Summary of Fastclip: a Suite Of Optimization Techniques to Accelerate Clip Training with Limited Resources, by Xiyuan Wei et al.
FastCLIP: A Suite of Optimization Techniques to Accelerate CLIP Training with Limited Resources
by Xiyuan Wei, Fanjiang Ye, Ori Yonay, Xingyu Chen, Baixi Sun, Dingwen Tao, Tianbao Yang
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Existing studies on training Contrastive Language-Image Pretraining (CLIP) models on large-scale data rely on hundreds or thousands of GPUs due to the requirement of a large batch size. However, this limits accessibility for most people. To address this issue, this paper explores various aspects of CLIP training with limited resources, up to tens of GPUs. The authors introduce FastCLIP, a framework built on advanced compositional optimization techniques designed for distributed settings and equipped with an efficient gradient reduction strategy. They also investigate three components of the framework from an optimization perspective: inner learning rate schedule, temperature parameter update rules, and model parameters. Experiments demonstrate the efficiency gains of FastCLIP compared to the state-of-the-art OpenCLIP baseline on different compute scales (up to 32 GPUs) and data sizes (2.7 million to 315 million image-text pairs). The authors release the code for FastCLIP at https://github.com/Optimization-AI/fast_clip. This paper’s contributions include a general CLIP training framework, efficient gradient reduction strategy, and optimized component scheduling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to train computers to understand images and text. Right now, this requires a lot of powerful computers and data, which not everyone has access to. The authors want to change that by developing a new way to train these computers, called FastCLIP, that uses fewer resources. They also experimented with different ways to make the training process more efficient. By doing so, they hope to make it possible for more people to use these powerful computer vision models, which can be used for things like recognizing objects in images and understanding what’s going on in videos. |
Keywords
* Artificial intelligence * Optimization * Pretraining * Temperature
