backend.ai 22.3.0

Description:

backend.ai 22.3.0

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.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.

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-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.


https://github.com/lablup/backend.ai-accelerator-rocm (ROCM accelerator plugin)

Package namespace: ai.backend.acceelrator.rocm



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


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.

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

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

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

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.


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.