pytorch-meets-baselines/

Highlighted Details

• Implements a wide range of state-of-the-art models across Computer Vision, Natural Language Processing, and Reinforcement Learning (including A2C, PPO, DQN variants, TRPO, DDPG).
• Features a declarative YAML configuration system for defining and managing experiments.
• Provides extensive examples for various use cases, including RL Atari environments and NLP tasks.
• Supports both configuration-driven execution and direct Python API usage.

Maintenance & Community

• This is described as an early version and a hobby project by the author.
• Documentation is currently nonexistent, with clarity provided through code and examples.
• Projects using Vel are linked: https://github.com/MillionIntegrals/baselines-experiments, https://github.com/MillionIntegrals/vel-miniworld.

Licensing & Compatibility

• The README does not explicitly state a license. Given the GitHub repository context, it's likely MIT or a similar permissive license, but this requires verification.

Limitations & Caveats

The project is in an early stage with no official documentation, which may hinder adoption for users unfamiliar with the codebase or requiring extensive guidance. The author prioritizes modularity over simplicity, which could increase the learning curve for newcomers.