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Lablup Backend.AI Meta-package

Project description


PyPI release version Supported Python versions Gitter

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.

Server-side Components

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.

Manager with API Gateway

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).

    • 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.

Server-side common plugins (for both manager and agents)


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


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


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


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

Client-side Components

Client SDK Libraries

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.


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.

Interacting with computation sessions

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.

Integrations with IDEs and Editors

Storage management

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.

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