stratum: 一种支持大规模基于代理的机器学习工作负载的系统基础设施
stratum: A System Infrastructure for Massive Agent-Centric ML Workloads
AI总结：
stratum是一种支持大规模基于代理的机器学习工作负载的系统基础设施，通过解耦流水线执行与规划推理，提升大规模代理流水线搜索效率。
摘要：
近年来，大型语言模型（LLMs）的进步改变了机器学习（ML）流水线的开发和评估方式。LLMs使一种新的工作负载类型——代理流水线搜索成为可能，其中自主或半自主的代理生成、验证和优化完整的ML流水线。这些代理主要在流行的Python ML库上操作，并表现出高度的探索性行为。这导致了成千上万次的数据剖析、流水线生成以及流水线阶段的迭代优化。然而，现有的基于Python的ML生态系统围绕Pandas和scikit-learn等库构建，这些库是为人类中心、交互式、顺序工作流设计的，并且受限于Python的解释执行模型、库级隔离以及对执行大量流水线的有限运行时支持。同时，许多高性能ML系统由系统社区提出，要么针对狭窄的工作负载类别，要么需要专门的编程模型，这限制了它们与Python ML生态系统整合，并使它们在LLM代理中变得不适用。这种日益加剧的不匹配暴露了在大规模支持代理流水线搜索方面的根本系统挑战。因此，我们提出了stratum，一种统一的系统基础设施，它在代理流水线搜索中将流水线执行与规划和推理解耦。Stratum无缝整合现有Python库，将流水线批量编译为优化的执行图，并在异构后端上高效执行，包括一种新的基于Rust的运行时。我们提出了stratum的架构愿景，以及一个早期原型，讨论了关键设计决策，并概述了开放挑战和研究方向。最后，初步实验表明，stratum可以显著加速大规模代理流水线搜索，最高可达16.6倍。
英文摘要：
Recent advances in large language models (LLMs) transform how machine learning (ML) pipelines are developed and evaluated. LLMs enable a new type of workload, agentic pipeline search, in which autonomous or semi-autonomous agents generate, validate, and optimize complete ML pipelines. These agents predominantly operate over popular Python ML libraries and exhibit highly exploratory behavior. This results in thousands of executions for data profiling, pipeline generation, and iterative refinement of pipeline stages. However, the existing Python-based ML ecosystem is built around libraries such as Pandas and scikit-learn, which are designed for human-centric, interactive, sequential workflows and remain constrained by Python's interpretive execution model, library-level isolation, and limited runtime support for executing large numbers of pipelines. Meanwhile, many high-performance ML systems proposed by the systems community either target narrow workload classes or require specialized programming models, which limits their integration with the Python ML ecosystem and makes them largely ill-suited for LLM-based agents. This growing mismatch exposes a fundamental systems challenge in supporting agentic pipeline search at scale. We therefore propose stratum, a unified system infrastructure that decouples pipeline execution from planning and reasoning during agentic pipeline search. Stratum integrates seamlessly with existing Python libraries, compiles batches of pipelines into optimized execution graphs, and efficiently executes them across heterogeneous backends, including a novel Rust-based runtime. We present stratum's architectural vision along with an early prototype, discuss key design decisions, and outline open challenges and research directions. Finally, preliminary experiments show that stratum can significantly speed up large-scale agentic pipeline search up to 16.6x.
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