Skip to main content

Lablup Backend.AI Meta-package

Project description

Backend.AI

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).

  • 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

Agent

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)

Kernels

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

Jail

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

Hook

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

Commons

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.

Media

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

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.

License

Refer to LICENSE file.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

backend.ai-22.3.0.tar.gz (7.9 kB view details)

Uploaded Source

Built Distribution

backend.ai-22.3.0-py3-none-any.whl (7.4 kB view details)

Uploaded Python 3

File details

Details for the file backend.ai-22.3.0.tar.gz.

File metadata

  • Download URL: backend.ai-22.3.0.tar.gz
  • Upload date:
  • Size: 7.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for backend.ai-22.3.0.tar.gz
Algorithm Hash digest
SHA256 c5aab5a6a3acec7017be922e126992ec24f4f75040f539e06583725b1f5a5692
MD5 23e29caf44a47140c5daa09126bcbe4a
BLAKE2b-256 5cf45fbde54fe469c9cabadeb299685fe849a4dd42a98e8e04f3d5d1a557ef95

See more details on using hashes here.

File details

Details for the file backend.ai-22.3.0-py3-none-any.whl.

File metadata

  • Download URL: backend.ai-22.3.0-py3-none-any.whl
  • Upload date:
  • Size: 7.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.9.12

File hashes

Hashes for backend.ai-22.3.0-py3-none-any.whl
Algorithm Hash digest
SHA256 ca2dc0f4c935afff78834b5c5636d62a9e9860c6faec23561a2b9a08229ce9a8
MD5 4eaa9ce6d4721a2aeda93bf54457d6cd
BLAKE2b-256 846d6119e4825985dc589d377e2e4400c333c4c63ff53f76d71affa128f75f71

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page