To achieve high accuracy when performing deep learning, it is necessary to use a large-scale training model. However, due to the limitations of GPU memory, it is difficult to train large-scale training models within a single GPU. NVIDIA introduced a technology called CUDA Unified...
A new generation of the distributed deep learning platform, known as Angel 4.0, was built by Peking University and Tencent for deep learning training based on massive training data and large-scale model parameters. In August, it was announced that the self-developed deep learning framework Hetu w...
[论文] | Graph Foundation Models Mr.nobody 论文地址: 文章探讨了图基础模型(Graph Foundation Model , GFM)的概念,总结了其主要挑战为:如何将其泛化到不同的图结构以及任务上。这是图领域的一个新兴的研究热点,但仍然处于起… 阅读全文 赞同 2 ...
Identifying performance gaps:The first step towards analyzing the performance of large-scale deep learning models is to identify whether there exist any performance inefficiencies that require further optimizations. As an example, the NVIDIA Tesla V100 GPU provides a total...
of a standard AlphaFold2 training run, the logit tensor produced in just one of these variants–one designed to attend over the deep protein MSAs fed to the model as input–is in excess of 12GB in half precision alone, dwarfing the peak ...
ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. 在这项工作中,我们研究了卷积网络深度在大规模的图像识别环境下对准确性的影响。我们的主要贡献是使用非常小的(3×3)卷积滤波器架构对网络深度的增加进行了全面评估,这表明通过...
We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architectures for common tasks in 3D learning such as convolution, pooling, attention, ray-tracing, meshing, etc. fVDB sim...
The primary challenge in the development of large-scale artificial intelligence (AI) systems lies in achieving scalable decision-making—extending the AI models while maintaining sufficient performance. Existing research indicates that distributed AI can
Execution framework for multi-task model parallelism. Enables the training of arbitrarily large models with a single GPU, with linear speedups for multi-gpu multi-task execution. machine-learningdeep-learninggpudistributed-computingpytorchmachine-learning-librarytask-parallelsystems-engineeringlarge-scale-machi...
PaddleHelix is a bio-computing tool, taking advantage of the machine learning approaches, especially deep neural networks, for facilitating the development of the following areas: Drug Discovery. Provide 1) Large-scale pre-training models: compounds and proteins; 2) Various applications: molecular prop...