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A Flexible and Powerful Parameter Server for large-scale machine learning
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
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
Distributed Fieldaware Factorization Machines based on Parameter Server
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
Serving layer for large machine learning models on Apache Flink
Machine Learning models for large datasets
a simple machine learning library
A simple and basic implement of parameter server for caffe.
A lightweight community-aware heterogeneous parameter server paradigm.
A parameter server compatible with PyTorch optimizers.
Improving Performance for Distributed SGD using Ray
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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.