Backend.AI Client
The official client SDK for Backend.AI
Usage (KeyPair mode)
You should set the access key and secret key as environment variables to use the API. Grab your keypair from cloud.backend.ai or your cluster admin.
On Linux/macOS, create a shell script as my-backend-ai.sh and run it before using the backend.ai command:
export BACKEND_ACCESS_KEY=...
export BACKEND_SECRET_KEY=...
export BACKEND_ENDPOINT=https://my-precious-cluster
export BACKEND_ENDPOINT_TYPE=apiOn Windows, create a batch file as my-backend-ai.bat and run it before using the backend.ai command:
chcp 65001
set PYTHONIOENCODING=UTF-8
set BACKEND_ACCESS_KEY=...
set BACKEND_SECRET_KEY=...
set BACKEND_ENDPOINT=https://my-precious-cluster
set BACKEND_ENDPOINT_TYPE=apiNote that you need to switch to the UTF-8 codepage for correct display of special characters used in the console logs.
Usage (Session mode)
Change BACKEND_ENDPOINT_TYPE to “session” and set the endpoint to the URL of your console server.
export BACKEND_ENDPOINT=https://my-precious-cluster
export BACKEND_ENDPOINT_TYPE=session$ backend.ai login
User ID: myid@mydomain.com
Password:
✔ Login succeeded!
$ backend.ai ... # run any command
$ backend.ai logout
✔ Logout done.The session expiration timeout is set by the console server.
Command-line Interface
backend.ai command is the entry point of all sub commands. (Alternatively you can use a verbosely long version: python -m ai.backend.client.cli)
Highlight: run command
The run command execute a code snippet or code source files on a Backend.AI compute session created on-the-fly.
To run the code specified in the command line directly, use -c option to pass the code string (like a shell).
$ backend.ai run python:3.6-ubuntu18.04 -c "print('hello world')"
∙ Client session token: d3694dda6e5a9f1e5c718e07bba291a9
✔ Kernel (ID: zuF1OzMIhFknyjUl7Apbvg) is ready.
hello worldBy default, you need to specify language with full version tag like python:3.6-ubuntu18.04. Depending on the Backend.AI admin’s language alias settings, this can be shortened just as python. If you want to know defined language aliases, contact the admin of Backend.AI server.
For more complicated programs, you may upload multiple files and then build & execute them. The below is a simple example to run a sample C program.
$ git clone https://gist.github.com/achimnol/df464c6a3fe05b21e9b06d5b80e986c5 c-example
Cloning into 'c-example'...
Unpacking objects: 100% (5/5), done.
$ cd c-example
$ backend.ai run gcc:gcc6.4-alpine3.8 main.c mylib.c mylib.h
∙ Client session token: 1c352a572bc751a81d1f812186093c47
✔ Kernel (ID: kJ6CgWR7Tz3_v2WsDHOwLQ) is ready.
✔ Uploading done.
✔ Build finished.
myvalue is 42
your name? LABLUP
hello, LABLUP!Please refer the --help manual provided by the run command.
Highlight: start and app command
backend.ai start is simliar to the run command in that it creates a new compute session, but it does not execute anything there. You can subsequently call backend.ai run -t <sessionId> ... to execute codes snippets or use backend.ai app command to start a local proxy to a container service such as Jupyter which runs inside the compute session.
$ backend.ai start -t mysess -r cpu=1 -r mem=2g lablup/python:3.6-ubuntu18.04
∙ Session ID mysess is created and ready.
∙ This session provides the following app services: ipython, jupyter, jupyterlab
$ backend.ai app mysess jupyter
∙ A local proxy to the application "jupyter" provided by the session "mysess" is available at: http://127.0.0.1:8080Highlight: ps and rm command
You can see the list of currently running sessions using your API keypair.
$ backend.ai ps
Session ID Lang/runtime Tag Created At Terminated At Status CPU Cores CPU Used (ms) Total Memory (MiB) Used Memory (MiB) GPU Cores
------------ ------------------------ ----- -------------------------------- --------------- -------- ----------- --------------- -------------------- ------------------- -----------
88ee10a027 lablup/python:3.6-ubuntu 2018-12-11T03:53:14.802206+00:00 RUNNING 1 16314 1024 39.2 0
fce7830826 lablup/python:3.6-ubuntu 2018-12-11T03:50:10.150740+00:00 RUNNING 1 15391 1024 39.2 0If you set -t option in the run command, it will be used as the session ID—you may use it to assign a human-readable, easy-to-type alias for your sessions. These session IDs can be reused after the current session using the same ID terminates.
To terminate a session, you can use terminate or rm command.
$ backend.ai rm 5baafb2136029228ca9d873e1f2b4f6a
✔ Done.Highlight: proxy command
To use API development tools such as GraphiQL for the admin API, run an insecure local API proxy. This will attach all the necessary authorization headers to your vanilla HTTP API requests.
$ backend.ai proxy
∙ Starting an insecure API proxy at http://localhost:8084More commands?
Please run backend.ai --help to see more commands.
Troubleshooting (FAQ)
There are error reports related to simplejson with Anaconda on Windows. This package no longer depends on simplejson since v1.0.5, so you may uninstall it safely since Python 3.5+ offers almost identical json module in the standard library.
If you really need to keep the simplejson package, uninstall the existing simplejson package manually and try reinstallation of it by downloading a pre-built binary wheel from here.
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Release files for backend.ai-client 26.4.15
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| backend_ai_client-26.4.15.tar.gz | 311.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| backend_ai_client-26.4.15-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 789.4 kB
Release files / backend_ai_client-26.4.15.tar.gz
| Download URL | backend_ai_client-26.4.15.tar.gz |
|---|---|
| Size | 311.1 kB |
| Tags | Source |
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| Download URL | backend_ai_client-26.4.15-py3-none-any.whl |
|---|---|
| Size | 478.3 kB |
| Tags | Python 3 |
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