Summary of Tensorsocket: Shared Data Loading For Deep Learning Training, by Ties Robroek (it University Of Copenhagen) et al.
TensorSocket: Shared Data Loading for Deep Learning Training
by Ties Robroek, Neil Kim Nielsen, Pınar Tözün
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of efficient deep learning model training by addressing the repetition and resource intensity associated with hyper-parameter tuning and neural architecture search. The authors focus on optimizing the supply chain of training data to improve computational efficiency, acknowledging that repetitive tasks exacerbate the need for and costs of computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to make deep learning model training more efficient by streamlining the process of supplying training data. By reducing repetition and resource waste, researchers hope to make this process more feasible for data scientists. |
Keywords
» Artificial intelligence » Deep learning
