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
andai.backend.manager
- Plugin interfaces
backendai_scheduler_v10
backendai_hook_v10
backendai_webapp_v10
backendai_monitor_stats_v10
backendai_monitor_error_v10
- Package namespace:
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
- 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-cuda-mock (CUDA mockup plugin)
- Package namespace:
ai.backend.acceelrator.cuda
- This emulates the presence of CUDA devices without actual CUDA devices, so that developers can work on CUDA integration without real GPUs.
- Package namespace:
- https://github.com/lablup/backend.ai-accelerator-rocm (ROCM accelerator plugin)
- Package namespace:
ai.backend.acceelrator.rocm
- Package namespace:
- 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
- Package namespace:
- 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.
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.
- 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.