3 Repos
Awesome GitHub RepositoriesTensor Communication Batching
Grouping multiple small tensor updates into larger buffers to minimize network round trips.
Distinct from Query Batching: Distinct from Query Batching: specifically applies to the batching of tensor gradients for network efficiency in ML.
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Awesome Tensor Communication Batching GitHub Repositories
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- uber/horovod
uber/horovod
14,686Auf GitHub ansehenHorovod is a distributed deep learning framework designed to scale machine learning training across multiple GPUs and nodes. It functions as an orchestrator for multi-GPU scaling and a tool for distributed gradient averaging, allowing users to increase compute capacity without rewriting core model logic. The project provides a consistent communication interface that supports multi-framework model distribution across TensorFlow, PyTorch, Keras, and MXNet. It leverages an MPI distributed training library to synchronize gradients across processes using collective communication operations. The s
Groups multiple small gradient updates into a single large buffer to reduce network communication frequency.
Python - horovod/horovod
horovod/horovod
14,686Auf GitHub ansehenHorovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across multiple GPUs and compute nodes. It functions as a distributed training orchestrator and an elastic training engine, utilizing an MPI collective communication library to synchronize weights and gradients across TensorFlow, PyTorch, Keras, and MXNet models. The system distinguishes itself through dynamic elastic scaling, which allows it to adjust the number of active workers at runtime and recover from node failures. It optimizes communication efficiency using tensor fusion batchi
Groups multiple small tensors into larger buffers to reduce network overhead during gradient synchronization.
Python - lightly-ai/lightly
lightly-ai/lightly
3,684Auf GitHub ansehenLightly is a self-supervised learning framework and computer vision data curation tool designed to manage large image datasets and train models on unlabeled data. It functions as a PyTorch vision library and dataset management SDK, providing tools to convert raw images into high-dimensional vectors for similarity search, visualization, and feature extraction. The project implements a variety of self-supervised architectures, including MoCo, SimCLR, VICReg, Barlow Twins, and masked image modeling. It distinguishes itself by combining these learning frameworks with active learning capabilities,
Groups multiple augmented views of the same image into separate tensors within a single batch.
Pythoncomputer-visioncontrastive-learningcontributions-welcome
