Distributed Computing
43 papers with code • 0 benchmarks • 1 datasets
Benchmarks
MNIST-8M
Greatest papers with code
Optuna: A Next-generation Hyperparameter Optimization Framework
optuna/optuna
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We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications.
MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation.
Flexible and Scalable Deep Learning with MMLSpark
In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark.
Billion-scale Network Embedding with Iterative Random Projection
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods
Recent advances in machine learning are consistently enabled by increasing amounts of computation.
MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Our framework is comprised of three key components: (1) a centralized task dispatching model, which supports the self-generated tasks and scalable training with heterogeneous policy combinations; (2) a programming architecture named Actor-Evaluator-Learner, which achieves high parallelism for both training and sampling, and meets the evaluation requirement of auto-curriculum learning; (3) a higher-level abstraction of MARL training paradigms, which enables efficient code reuse and flexible deployments on different distributed computing paradigms.
A System for Massively Parallel Hyperparameter Tuning
Modern learning models are characterized by large hyperparameter spaces and long training times.
MANGO: A Python Library for Parallel Hyperparameter Tuning
Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually.
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
kunglab/ddnn
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In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.
