Backend.AI
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.gatewayandai.backend.manager - Plugin interfaces
backendai_scheduler_v10backendai_hook_v10backendai_webapp_v10backendai_monitor_stats_v10backendai_monitor_error_v10
- Package namespace:
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.
- https://github.com/lablup/backend.ai-agent
- Package namespace:
ai.backend.agent - Plugin interfaces
backendai_accelerator_v12backendai_monitor_stats_v10backendai_monitor_error_v10
- Package namespace:
- https://github.com/lablup/backend.ai-accelerator-cuda (CUDA accelerator plugin)
- Package namespace:
ai.backend.acceelrator.cuda
- Package namespace:
- https://github.com/lablup/backend.ai-accelerator-rocm (ROCM accelerator plugin)
- Package namespace:
ai.backend.acceelrator.rocm
- Package namespace:
Server-side common plugins (for both manager and agents)
- 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
Kernels
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
- Package namespace:
- https://github.com/lablup/backend.ai-kernels
- Runtime-specific recipes to build the Docker images (Dockerfile)
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.
- Package namespaces:
ai.backend.common - https://github.com/lablup/backend.ai-common
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.
- 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
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.
- All container sessions have intrinsic SSH/SFTP/SCP support with auto-generated per-user SSH keypair.
- VSCode (coming soon)
- Most container sessions have intrinsic web-based VSCode support.
Integrations with IDEs and Editors
- 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
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.