lablup/backend.ai
Backend.ai
Este proyecto es una plataforma de computación distribuida diseñada para orquestar cargas de trabajo en contenedores a través de clústeres de hardware heterogéneos. Funciona como un plano de control centralizado que gestiona la asignación de recursos, la programación y los entornos de ejecución, permitiendo a las organizaciones compartir infraestructura de computación de alto rendimiento de forma segura entre múltiples usuarios y proyectos.
La plataforma se distingue por sus capacidades avanzadas de virtualización de hardware y gestión multi-inquilino. Admite la partición de unidades de procesamiento gráfico físicas en segmentos fraccionarios, lo que permite a múltiples usuarios concurrentes acceder a recursos de hardware dedicados con un aislamiento estricto. Además, el sistema proporciona acceso remoto seguro y cifrado a estos contenedores aislados y mantiene una funcionalidad operativa completa dentro de entornos sin conexión a internet (air-gapped) para cumplir con estrictos requisitos de soberanía de datos.
Más allá de su orquestación central, la plataforma incluye una arquitectura basada en plugins que abstrae diversos aceleradores de IA y backends de almacenamiento, garantizando flujos de trabajo consistentes en infraestructuras locales y en la nube. Cuenta con herramientas integradas para monitorear el estado del clúster, aplicar cuotas de recursos y gestionar almacenamiento virtualizado, proporcionando una interfaz unificada para escalar y optimizar tareas informáticas complejas.
Features
- Workload Orchestration - Orchestrates containerized workloads across heterogeneous hardware clusters to enable efficient resource utilization and infrastructure scaling.
- High-Performance Computing - Orchestrates containerized workloads across heterogeneous hardware accelerators and multi-node infrastructure for high-performance computing.
- AI Workload Orchestration - Manages and scales containerized computing workloads across heterogeneous hardware clusters for complex machine learning tasks.
- Cluster Orchestrators - Coordinates distributed containerized workloads and resource allocation across heterogeneous hardware clusters.
- GPU Fractional Slicing - Partitions physical graphics processors into fractional slices to enable multi-tenant execution with dedicated resource allocation.
- Container Orchestration Environments - Manages isolated, resource-constrained execution environments across on-premises and cloud-based clusters.
- Multi-Tenant Orchestrators - Enforces resource quotas, security sandboxing, and access controls for distributed teams sharing high-performance clusters.
- Containerized Deployment Orchestration - Schedules and manages containerized environments across heterogeneous hardware clusters using centralized orchestration.
- GPU Resource Virtualization - Partitions physical graphics processing units into fractional slices to enable concurrent multi-tenant access with strict hardware isolation.
- OCI Workload Execution - Executes isolated, resource-constrained containerized code across heterogeneous hardware clusters to support diverse programming and machine learning tasks.
- Hardware Acceleration Abstractions - Decouples the control plane from specific AI accelerators and storage backends using modular plugin interfaces.
- Hardware Acceleration Support - Connects diverse AI accelerators through a plugin architecture to utilize specialized hardware for high-performance tasks.
- Fractional GPU Slicing - Partitions physical graphics hardware into secure, fractional slices for concurrent multi-tenant access.
- Network Attached Storage - Mounts remote network storage backends as local virtual folders for consistent data access across distributed nodes.
- Air-Gapped Execution - Maintains full operational functionality for containerized code within isolated, offline network environments.
- Hybrid Cloud Infrastructure - Coordinates workloads across on-premises data centers and public cloud providers through a single interface.
- Multi-Tenant Hardware Sharing - Distributes GPU resources among multiple users or tasks through a plugin architecture to maximize hardware efficiency in shared environments.
- Cluster Resource Managers - Allocates computing nodes, GPUs, and storage while enforcing policy-based resource limits and idle checks to optimize capacity.
- Sandbox Network Security Controls - Applies system-level sandboxing and resource controls to containerized environments to ensure secure and isolated execution of computing tasks.
- Compute Quota Management - Enforces resource quotas and access policies across departments to ensure fair distribution of shared computing capacity.
- Kernel-Based Sandboxing - Enforces strict security and resource limits for concurrent tasks within shared computing nodes using kernel-based controls.
- Secure Remote Access - Establishes encrypted tunnels into running containers to provide secure remote access via web-based terminals and development environments.
- Cloud Session Tunnels - Provides secure, encrypted remote access to isolated containers for terminal and development environment connectivity.
- Hardware Abstraction Layers - Provides a unified control plane interface for managing diverse AI accelerators and storage backends.
- Multi-tenant Isolation Policies - Enforces organizational resource quotas and automatic capacity redistribution to ensure fair access across user groups.
- Cluster Health Monitoring - Tracks resource usage, session status, and hardware performance metrics across diverse nodes from a unified dashboard.
