There are 1 repository under horovod topic.
TonY is a framework to natively run deep learning frameworks on Apache Hadoop.
Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
A curated list of dedicated resources and applications
Neural network based solvers for partial differential equations and inverse problems :milky_way:. Implementation of physics-informed neural networks in pytorch.
Distributed, mixed-precision training with PyTorch
Code for tutorials and examples
GPU scheduler for elastic/distributed deep learning workloads in Kubernetes cluster
A text classification example using ddp horovod and accelerate
Create Horovod cluster easily using Ansible
Distributed training of digital pathology tissue slide images using SageMaker and Horovod.
Reimplement Deep Cell with Keras and Horovod.
This is a sub-repository in building to create acoustic model in Mandarin speech recognition.
Experiments with low level communication patterns that are useful for distributed training.
Simple bash script to launch gridsearch qsub jobs on PBS
Scaling Unet in Pytorch
Distributed training framework for TensorFlow, Keras
Distributed training with Batch AI
Segmenting EM-shower particles and track particles using Unet and Horovod
GPU Optimized version of AI Radiologist
SHUKUN Technology Co.,Ltd Algorithm intern (2020/12-2021/5). Multi-GPU, Multi-node training for deep learning models. Horovod, NVIDIA clara train sdk, configuration tutorial,performance testing.
Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of Horovod is to make distributed deep learning fast and easy to use.
This repository contains the code for RNNs which are trained for 3 bits Flip-flop task
Scaling Unet in Tensorflow
Proxy application for analyzing dynamical systems.
Horovod Tutorial for Pytorch using NVIDIA-Docker.
Making the official ludwigai/ludwig-ray-gpu image available for jupyterhub.
NGCF(Neural Graph Collaborative Filtering) Pytorch & Horovod implementation
Training Deep Learning models made easy