lablup/backend.ai

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.

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