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
A fully adaptive, zero-tuning parameter manager that enables efficient distributed machine learning training
Distributed Fieldaware Factorization Machines based on Parameter Server
OpenEmbedding is an open source framework for Tensorflow distributed training acceleration.
Educational distributed training and inference library for heterogeneous local hardware: Mac minis, Raspberry Pis and GPUs over plain Python sockets. FSDP, GRPO, parameter-server, expert and data parallelism.
Reduce the training time of CNNs by leveraging the power of multiple GPUs in 2 approaches, Multi-workers & Parameter Sever Training using TensorFlow 2
PetPS: Supporting Huge Embedding Models with Tiered Memory
Serving layer for large machine learning models on Apache Flink
A lightweight community-aware heterogeneous parameter server paradigm.
Extended DRAGON, a dynamic resource scheduler for deep learning, with Container Migration
a simple machine learning library
A simple and basic implement of parameter server for caffe.
Adaptive Hybrid Distributed SGD implements and evaluates Parameter Server, Ring AllReduce, and a runtime-adaptive hybrid that switches communication topology during model training based on live metrics.
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