Making large AI models cheaper, faster and more accessible
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Updated
Jun 13, 2024 - Python
Making large AI models cheaper, faster and more accessible
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark.
飞桨大模型开发套件,提供大语言模型、跨模态大模型、生物计算大模型等领域的全流程开发工具链。
LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
Easy Parallel Library (EPL) is a general and efficient deep learning framework for distributed model training.
Ternary Gradients to Reduce Communication in Distributed Deep Learning (TensorFlow)
Large scale 4D parallelism pre-training for 🤗 transformers in Mixture of Experts *(still work in progress)*
Distributed Keras Engine, Make Keras faster with only one line of code.
Distributed training (multi-node) of a Transformer model
SC23 Deep Learning at Scale Tutorial Material
Official Repository for the paper: Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
Understanding the effects of data parallelism and sparsity on neural network training
A fully distributed hyperparameter optimization tool for PyTorch DNNs
WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.
Example of Distributed pyTorch
Towards Rehearsal-based Continual Learning at Scale: distributed CL with Horovod + PyTorch
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