DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
machine-learning
deep-learning
pytorch
gpu
compression
billion-parameters
data-parallelism
inference
mixture-of-experts
model-parallelism
pipeline-parallelism
trillion-parameters
zero
Обновлено 2024-11-20 04:04:47 +03:00
Metadata for Azure HPC Extensions
Обновлено 2024-11-19 02:38:24 +03:00
Resource scheduling and cluster management for AI
machine-learning
tensorflow
ai
jupyter
pytorch
artificial-intelligence
model-training
kubernetes
gpu-scheduler
on-premise
resource-management
scheduling
chainer
cloud
cluster-management
cluster-manager
gpu
gpu-cluster
gpu-computing
Обновлено 2024-06-06 10:56:06 +03:00
Simplify HPC and Batch workloads on Azure
azure
docker
azure-functions
containers
serverless
gpu
azure-batch
hpc
infiniband
windows-containers
mpi
slurm
rdma
batch-processing
glusterfs
nfs
singularity
Обновлено 2023-03-21 00:31:21 +03:00
A highly extensible software stack to empower everyone to build practical real-world live video analytics applications for object detection and counting with cutting edge machine learning algorithms.
azure
docker
dotnet-core
tensorflow
object-detection
gpu
counting
edge-computing
video-analytics
yolov3
Обновлено 2022-07-26 04:18:01 +03:00
Svirl is GPU-accelerated solver of complex Ginzburg-Landau equations for superconductivity. It consists of time-dependent solver to describe vortex dynamics and free energy minimizer to accurately find static configurations.
Обновлено 2021-07-23 21:41:12 +03:00
AKS Deployment Tutorial
Обновлено 2020-02-12 05:11:52 +03:00
Distributed Deep Learning using AzureML
Обновлено 2019-11-19 05:28:26 +03:00
Show how to perform fast retraining with LightGBM in different business cases
azure
machine-learning
benchmark
gpu
lightgbm
distributed-systems
gbdt
xgboost
gbm
gbrt
kaggle
boosted-trees
Обновлено 2019-07-18 11:16:44 +03:00
Tutorial on how to deploy Deep Learning models on GPU enabled Kubernetes cluster
Обновлено 2019-02-01 22:04:55 +03:00