A Flexible and Powerful Parameter Server for large-scale machine learning
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Updated
Jan 16, 2024 - Java
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
Serverless ML Framework
Distributed Fieldaware Factorization Machines based on Parameter Server
A fully adaptive, zero-tuning parameter manager that enables efficient distributed machine learning training
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Serving layer for large machine learning models on Apache Flink
PetPS: Supporting Huge Embedding Models with Tiered Memory
Distributed training with Multi-worker & Parameter Server in TensorFlow 2
A lightweight community-aware heterogeneous parameter server paradigm.
A demonstration app of the parameter server implementation for gSMFRETda.
Machine Learning models for large datasets
a simple machine learning library
A simple and basic implement of parameter server for caffe.
python lib for sparse parameter server using rocksdb, written in c++
WIP. Veloce is a low-code Ray-based parallelization library that makes machine learning computation novel, efficient, and heterogeneous.
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