3 repository-uri
Awesome GitHub RepositoriesAutomatic ML Workload Batching
Automatic batching of GPU and ML workloads like text embeddings to achieve higher throughput without manual configuration.
Distinct from Automatic Batch Size Optimization: Distinct from Automatic Batch Size Optimization: focuses on batching entire workloads rather than just optimizing batch size for a single model.
Explore 3 awesome GitHub repositories matching artificial intelligence & ml · Automatic ML Workload Batching. Refine with filters or upvote what's useful.
Awesome Automatic ML Workload Batching GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- cocoindex-io/cocoindex
cocoindex-io/cocoindex
6,117Vezi pe GitHubCocoindex is an incremental data processing engine that builds and maintains live indexes for AI agents, with a core focus on codebase indexing and knowledge graph extraction. The engine uses a function-graph execution model where user-defined Python functions are composed into a directed acyclic graph, and it processes data incrementally so only changed source records or code paths are re-computed, avoiding full recomputation at any scale. It supports automatic schema inference from transformation pipeline type annotations and provides full data lineage tracing, tagging every output record wi
Automatically batches GPU and ML workloads like text embeddings for higher throughput.
Rustagentic-data-frameworkaiai-agents - reactwg/react-18
reactwg/react-18
5,195Vezi pe GitHubAcest proiect este un grup de lucru software colaborativ și un track de lansare axat pe dezvoltarea tehnică și implementarea actualizării bibliotecii React 18. Acesta servește ca efort de coordonare comunitară și forum de discuții pentru gestionarea milestone-urilor și a seturilor de funcționalități ale acestei versiuni majore a framework-ului frontend. Grupul de lucru facilitează coordonarea lansărilor open source și planificarea versiunilor software printr-un grup distribuit de contribuitori. Se concentrează pe colectarea feedback-ului tehnic din comunitate și gestionarea discuțiilor publice pentru a rafina codul și documentația bibliotecii înainte de o lansare oficială. Domeniul de dezvoltare acoperă randarea concurrentă a interfeței utilizator, gestionarea stării frontend și rafinarea logicii interne de reconciliere și randare.
Groups multiple state updates into a single render pass to improve performance and reduce repaints.
- llm-d/llm-d
llm-d/llm-d
2,514Vezi pe GitHubllm-d is a distributed serving framework designed for large language model inference. It functions as an inference orchestrator and gateway, providing a control plane for deploying model replicas and managing hardware accelerators. The system includes a batch inference scheduler and a cache manager to coordinate request flow and memory utilization. The project is distinguished by a disaggregated serving architecture that separates prefill and decode execution phases across specialized workers to maximize throughput. It employs a hardware-agnostic control plane and tiered cache offloading, mov
Provides a disaggregated prefill and decode topology specifically designed to maximize throughput for batch-intensive LLM workloads.
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