Metadata-Version: 2.1
Name: backend.ai-agent
Version: 18.12.0a4
Summary: Backend.AI Agent
Home-page: https://backend.ai
Author: Lablup Inc.
Author-email: joongi@lablup.com
License: LGPLv3
Project-URL: Documentation, https://docs.backend.ai
Project-URL: Source, https://github.com/lablup/backend.ai-agent
Project-URL: Tracker, https://github.com/lablup/backend.ai-agent/issues
Description: # Backend.AI Agent
        
        The Backend.AI Agent is a small daemon that does:
        
        * Reports the status and available resource slots of a worker to the manager
        * Routes code execution requests to the designated kernel container
        * Manages the lifecycle of kernel containers (create/monitor/destroy them)
        
        ## Package Structure
        
        * `ai.backend`
          - `agent`: The agent daemon implementation
        
        
        ## Installation
        
        Please visit [the installation guides](https://github.com/lablup/backend.ai/wiki).
        
        ### For development
        
        #### Prerequisites
        
        * `libnsappy-dev` or `snappy-devel` system package depending on your distro
        * Python 3.6 or higher with [pyenv](https://github.com/pyenv/pyenv)
        and [pyenv-virtualenv](https://github.com/pyenv/pyenv-virtualenv) (optional but recommneded)
        * Docker 17.03 or later with docker-compose (18.03 or later is recommended)
        
        Clone [the meta repository](https://github.com/lablup/backend.ai) and install a "halfstack" configuration.
        The halfstack configuration installs and runs dependency daemons such as etcd in the background.
        
        ```console
        ~$ git clone https://github.com/lablup/backend.ai halfstack
        ~$ cd halfstack
        ~/halfstack$ docker-compose -f docker-compose.halfstack.yml up -d
        ```
        
        Then prepare the source clone of the agent as follows.
        First install the current working copy.
        
        ```console
        ~$ git clone https://github.com/lablup/backend.ai-agent agent
        ~$ cd agent
        ~/agent$ pyenv virtualenv venv-agent
        ~/agent$ pyenv local venv-agent
        ~/agent (venv-agent) $ pip install -U pip setuptools   # ensure latest versions
        ~/agent (venv-agent) $ pip install -U -r requirements-dev.txt
        ```
        
        With the halfstack, you can run the agent simply like
        (note that you need a working manager running with the halfstack already):
        
        ```console
        ~/agent (venv-agent) $ scripts/run-with-halfstack.sh python -m ai.backend.agent.server \
                             >                                      --scratch-root=/tmp --debug
        ```
        
        To run tests:
        
        ```console
        ~/agent (venv-agent) $ scripts/run-with-halfstack.sh python -m pytest -m 'not integration'
        ```
        
        To run tests including integration tests, you first need to install the manager in the same virtualenv.
        
        ```console
        ~$ git clone https://github.com/lablup/backend.ai-manager manager
        ~$ cd agent
        ~/agent (venv-agent) $ pip install -e ../manager
        ~/agent (venv-agent) $ scripts/run-with-halfstack.sh python -m pytest
        ```
        
        
        ## Deployment
        
        ### Running from a command line
        
        The minimal command to execute:
        
        ```sh
        python -m ai.backend.agent.server --etcd-addr localhost:2379 --namespace my-cluster
        ```
        
        The agent reads most configurations from the given etcd v3 server where
        the cluster administrator or the Backend.AI manager stores all the necessary
        settings.
        
        The etcd address and namespace must match with the manager to make the agent
        paired and activated.
        By specifying distinguished namespaces, you may share a single etcd cluster with multiple
        separate Backend.AI clusters.
        
        By default the agent uses `/var/cache/scratches` directory for making temporary
        home directories used by kernel containers (the `/home/work` volume mounted in
        containers).  Note that the directory must exist in prior and the agent-running
        user must have ownership of it.  You can change the location by
        `--scratch-root` option.
        
        For more arguments and options, run the command with ``--help`` option.
        
        ### Example config for agent server/instances
        
        `/etc/supervisor/conf.d/agent.conf`:
        
        ```dosini
        [program:backend.ai-agent]
        user = user
        stopsignal = TERM
        stopasgroup = true
        command = /home/user/run-agent.sh
        ```
        
        `/home/user/run-agent.sh`:
        
        ```sh
        #!/bin/sh
        source /home/user/venv/bin/activate
        exec python -m ai.backend.agent.server \
             --etcd-addr localhost:2379 \
             --namespace my-cluster
        ```
        
        ## Networking
        
        Basically all TCP ports must be transparently open to the manager.
        The manager and agent should run in the same local network or different
        networks reachable via VPNs.
        
        The operation of agent itself does not require both incoming/outgoing access to
        the public Internet, but if the user's computation programs need, the docker
        containers should be able to access the public Internet (maybe via some
        corporate firewalls).
        
        Several optional features such as automatic kernel image updates may require
        outgoing public Internet access from the agent as well.
        
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 :: 3.6
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: >=3.6,<3.7
Description-Content-Type: text/markdown
Provides-Extra: ci
Provides-Extra: test
Provides-Extra: monitor
Provides-Extra: build
Provides-Extra: dev
