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An interference-aware scheduler for fine-grained GPU sharing

Artifact of OSDI '24 paper, ”Llumnix: Dynamic Scheduling for Large Language Model Serving“

LightLLM is a Python-based LLM (Large Language Model) inference and serving framework, notable for its lightweight design, easy scalability, and high-speed performance.

Official repository for the paper DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

[TMLR] A curated list of language modeling researches for code (and other software engineering activities), plus related datasets.

Ongoing research training transformer language models at scale, including: BERT & GPT-2

An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

A Quasi-Wasserstein Loss for Learning Graph Neural Networks (QW loss)

FedScale is a scalable and extensible open-source federated learning (FL) platform.

ATC23 AE

A curated reading list of research in Mixture-of-Experts(MoE).

Compiler for Dynamic Neural Networks

A curated reading list of research in Adaptive Computation, Inference-Time Computation & Mixture of Experts (MoE).

A fast MoE impl for PyTorch

Custom Python Scheduler for Kubernetes

⏰ Collaboratively track worldwide conference deadlines (Website, Python Cli, Wechat Applet) / If you find it useful, please star this project, thanks~

📄 🇨🇳 📃 论文阅读笔记（分布式系统、虚拟化、机器学习）Papers Notebook (Distributed System, Virtualization, Machine Learning)

Code for "Heterogenity-Aware Cluster Scheduling Policies for Deep Learning Workloads", which appeared at OSDI 2020

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

Kubernetes Scheduler for Deep Learning

GPU-scheduler-for-deep-learning

Tools for monitoring NVIDIA GPUs on Linux

GPU Sharing Scheduler for Kubernetes Cluster

Repository for benchmarking