lablup/backend.ai
Backend.ai
This project is a distributed computing platform designed to orchestrate containerized workloads across heterogeneous hardware clusters. It functions as a centralized control plane that manages resource allocation, scheduling, and execution environments, enabling organizations to share high-performance computing infrastructure securely among multiple users and projects.
The platform distinguishes itself through advanced hardware virtualization and multi-tenant management capabilities. It supports the partitioning of physical graphics processing units into fractional slices, allowing multiple concurrent users to access dedicated hardware resources with strict isolation. Additionally, the system provides secure, encrypted remote access to these isolated containers and maintains full operational functionality within air-gapped environments to meet stringent data sovereignty requirements.
Beyond its core orchestration, the platform includes a plugin-based architecture that abstracts diverse AI accelerators and storage backends, ensuring consistent workflows across on-premises and cloud-based infrastructure. It features integrated tools for monitoring cluster health, enforcing resource quotas, and managing virtualized storage, providing a unified interface for scaling and optimizing complex computing tasks.
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.
