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ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial intelligence models across clusters of hardware accelerators. It functions as a parallel computing engine that partitions model workloads and data across multiple processors to maximize memory efficiency and throughput. The platform distinguishes itself through a comprehensive suite of parallelization strategies, including multi-dimensional tensor parallelism and pipeline-based model parallelism, which segment neural network layers and stages across devices. To support large-scale genera
Mesh TensorFlow: Model Parallelism Made Easier
This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in pointnet/model.py.
Train a deep learning net with OpenStreetMap features and satellite imagery.
"Neural Turing Machine" in Tensorflow
The main features of carpedm20/ntm-tensorflow are: Deep Learning Frameworks, Model Implementations.
Projects with overlapping indexed features include: tensorflow/mesh — Mesh TensorFlow: Model Parallelism Made Easier. trailbehind/deeposm — Train a deep learning net with OpenStreetMap features and satellite imagery. fxia22/pointnet.pytorch — This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in… hpcaitech/colossalai — ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial… meta-llama/llama-cookbook — This project is a collection of implementation guides, recipes, and developer resources for building applications with… adap/flower — Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across…