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backend.ai-22.3.0
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Lablup Backend.AI Meta-package
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | backend.ai-22.3.0 |
| نام | backend.ai |
| نسخه کتابخانه | 22.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Lablup Inc. |
| ایمیل نویسنده | devops@lablup.com |
| آدرس صفحه اصلی | https://backend.ai |
| آدرس اینترنتی | https://pypi.org/project/backend.ai/ |
| مجوز | LGPLv3 |
Backend.AI
==========
[](https://pypi.org/project/backend.ai/)
[](https://gitter.im/lablup/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](http://docs.backend.ai).
### 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](https://github.com/lablup/backend.ai/blob/main/LICENSE).
نیازمندی
| مقدار | نام |
|---|---|
| <22.4.0,~=22.3.0 | backend.ai-client |
| <22.4.0,>=22.3.0 | backend.ai-agent |
| >=0.37.0 | wheel |
| >=3.5.0 | twine |
| >=60.0.0 | setuptools |
| ~=3.3 | sphinx |
| >=1.0.0 | sphinx-rtd-theme |
| ~=2.6 | pygments |
| >=4.0.1 | flake8 |
| <22.4.0,>=21.3.0 | backend.ai-manager |
| >=6.2.5 | pytest |
| >=0.930 | mypy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.9 | Python |
نحوه نصب
نصب پکیج whl backend.ai-22.3.0:
pip install backend.ai-22.3.0.whl
نصب پکیج tar.gz backend.ai-22.3.0:
pip install backend.ai-22.3.0.tar.gz