moolib: A Platform for Distributed RL
We present moolib, a library that enables the implementation of distributed reinforcement learning and other machine learning codebases. Our implementation aims to be both simple and scalable, targeting researchers with a wide range of available computing resources, e.g., from individual to hundreds of GPUs. moolib is build around efficient remote procedure calls (RPCs) for both tensor and non-tensor data. Together with the moolib library, we present example user code which shows how moolib’s components can be used to implement common reinforcement learning agents as a simple but scalable distributed network of homogeneous peers. Together with this whitepaper, moolib and its examples are provided as open source code at our repository at github.com/facebookresearch/moolib.
Code (1)
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time. Despite recent progress in the field, reproducibility issues have not been s…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningA Modular Platform For Collaborative, Distributed Sensor Fusion
Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are.…
Sensor FusionEasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition
We present EasyASR, a distributed machine learning platform for training and serving large-scale Automatic Speech Recognition (ASR) models, as well as collecting and processing audio data at scale. Our platform is built …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)BIG-bench Machine LearningGPU+2Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms
This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are …
Deep LearningPrivacy PreservingPrivacy Preserving Deep LearningASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale
As deep learning models and input data are scaling at an unprecedented rate, it is inevitable to move towards distributed training platforms to fit the model and increase training throughput. State-of-the-art approaches …