Cocktailsgd: Fine-tuning foundation models over 500mbps networks
Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …
communication-intensive and so has heavily relied on centralized data centers with fast …
Fine-tuning language models over slow networks using activation quantization with guarantees
Communication compression is a crucial technique for modern distributed learning systems
to alleviate their communication bottlenecks over slower networks. Despite recent intensive …
to alleviate their communication bottlenecks over slower networks. Despite recent intensive …
Fine-tuning language models over slow networks using activation compression with guarantees
Communication compression is a crucial technique for modern distributed learning systems
to alleviate their communication bottlenecks over slower networks. Despite recent intensive …
to alleviate their communication bottlenecks over slower networks. Despite recent intensive …
Exploring the robustness of decentralized training for large language models
Decentralized training of large language models has emerged as an effective way to
democratize this technology. However, the potential threats associated with this approach …
democratize this technology. However, the potential threats associated with this approach …
How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study
This paper aims to answer the question: Can deep learning models be cost-efficiently
trained on a global market of spot VMs spanning different data centers and cloud providers …
trained on a global market of spot VMs spanning different data centers and cloud providers …
Semantic parameter matching in Web APIs with Transformer-based question answering
S Kotstein, C Decker - 2023 IEEE International Conference on …, 2023 - ieeexplore.ieee.org
OpenAPI, WADL, RAML, and API Blueprint are popular formats for documenting Web APIs.
Although these formats are in general both human and machine-readable, only the part of …
Although these formats are in general both human and machine-readable, only the part of …
ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment
The advent of the Transformer architecture has propelled the growth of natural language
processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet …
processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet …
Position: Exploring the Robustness of Pipeline-Parallelism-Based Decentralized Training
Modern machine learning applications increasingly demand greater computational
resources for training large models. Decentralized training has emerged as an effective …
resources for training large models. Decentralized training has emerged as an effective …
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
Distributed training of foundation models, especially large language models (LLMs), is
communication-intensive and so has heavily relied on centralized data centers with fast …
communication-intensive and so has heavily relied on centralized data centers with fast …