Hivemind: Decentralized Deep Learning in PyTorch
Hivemind is an innovative PyTorch library designed for decentralized deep learning. Its primary goal is to enable the training of large machine learning models across multiple computers from different institutions, companies, and independent contributors worldwide. This approach to deep learning allows for many computers to participate in the training process without relying on a central governing computer or 'master node'.
Key Features
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Distributed Training: Hivemind utilizes a Distributed Hash Table technology that allows computing devices to connect in a decentralized manner. This means that no single computer is needed to coordinate the process, making it more resilient and flexible.
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Fault-Tolerant Backpropagation: This feature ensures that the process of calculating gradients and updating model parameters will continue, even if some computers do not respond or are slower than others.
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Decentralized Parameter Averaging: This technique enables the aggregation of model updates from different computers without needing to stop and synchronize the entire network. It allows for a more seamless updating process.
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Train Large Neural Networks: By distributing parts of network layers across participating computers, Hivemind can handle neural networks of essentially any size using a technique called Decentralized Mixture-of-Experts.
Example Use Cases
Hivemind is already being leveraged in various extraordinary projects:
- Petals: A platform for the inference and fine-tuning of extensive language models over a decentralized network.
- Training Transformers Together: An initiative demonstrated at NeurIPS 2021, focusing on collaborative training of text-to-image transformer models.
- CALM: A language model specifically trained on Arabic datasets through decentralized collaboration.
- sahajBERT: A Bengali language modeling project that uses collaborative pretraining.
Installation
Hivemind is compatible with Python 3.8+ and PyTorch 1.9.0 or newer. It can be installed using pip or built from the source. Additionally, if you require enhanced data compression during transfers, the bitsandbytes library can be integrated for more efficient operations.
System Requirements
Hivemind is primarily developed for Linux systems, with Ubuntu 18.04+ being the recommended OS. It also offers partial support for macOS and experimental support for Windows 10+ through Windows Subsystem for Linux (WSL).
Documentation and Support
For getting started with Hivemind, there is a quickstart tutorial available, along with additional tutorials and API references. The community can offer support through a Discord chat or by raising issues on GitHub.
Contributing
Hivemind is open to contributions from the community. Whether it's fixing bugs, improving documentation, or even adding new features, everyone is encouraged to participate. Potential contributors can look at existing issues for inspiration or start a discussion on new ideas in the project's chat room.
Citation
If Hivemind is utilized in research, it's encouraged to cite the work properly using the bibliographic information provided. Additionally, several papers related to the technology and concepts employed by Hivemind are available for reference.
Hivemind represents an exciting step in the field of deep learning, enabling large-scale collaboration without centralized control, thus making powerful machine learning models more accessible and scalable.