Source URL: https://simonwillison.net/2024/Aug/27/distro/#atom-everything
Source: Simon Willison’s Weblog
Title: NousResearch/DisTrO
Feedly Summary: NousResearch/DisTrO
DisTrO stands for Distributed Training Over-The-Internet – it’s “a family of low latency distributed optimizers that reduce inter-GPU communication requirements by three to four orders of magnitude".
This tweet from @NousResearch helps explain why this could be a big deal:
DisTrO can increase the resilience and robustness of training LLMs by minimizing dependency on a single entity for computation. DisTrO is one step towards a more secure and equitable environment for all participants involved in building LLMs.
Without relying on a single company to manage and control the training process, researchers and institutions can have more freedom to collaborate and experiment with new techniques, algorithms, and models.
Training large models is notoriously expensive in terms of GPUs, and most training techniques require those GPUs to be collocated due to the huge amount of information that needs to be exchanged between them during the training runs.
If DisTrO works as advertised it could enable SETI@home style collaborative training projects, where thousands of home users contribute their GPUs to a larger project.
There are more technical details in the PDF preliminary report shared by Nous Research on GitHub.
I continue to hate reading PDFs on a mobile phone, so I converted that report into GitHub Flavored Markdown (to ensure support for tables) and shared that as a Gist. I used Gemini 1.5 Pro (gemini-1.5-pro-exp-0801) in Google AI Studio with the following prompt:
Convert this PDF to github-flavored markdown, including using markdown for the tables. Leave a bold note for any figures saying they should be inserted separately.
Tags: gemini, pdf, generative-ai, ai, llms
AI Summary and Description: Yes
Summary: The text discusses DisTrO, a novel approach for distributed training of large language models (LLMs) that significantly reduces inter-GPU communication, potentially enhancing resilience and collaboration in AI training environments. It addresses the costs and logistics of LLM training and promotes a decentralized model that encourages collective contributions, akin to the SETI@home project.
Detailed Description: The content elaborates on DisTrO (Distributed Training Over-The-Internet), which is described as a family of low-latency distributed optimizers. This new methodology is poised to revolutionize how LLMs are trained by minimizing the dependency on single entities for GPU computation. Here are the major points of relevance:
– **Reduction in Communication Requirements**: DisTrO aims to decrease the inter-GPU communication needs by three to four orders of magnitude, which is a significant advancement for distributed training.
– **Resilience and Robustness**: By reducing reliance on a single corporate entity for training computations, DisTrO enhances the resilience and fairness of the process, allowing decentralization of AI model training.
– **Increased Collaboration**: This framework enables diverse researchers and institutions to collaborate and experiment with new techniques, algorithms, and models, signaling a shift towards more inclusivity in AI development.
– **Cost-Effective Training**: Traditional training methods for large models are expensive due to the need for colocated GPUs. DisTrO’s approach could alleviate some of the financial burdens associated with AI model training and open doors for participation from a broader range of contributors.
– **Community-Driven Projects**: The potential for projects like SETI@home, where thousands of users contribute their GPUs for large-scale training, introduces an innovative way to leverage community resources for AI development.
– **Technical Documentation**: There is a mention of a preliminary report available in PDF format on GitHub, detailing more technical aspects of DisTrO. The author also shares their experience in converting this PDF into GitHub Flavored Markdown for better accessibility and sharing.
The insights provided by the text emphasize how emerging technologies like DisTrO could democratize LLM training, aligning well with trends in AI decentralization and collaborative tech development. This relevance is especially notable for professionals engaged in AI, infrastructure security, and collaborative computing frameworks.