snuspl / cruiseLinks
Cruise: A Distributed Machine Learning Framework with Automatic System Configuration
☆26Updated 6 years ago
Alternatives and similar repositories for cruise
Users that are interested in cruise are comparing it to the libraries listed below
Sorting:
- ☆24Updated 6 years ago
- MIST: High-performance IoT Stream Processing☆17Updated 6 years ago
- Nemo: A flexible data processing system☆21Updated 7 years ago
- Tiresias is a GPU cluster manager for distributed deep learning training.☆158Updated 5 years ago
- Apache Nemo (Incubating) - Data Processing System for Flexible Employment With Different Deployment Characteristics☆112Updated 2 months ago
- Model-less Inference Serving☆91Updated last year
- A Federated Execution Engine for Fast Distributed Computation Over Slow Networks☆26Updated 4 years ago
- A Generic Resource-Aware Hyperparameter Tuning Execution Engine☆15Updated 3 years ago
- Fine-grained GPU sharing primitives☆143Updated last month
- Simulation code for the LHD cache replacement policy as published in NSDI 2018.☆25Updated 7 years ago
- PipeSwitch: Fast Pipelined Context Switching for Deep Learning Applications☆126Updated 3 years ago
- Crossbow: A Multi-GPU Deep Learning System for Training with Small Batch Sizes☆56Updated 2 years ago
- Virtual Memory Abstraction for Serverless Architectures☆48Updated 3 years ago
- ☆50Updated 8 months ago
- ☆84Updated 2 months ago
- An Efficient Dynamic Resource Scheduler for Deep Learning Clusters☆42Updated 7 years ago
- ☆21Updated 2 years ago
- Multi-Instance-GPU profiling tool☆59Updated 2 years ago
- ☆192Updated 6 years ago
- "JABAS: Joint Adaptive Batching and Automatic Scaling for DNN Training on Heterogeneous GPUs" (EuroSys '25)☆14Updated 4 months ago
- ☆12Updated 4 months ago
- [ACM EuroSys 2023] Fast and Efficient Model Serving Using Multi-GPUs with Direct-Host-Access☆57Updated 3 weeks ago
- ☆14Updated 2 years ago
- ☆22Updated 6 years ago
- ☆44Updated 3 years ago
- Code for "Heterogenity-Aware Cluster Scheduling Policies for Deep Learning Workloads", which appeared at OSDI 2020☆128Updated last year
- ☆24Updated 2 years ago
- Exploiting Cloud Services for Cost-Effective, SLO-Aware Machine Learning Inference Serving☆37Updated 5 years ago
- Artifact for "Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning" [NSDI '23]☆45Updated 2 years ago
- ☆51Updated 2 years ago