Metadata-Version: 1.2
Name: backend.ai-client
Version: 1.0.6
Summary: Backend.AI API Client Library
Home-page: https://github.com/lablup/backend.ai-client-py
Author: Lablup Inc.
Author-email: joongi@lablup.com
License: LGPLv3
Description-Content-Type: UNKNOWN
Description: Backend.AI Client
        =================
        
        .. image:: https://badge.fury.io/py/backend.ai-client.svg
           :target: https://badge.fury.io/py/backend.ai-client
           :alt: PyPI version
        
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           :target: https://pypi.org/project/backend.ai-client/
           :alt: Python Versions
        
        .. image:: https://travis-ci.org/lablup/backend.ai-client-py.svg?branch=master
           :target: https://travis-ci.org/lablup/backend.ai-client-py
           :alt: Build Status (Linux)
        
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           :target: https://ci.appveyor.com/project/lablup/backend.ai-client-py/branch/master
           :alt: Build Status (Windows)
        
        .. image:: https://codecov.io/gh/lablup/backend.ai-client-py/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/lablup/backend.ai-client-py
           :alt: Code Coverage
        
        The official API client library for `Backend.AI <https://backend.ai>`_
        
        Usage
        -----
        
        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.
        
        .. code-block:: sh
        
           export BACKEND_ACCESS_KEY=...
           export BACKEND_SECRET_KEY=...
        
           # optional (for local clusters)
           export BACKEND_ENDPOINT="https://my-precious-cluster/"
        
        
        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
        ~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        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 python3 -c "print('hello world')"
           ∙ Client session token: d3694dda6e5a9f1e5c718e07bba291a9
           ✔ Kernel (ID: zuF1OzMIhFknyjUl7Apbvg) is ready.
           hello world
           ✔ Cleaned up the kernel.
        
        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 ``'\n'`` as
        actual newlines.)
        
        .. code-block:: console
        
           $ backend.ai run c -c $'#include <stdio.h>\nint main() {printf("hello world\\n");}'
           ∙ Client session token: abc06ee5e03fce60c51148c6d2dd6126
           ✔ Kernel (ID: d1YXvee-uAJTx4AKYyeksA) is ready.
           hello world
           ✔ Cleaned up the kernel.
        
        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 c 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!
           ✔ Cleaned up the kernel.
        
        Please refer the ``--help`` manual provided by the ``run`` command.
        
        You may use a shortcut command ``lcc`` and ``lpython`` instead of typing the full
        Python module path like:
        
        .. code-block:: console
        
           $ lcc main.c mylib.c mylib.h
        
        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.
        
        
        Synchronous API
        ---------------
        
        .. code-block:: python
        
           from ai.backend.client import Kernel
        
           kern = Kernel.get_or_create('lua5', client_token='abc')
           result = kern.execute('print("hello world")', mode='query')
           print(result['console'])
           kern.destroy()
        
        You need to take care of ``client_token`` because it determines whether to
        reuse kernel sessions or not.
        Sorna cloud has a timeout so that it terminates long-idle kernel sessions,
        but within the timeout, any kernel creation requests with the same ``client_token``
        let Sorna cloud to reuse the kernel.
        
        Asynchronous API
        ----------------
        
        .. code-block:: python
        
           import asyncio
           from ai.backend.client.asyncio import AsyncKernel
        
           async def main():
               kern = await AsyncKernel.get_or_create('lua5', client_token='abc')
               result = await kern.execute('print("hello world")', mode='query')
               print(result['console'])
               await kern.destroy()
        
           loop = asyncio.get_event_loop()
           try:
               loop.run_until_complete(main())
           finally:
               loop.close()
        
        All the methods of ``AsyncKernel`` objects are exactly same to the synchronous version,
        except that they are coroutines.
        
        Additionally, ``AsyncKernel`` offers async-only method ``stream_pty()``.
        It returns a ``StreamPty`` object which allows you to access a pseudo-tty of the kernel.
        ``StreamPty`` works like an async-generator and provides methods to send stdin inputs
        as well as resize the terminal.
        
        
        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>`_.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 or later (LGPLv3+)
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Environment :: No Input/Output (Daemon)
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Python: >=3.5
