2 dépôts
Awesome GitHub RepositoriesDistributed Layer Synchronizers
Systems for coordinating data movement and state consistency between parallelized model layers.
Distinct from Distributed Acceleration Layers: Distinct from Distributed Acceleration Layers: focuses on the synchronization of specific layer data during distributed execution rather than general hardware abstraction.
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Awesome Distributed Layer Synchronizers GitHub Repositories
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- zhaochenyang20/awesome-ml-sys-tutorial
zhaochenyang20/Awesome-ML-SYS-Tutorial
5,371Voir sur GitHubThis project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr
Coordinates data movement between independent attention workers and shared MLP layers to maintain state consistency across parallel processing units.
Python - b4rtaz/distributed-llama
b4rtaz/distributed-llama
2,837Voir sur GitHubDistributed-llama is a distributed inference engine and command line tool for running large language models across multiple networked machines. It functions as a compute cluster manager that coordinates worker nodes to share the computational load of a single model. The system utilizes tensor parallelism to shard model weights across different hosts, allowing the execution of models that exceed the memory capacity of a single piece of hardware. It includes a dedicated format converter to transform standard model files into a compatible binary layout optimized for distributed loading. The eng
Coordinates the forward pass to ensure each model layer finishes processing across all nodes before the next begins.
C++distributed-computingdistributed-llmllama2
