Backend.AI is a streamlined, container-based computing cluster orchestrator that hosts diverse programming languages and popular computing/ML frameworks, with pluggable heterogeneous accelerator support including CUDA and ROCM. It allocates and isolates the underlying computing resources for multi-tenant computation sessions on-demand or in batches with customizable job schedulers. All its functions are exposed as REST/GraphQL/WebSocket APIs.

If you want to run a Backend.AI cluster on your own, you need to install and configure the following server-side components. All server-side components are licensed under LGPLv3 to promote non-proprietary open innovation in the open-source community.

There is no obligation to open your service/system codes if you just run the server-side components as-is (e.g., just run as daemons or import the components without modification in your codes). Please contact us (contact-at-lablup-com) for commercial consulting and more licensing details/options about individual use-cases.

For details about server installation and configuration, please visit our documentation.

It routes external API requests from front-end services to individual agents. It also monitors and scales the cluster of multiple agents (a few tens to hundreds).

• https://github.com/lablup/backend.ai-manager
  • Package namespace: ai.backend.gateway and ai.backend.manager
  • Plugin interfaces
    • backendai_scheduler_v10
    • backendai_hook_v10
    • backendai_webapp_v10
    • backendai_monitor_stats_v10
    • backendai_monitor_error_v10

It manages individual server instances and launches/destroys Docker containers where REPL daemons (kernels) run. Each agent on a new EC2 instance self-registers itself to the instance registry via heartbeats.

• https://github.com/lablup/backend.ai-agent
  • Package namespace: ai.backend.agent
  • Plugin interfaces
    • backendai_accelerator_v12
    • backendai_monitor_stats_v10
    • backendai_monitor_error_v10
    • backendai_krunner_v10
• https://github.com/lablup/backend.ai-accelerator-cuda (CUDA accelerator plugin)
  • Package namespace: ai.backend.acceelrator.cuda
• https://github.com/lablup/backend.ai-accelerator-rocm (ROCM accelerator plugin)
  • Package namespace: ai.backend.acceelrator.rocm

• https://github.com/lablup/backend.ai-stats-monitor
  • Statistics collector based on the Datadog API
  • Package namespace: ai.backend.monitor.stats
• https://github.com/lablup/backend.ai-error-monitor
  • Exception collector based on the Sentry API
  • Package namespace: ai.backend.monitor.error

A set of small ZeroMQ-based REPL daemons in various programming languages and configurations.

• https://github.com/lablup/backend.ai-kernel-runner
  • Package namespace: ai.backend.kernel
  • A common interface for the agent to deal with various language runtimes
• https://github.com/lablup/backend.ai-kernels
  • Runtime-specific recipes to build the Docker images (Dockerfile)

A programmable sandbox implemented using ptrace-based sytem call filtering written in Go.

• https://github.com/lablup/backend.ai-jail

A set of libc overrides for resource control and web-based interactive stdin (paired with agents).

• https://github.com/lablup/backend.ai-hook

A collection of utility modules commonly shared throughout Backend.AI projects.

• Package namespaces: ai.backend.common
• https://github.com/lablup/backend.ai-common

We offer client SDKs in popular programming languages. These SDKs are freely available with MIT License to ease integration with both commercial and non-commercial software products and services.

• Python (provides the command-line interface)
  • pip install backend.ai-client
  • https://github.com/lablup/backend.ai-client-py
• Java
  • Currently only available via GitHub releases
  • https://github.com/lablup/backend.ai-client-java
• Javascript
  • npm install backend.ai-client
  • https://github.com/lablup/backend.ai-client-js
• PHP (under preparation)
  • composer require lablup/backend.ai-client
  • https://github.com/lablup/backend.ai-client-php

The front-end support libraries to handle multi-media outputs (e.g., SVG plots, animated vector graphics)

• The Python package (lablup) is installed inside kernel containers.
• To interpret and display media generated by the Python package, you need to load the Javascript part in the front-end.
• https://github.com/lablup/backend.ai-media

Backend.AI provides websocket tunneling into individual computation sessions (containers), so that users can use their browsers and client CLI to access in-container applications directly in a secure way.

• Jupyter Kernel: data scientists' favorite tool
  • Most container sessions have intrinsic Jupyter and JupyterLab support.
• Web-based terminal
  • All container sessions have intrinsic ttyd support.
• SSH
  • All container sessions have intrinsic SSH/SFTP/SCP support with auto-generated per-user SSH keypair. PyCharm and other IDEs can use on-demand sessions using SSH remote interpreters.
• VSCode (coming soon)
  • Most container sessions have intrinsic web-based VSCode support.

• Visual Studio Code Extension
  • Search “Live Code Runner” among VSCode extensions.
  • https://github.com/lablup/vscode-live-code-runner
• Atom Editor plugin
  • Search “Live Code Runner” among Atom plugins.
  • https://github.com/lablup/atom-live-code-runner

Backend.AI provides an abstraction layer on top of existing network-based storages (e.g., NFS/SMB), called vfolders (virtual folders). Each vfolder works like a cloud storage that can be mounted into any computation sessions and shared between users and user groups with differentiated privileges.

Refer to LICENSE file.