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2 repository-uri

Awesome GitHub RepositoriesPipeline Execution Optimizations

Strategies to reduce latency in processing pipelines through fail-fast mechanisms and asynchronous execution.

Distinct from Performance and Optimization: Candidates are too specific to DBs, SEO, or audio; this is a general pipeline performance concern.

Explore 2 awesome GitHub repositories matching software engineering & architecture · Pipeline Execution Optimizations. Refine with filters or upvote what's useful.

Awesome Pipeline Execution Optimizations GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • datajuicer/data-juicerAvatar datajuicer

    datajuicer/data-juicer

    6,574Vezi pe GitHub↗

    Data-Juicer is an open-source framework for cleaning, filtering, deduplicating, and transforming multimodal datasets to prepare them for training large language and vision models. It functions as a distributed data pipeline engine that runs processing jobs across Ray clusters, handling billions of samples with automatic operator fusion and adaptive parallelism. The framework provides a library of operators that leverage large language models for semantic extraction, filtering, and data synthesis within processing pipelines. The project distinguishes itself through a YAML-based data recipe sys

    Optimizes pipeline execution by fusing operators and adapting parallelism to achieve speedups of 2-10x.

    Pythondatadata-analysisdata-pipeline
    Vezi pe GitHub↗6,574
  • protectai/llm-guardAvatar protectai

    protectai/llm-guard

    2,561Vezi pe GitHub↗

    LLM Guard is a security firewall and guardrail framework designed to scan and sanitize inputs and outputs for large language models. It functions as a proxy gateway and security layer to block prompt injections, toxicity, and sensitive data leakage while ensuring that model interactions remain compliant with organizational policies. The system distinguishes itself through a modular scanner pipeline that utilizes local model orchestration to eliminate external network dependencies. It supports real-time security filtering via streaming chunk analysis and implements a fail-fast execution model

    Reduces latency and resource load via fail-fast exits, asynchronous processing, and request sampling.

    Pythonadversarial-machine-learningchatgptlarge-language-models
    Vezi pe GitHub↗2,561
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