Resource scheduling and cluster management for AI

Best practices & guides on how to write distributed pytorch training code

Mixed-vendor GPU inference cluster manager with speculative decoding

AI动力(AI Power) GPU云平台使用指南

qihoo360 xlearning with GPU support; AI on Hadoop

GPU management System on Kubernetes For AI, Deep-Learning, Machine-Learning Researcher.

An imperative command-line-interface for AI workload orchestration

Example code for deploying GPU workloads on ECS

Complete setup guide for a 2-node NVIDIA DGX Spark cluster — distributed training, CUDA inference with EXO, NCCL tuning for Grace Blackwell, NVMe-TCP shared storage, and 200 Gb/s direct fabric networking.

Open-source GPU operator agent for the Scalattice inference network (Rust).

Project "Springfield"

Detailed Analysis Traces for AI jobs leveraging spot GPU resources

Audit GPU cluster communication schedules from NCCL logs. Zero dependencies. CI-ready.

AI Inference Gateway - orchestrates Ollama, vLLM, cloud providers, and vision services into a unified, production-ready platform

Docker Images for the GPU Cluster

Generate reproducible deep-learning workload traces by mapping production GPU-cluster traces to profiled executable workloads.

Detailed Analysis Traces for GPU-Disaggregated Deep Learning Recommendation Models

Web UI for orchestrating distributed llama.cpp RPC GPU clusters with auto node discovery, telemetry, and one-click deployment.