7 repository-uri
Tools that interact with cloud provider APIs to programmatically adjust the capacity of compute resources.
Distinct from Cloud Management APIs: The candidates focus on HTTP request execution or storage APIs, whereas this is specifically about infrastructure capacity scaling via APIs.
Explore 7 awesome GitHub repositories matching devops & infrastructure · Cloud Infrastructure Scaling. Refine with filters or upvote what's useful.
kops is a Kubernetes cluster provisioner and lifecycle manager designed to automate the creation, maintenance, and destruction of production-grade clusters on cloud infrastructure. It functions as a declarative infrastructure manager, synchronizing the live state of a cluster with versioned manifests stored in remote object storage to ensure idempotent operations. The project distinguishes itself by offering comprehensive automation for the entire cluster lifecycle, including high-availability control plane deployment, incremental rolling updates, and automated version upgrades. It also serve
Interacts with cloud APIs to programmatically adjust compute resource capacity and optimize container performance.
The Kubernetes Cluster Autoscaler is a mechanism that automatically adjusts the number of nodes in a cluster to match the resource demands of pending pods. It functions as a cloud infrastructure scaler that manages the desired capacity of scaling groups to ensure sufficient compute resources for workloads. The system manages cloud infrastructure automation by adjusting node counts when resources are insufficient or nodes are underutilized. It includes a manager for scaling groups using mixed instance policies to balance on-demand and spot instances for cost and availability. The project also
Provides programmatic scaling of cloud infrastructure by communicating with provider APIs based on cluster needs.
reconftw is an attack surface management framework and reconnaissance workflow orchestrator designed to automate the discovery, mapping, and monitoring of external digital assets. It operates as a modular tool-chain pipeline that coordinates a sequence of security tools to perform intelligence gathering and vulnerability scanning. The project distinguishes itself through a cloud-native deployment model that parallelizes scanning workloads across a fleet of remote VPS instances to bypass local resource constraints. It utilizes container-based environment isolation to ensure consistent executio
Scales reconnaissance capacity via cloud provider APIs to handle large target lists efficiently.
Acest proiect oferă roadmap-uri strategice și ghiduri care detaliază evoluția și tiparele de deployment ale serviciilor gestionate de orchestrare a containerelor și securitate. Servește drept document public de urmărire pentru funcționalitățile viitoare și prioritățile de dezvoltare pentru EKS, ECS, ECR și Fargate. Resursa include un ghid de orchestrare a containerelor în cloud și o strategie pentru Kubernetes și ECS, conturând dezvoltarea serviciilor gestionate de Kubernetes și a serviciilor de orchestrare proprietare pentru infrastructura cloud. De asemenea, oferă un plan de securitate și monitorizare axat pe scanarea activităților malițioase și urmărirea sănătății workload-urilor. Materialul acoperă o gamă largă de capabilități de infrastructură, inclusiv provizionarea resurselor, scalarea automată a resurselor de calcul și a sarcinilor, și gestionarea imaginilor de containere. Abordează rețelistica și gestionarea traficului prin load balancing și optimizarea densității pod-urilor, precum și observabilitatea prin rutarea log-urilor și urmărirea performanței.
Details tools and strategies for programmatically adjusting compute capacity and task counts to match resource demand.
This project provides a programmatic interface and framework for integrating large language models with secure, stateful, and multimodal code execution environments. It functions as a code interpreter API that enables the execution of arbitrary Python scripts within isolated sandboxed runtimes. The system supports multimodal data analysis by processing combined text and file inputs to generate visualizations and computational results. It manages stateful workflows by maintaining conversation memory and session history, allowing language models to complete multi-step technical tasks. The fram
Interacts with cloud APIs to programmatically scale the number of available execution environments for workloads.
This project is a collection of structured study notes and conceptual breakdowns designed for the AWS Certified Cloud Practitioner exam. It serves as a technical reference and study guide, organizing cloud service details and architectural principles to assist in certification preparation. The knowledge base is built using markdown files and includes curated cheat sheets and interactive mind-map visualizations. These tools map complex certification topics into visual hierarchies to enable drill-down study paths and rapid revision. The materials cover a wide range of cloud capabilities, inclu
Covers methods for adjusting compute capacity dynamically to match fluctuating business demands.
Polyaxon is a Kubernetes-native machine learning orchestration platform and MLOps pipeline orchestrator. It serves as a control plane for managing distributed deep learning workloads, automated machine learning pipelines, and experiment tracking. The platform distinguishes itself through specialized services for distributed training management, including MPI-based coordination for PyTorch and TensorFlow. It provides an automated hyperparameter optimization service utilizing Bayesian, random, and grid search algorithms, alongside managed interactive AI workspaces for launching Jupyter notebook
Deploys jobs and experiments across multiple cloud providers or on-premises hardware to manage concurrency.