Awesome GitHub RepositoriesWorker Resource Allocation

WebDriverIO is a Node.js test automation framework used for automating functional tests across web browsers and mobile applications. It acts as a WebDriver protocol client that manages remote browser sessions and executes commands against WebDriver and Appium servers to perform end-to-end testing. The framework is distinguished by its ability to control both native and hybrid mobile applications and its support for running automated suites across local machines, remote grids, and cloud device providers. It includes specialized capabilities for coordinating multi-browser interactions and estab

ClearML is a comprehensive MLOps platform designed to manage the end-to-end machine learning lifecycle, from initial experimentation to production deployment. It provides a suite of integrated tools including a pipeline orchestrator for automating workflows, an experiment tracking tool for logging hyperparameters and metrics, and a metadata-driven data versioning system for managing large-scale datasets and model artifacts. The platform is distinguished by its advanced compute management and serving capabilities. It features a GPU compute manager that supports fractional resource slicing and

Feast is an open-source feature store for machine learning that provides a central platform for defining, storing, and serving features across both training and inference workflows. It operates as a declarative system where feature definitions are written as code in Python files, synchronized to a central registry, and made available for low-latency online retrieval or point-in-time correct historical joins for training datasets. The project abstracts storage behind a pluggable architecture, allowing offline and online backends to be swapped without changing retrieval logic, and coordinates ma

Volcano is a Kubernetes-native batch scheduler specialized for AI, machine learning, and high-performance computing workloads. It provides gang scheduling to atomically allocate resources for all tasks of a distributed job, preventing deadlocks from partial allocation, and supports hierarchical queue management for multi-tenant resource isolation with configurable quotas, borrowing, and preemption. Topology-aware placement optimizes communication-intensive workloads by modeling network hierarchy to minimize cross-switch latency. Volcano differentiates itself with automated orchestration of di

Parallel is a Ruby library and multi-process execution framework designed to accelerate CPU-intensive operations. It functions as a parallel job orchestrator and concurrent task runner that enables the execution of code across multiple processes or threads. The project distinguishes itself through secure inter-process communication, utilizing signed data serialization to prevent the injection of forged payloads between parent and child processes. It further differentiates its worker management by assigning unique identifiers to individual processes to prevent collisions when accessing shared

RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface