Machine Learning Systems

MoE-Infinity: Activation-Aware Expert Offloading for Efficient MoE Serving

This paper presents MoE-Infinity, a cost-efficient mixture-of-expert (MoE) serving system that realizes activation-aware expert offloading. MoE-Infinity features sequence-level expert activation tracing, a new approach adept at identifying sparse …

ServerlessLLM: Locality-Enhanced Serverless Inference for Large Language Models

This paper presents ServerlessLLM, a locality-enhanced serverless inference system for Large Language Models (LLMs). ServerlessLLM exploits the substantial capacity and bandwidth of storage and memory devices available on GPU servers, thereby …

TorchOpt: An Efficient Library for Differentiable Optimization

Differentiable optimization algorithms often involve expensive computations of various meta-gradients. To address this, we design and implement TorchOpt, a new PyTorch-based differentiable optimization library. TorchOpt provides an expressive and …

GEAR: A GPU-Centric Experience Replay System for Large Reinforcement Learning Models

This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers). With such models, existing systems such as Reverb face …

Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness

Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number of sampled graph nodes and aggregated GNN features. This makes it …

Ekko: A Large-Scale Deep Learning Recommender System with Low-Latency Model Update

Deep Learning Recommender Systems (DLRSs) need to update models at low latency, thus promptly serving new users and content. Existing DLRSs, however, fail to do so. They train/validate models offline and broadcast entire models to global inference …

A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning

Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations. In this paper, we develop a …

MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment

Large-scale Bundle Adjustment (BA) requires massive memory and computation resources which are difficult to be fulfilled by existing BA libraries. In this paper, we propose MegBA, a GPU-based distributed BA library. MegBA can provide massive …

Efficient Reinforcement Learning Development with RLzoo

Many multimedia developers are exploring for adopting Deep Re- inforcement Learning (DRL) techniques in their applications. They however often find such an adoption challenging. Existing DRL libraries provide poor support for prototyping DRL agents …

Fast and Flexible Human Pose Estimation with HyperPose

Estimating human pose is an important yet challenging task in multimedia applications. Existing pose estimation libraries target reproducing standard pose estimation algorithms. When it comes to customising these algorithms for real-world …