OpenDiLoCo

An open-source implementation for distributed low-bandwidth AI model training

PremiumNewProductProductivityDistributed TrainingOpen Source

OpenDiLoCo is an open-source framework that implements and extends DeepMind’s Distributed Low-Bandwidth (DiLoCo) method, supporting global distributed AI model training. It makes it possible to efficiently train AI models in areas with scattered resources by providing a scalable and decentralized framework, which is significant for promoting the普及 and innovation of AI technology.

Supports global distributed AI model training.
Enables node communication and metadata synchronization through the Hivemind library.
Integrates with PyTorch FSDP
allowing a single DiLoCo worker node to scale to hundreds of machines.
Demonstrates the practicality of model training over two continents and three countries
maintaining 90-95% computational utilization.
Delivers deep insights into the scalability and computational efficiency of algorithms through ablation studies.
Supports fault-tolerant training across different hardware setups.
Offers real-time addition and removal of resources
allowing new devices and clusters to join or leave during training.

OpenDiLoCo is designed for researchers and developers who require global AI model training
particularly those teams limited by local computing resources. It breaks down the barrier of AI technology development
which was previously confined to institutions with large computing clusters
thereby promoting the democratization and innovation of AI technology.

Researchers use OpenDiLoCo to collaborate on the development of large language models across training nodes in different countries.
Educational institutions use the framework for distributed teaching
involving students in the AI model training process.
Enterprises train customized AI solutions globally through OpenDiLoCo in data centers.

1. Ensure access to at least two GPUs
which do not need to be in the same location.
2. Set up the environment and create an initial DHT node using the provided commands on another terminal.
3. Start the DiLoCo worker node on another terminal using the specified environment variables.
4. Set the PEER

Visit

OpenDiLoCo Visit Over Time

Monthly Visits

115445

Bounce Rate

51.17%

Page per Visit

3.2

Visit Duration

00:02:04

OpenDiLoCo Visit Trend

OpenDiLoCo Visit Geography

OpenDiLoCo Traffic Sources

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