3 repositorios
Deploying specific subsets of application modules to targeted environments.
Distinct from Package Component Deployment: Focuses on selecting which components are deployed, rather than the filesystem mapping described in Package Component Deployment.
Explore 3 awesome GitHub repositories matching devops & infrastructure · Selective Component Deployment. Refine with filters or upvote what's useful.
Spin is a WebAssembly serverless framework and development toolchain for building and running portable microservices. It functions as an event-driven orchestrator and runtime that executes WebAssembly components, allowing developers to map HTTP requests, Redis messages, and cron schedules to specific modules. The project distinguishes itself by implementing a Wasm-based AI inference gateway, enabling components to perform model inference and generate text embeddings. It utilizes the WebAssembly Component Model and WASI for language-agnostic composition and portable host interfacing, while emp
Allows running a specific subset of components to distribute service layers across diverse environments.
Reth is a modular, high-performance Ethereum execution layer client written in Rust. It serves as a full Ethereum node that syncs, validates, and serves blockchain data, functioning as an archive node implementation, a high-throughput RPC node server, and a snapshot sync tool. The project is built around a modular component architecture that allows assembling custom node behavior by swapping independent Rust crates for consensus, execution, mempool, and networking. The client distinguishes itself through a staged sync pipeline that downloads headers and bodies online before processing the res
Chooses minimal, full, or archive profiles and custom component sets through a web UI or CLI flags.
KServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere
Selects either standard Kubernetes resources or Knative Serving to match production stability or scale-to-zero needs.