backend.ai-client 22.6.0.dev2

Backend.AI Client SDK

Backend.AI Client

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The official API client library for Backend.AI <https://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 <https://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:

.. code-block:: sh

export BACKEND_ACCESS_KEY=... export BACKEND_SECRET_KEY=... export BACKEND_ENDPOINT=https://my-precious-cluster export BACKEND_ENDPOINT_TYPE=api

On Windows, create a batch file as my-backend-ai.bat and run it before using the backend.ai command:

.. code-block:: bat

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=api

Note 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.

.. code-block:: sh

export BACKEND_ENDPOINT=https://my-precious-cluster export BACKEND_ENDPOINT_TYPE=session

.. code-block:: console

$ backend.ai login User ID: [e-mail] 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).

.. code-block:: console

   $ backend.ai run python:3.6-ubuntu18.04 -c "print('hello world')"
   ∙ Client session token: d3694dda6e5a9f1e5c718e07bba291a9
   ✔ Kernel (ID: zuF1OzMIhFknyjUl7Apbvg) is ready.
   hello world

By 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.

You can even run a C code on-the-fly. (Note that we put a dollar sign before
the single-quoted code argument so that the shell to interpret ``'
'`` as
actual newlines.)

.. code-block:: console

   $ backend.ai run gcc:gcc6.4-alpine3.8 -c $'#include <stdio.h>
int main() {printf("hello world\n");}'
   ∙ Client session token: abc06ee5e03fce60c51148c6d2dd6126
   ✔ Kernel (ID: d1YXvee-uAJTx4AKYyeksA) is ready.
   hello world

For larger programs, you may upload multiple files and then build & execute
them.  The below is a simple example to run `a sample C program
<https://gist.github.com/achimnol/df464c6a3fe05b21e9b06d5b80e986c5>`_.

.. code-block:: console

   $ 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.

.. code-block:: console

$ 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://127.0.0.1:8080

Highlight: ps and rm command

You can see the list of currently running sessions using your API keypair.

.. code-block:: console

   $ 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            0

If 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.

.. code-block:: console

   $ 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.

.. code-block:: console

   $ backend.ai proxy
   ∙ Starting an insecure API proxy at http://localhost:8084

More 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
  <https://www.lfd.uci.edu/%7Egohlke/pythonlibs/#simplejson>`_.