Here are
28 public repositories
matching this topic...
A Flexible and Powerful Parameter Server for large-scale machine learning
Updated
May 31, 2022
Java
Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.
extremely distributed machine learning
Updated
Apr 12, 2022
Scala
自己实现的深度学习训练框架,纯java实现,没有过多的第三方依赖,可分布式训练
Updated
Jul 30, 2018
Java
A hierarchical parameter server framework based on MXNet. GeoMX also implements multiple communication-efficient strategies.
OpenEmbedding is an open source framework for Tensorflow distributed training acceleration.
WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.
Updated
Mar 25, 2022
Python
A fully adaptive, zero-tuning parameter server
Distributed Fieldaware Factorization Machines based on Parameter Server
A parameter server implement with MPI.
DDLS is a parameter server based Distributed Deep Learning Studio for training deep learning models on Big Data with a numbers of machines and deploying high-performance online model service
Updated
Mar 20, 2017
Scala
Serving layer for large machine learning models on Apache Flink
Machine Learning models for large datasets
Updated
Jan 19, 2018
Gnuplot
a simple machine learning library
Updated
Jul 2, 2017
Terra
ROS utility package for build-time configuration file generation and dumping/restoring contents of ROS parameter server to/from ROS bags.
A demonstration app of the parameter server implementation for gSMFRETda.
Updated
Jan 27, 2022
Python
A simple and basic implement of parameter server for caffe.
A lightweight community-aware heterogeneous parameter server paradigm.
A lightweight parameter server interface
ADMM on LIBBLE by Parameter Server
A parameter server compatible with PyTorch optimizers.
Updated
Jul 20, 2018
Python
Distributed training with Multi-worker & Parameter Server in TensorFlow 2
Updated
Feb 25, 2022
Jupyter Notebook
Improving Performance for Distributed SGD using Ray
Updated
May 21, 2020
Python
Updated
May 5, 2022
Python
Parameter Server + One-sided communication + Bounded Staleness
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As of now we don't count 1) cost of bandwidth from S3 to the Cirrus workers, 2) cost of S3 requests.
The cost of requests can be expensive for very high IOPS.