7 个仓库
Interfaces for customizing worker and scheduler implementations to support specialized task queuing or parameter injection.
Distinct from Custom Parallel Task Execution: Distinct from custom parallel task execution: focuses on overriding core engine logic rather than workload decomposition.
Explore 7 awesome GitHub repositories matching development tools & productivity · Execution Logic Overrides. Refine with filters or upvote what's useful.
Luigi is a Python framework designed for building and managing complex batch data pipelines. It functions as a workflow orchestration engine that organizes tasks into directed acyclic graphs, ensuring that jobs execute in the correct logical order based on their dependencies. By utilizing a centralized scheduler, the system coordinates task execution across distributed environments, tracks global workflow state, and prevents redundant processing by verifying the existence of output targets before triggering any work. The project distinguishes itself through a robust state-tracking mechanism t
Supports overriding default worker and scheduler implementations for specialized requirements.
Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl
Allows implementation of user-defined sampling strategies and pruning rules to tailor the search process.
Dask 是一个并行计算框架和分布式任务调度器,旨在将 Python 数据科学工作流从单机扩展到大型集群。它作为一个集群资源管理器,通过将任务及其依赖项表示为有向无环图来编排计算逻辑。这种架构允许系统在管理复杂执行要求的同时,自动将工作负载分配到可用硬件上。 该项目通过一个延迟评估引擎脱颖而出,该引擎将数据操作推迟到明确请求时才执行,从而实现全局图优化和高效的资源分配。它结合了内存感知数据溢出功能,以防止在处理超过可用内存的数据集时系统崩溃,并利用任务图融合将操作序列组合成单个执行步骤,从而最大限度地减少调度开销和节点间通信。 该平台为大规模数据分析提供了全面的功能面,包括对分布式机器学习、高性能计算集成和并行数据处理的支持。它提供了用于集群生命周期管理、性能分析和任务执行实时监控的广泛工具。用户可以在各种基础设施上部署这些环境,包括本地硬件、云提供商、容器化系统和高性能计算集群。
Allows overriding default graph transformation logic with user-defined functions to tailor performance tuning for specific application requirements.
Hardhat is a smart contract development framework and EVM tooling suite designed for the full lifecycle of Ethereum and EVM-compatible applications. It serves as a development environment for compiling, testing, and deploying smart contracts, providing a local blockchain simulation and a programmable task runner. The framework is distinguished by its extensive simulation capabilities, including the ability to fork remote network state and manipulate block time or account balances. It features a hook-based plugin system that allows for the extension of core functionality and the creation of cu
Injects custom behavior into workflows by registering handlers that can modify arguments or block execution.
BAML is a prompt engineering framework and LLM client generator that defines AI prompts as type-safe functions. It serves as a structured data extraction tool and workflow orchestrator, transforming unstructured model responses into strongly typed objects using a custom schema language and alignment algorithms. The project distinguishes itself by using a compiler to generate language-specific boilerplate code for API communication and output parsing. It features a dedicated environment for designing complex prompt templates with conditional logic and reusable snippets, and employs genetic alg
Modifies the internal functions used to propose prompt improvements or analyze failure patterns during optimization.
express-graphql 是一个 GraphQL API 服务器实现和 HTTP 中间件,用于将 GraphQL 模式(schema)连接到 HTTP 服务器。它提供了一个请求解析器,用于从各种内容类型中提取查询和变量,并提供了一个上下文提供程序,将 HTTP 请求数据和会话状态注入到解析器(resolver)函数中。 该库包含一个基于浏览器的交互式 IDE,可检测 GET 请求并提供 HTML 界面,用于测试查询和检查响应。它还支持自定义执行管道,允许覆盖默认的解析、验证、执行和错误格式化函数。 该项目涵盖了通过中间件和请求参数解析进行的 API 集成,以及基于 AST 的查询验证和基于模式的执行架构。它还提供了扩展响应元数据和应用验证规则以限制特定字段或操作的机制。
Allows overriding default parsing, validation, execution, and error formatting functions with custom logic.
ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented
Swaps specific inputs or checkpoint outputs during a replay to isolate the impact of changes against a baseline execution.