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Found 7 papers, 1 papers with code
AntBatchInfer: Elastic Batch Inference in the Kubernetes Cluster
Offline batch inference is a common task in the industry for deep learning applications, but it can be challenging to ensure stability and performance when dealing with large amounts of data and complicated inference pipelines.
AntDT: A Self-Adaptive Distributed Training Framework for Leader and Straggler Nodes
Previous works only address part of the stragglers and could not adaptively solve various stragglers in practice.
M2-Encoder: Advancing Bilingual Image-Text Understanding by Large-scale Efficient Pretraining
Vision-language foundation models like CLIP have revolutionized the field of artificial intelligence.
Ranked #1 on
Zero-shot Image Retrieval
on Flickr30k-CN
(using extra training data)
Zero-Shot Cross-Modal Retrieval
Zero-shot Image Retrieval
+3
G-Meta: Distributed Meta Learning in GPU Clusters for Large-Scale Recommender Systems
However, the existing systems are not tailored for meta learning based DLRM models and have critical problems regarding efficiency in distributed training in the GPU cluster.
An Adaptive Placement and Parallelism Framework for Accelerating RLHF Training
Furthermore, our framework provides a simple user interface and allows for the agile allocation of models across devices in a fine-grained manner for various training scenarios, involving models of varying sizes and devices of different scales.
Rethinking Memory and Communication Cost for Efficient Large Language Model Training
In this paper, we rethink the impact of memory consumption and communication costs on the training speed of large language models, and propose a memory-communication balanced strategy set Partial Redundancy Optimizer (PaRO).
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML).
