Skip to main content

Backend.AI Agent

Project 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


Please visit the installation guides.

For development


  • libnsappy-dev or snappy-devel system package depending on your distro
  • Python 3.6 or higher with pyenv and pyenv-virtualenv (optional but recommneded)
  • Docker 17.03 or later with docker-compose (18.03 or later is recommended)

Clone the meta repository and install a "halfstack" configuration. The halfstack configuration installs and runs dependency daemons such as etcd in the background.

~$ git clone 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.

~$ git clone 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):

~/agent (venv-agent) $ scripts/ python -m ai.backend.agent.server \
                     >                                      --scratch-root=/tmp --debug

To run tests:

~/agent (venv-agent) $ scripts/ python -m pytest -m 'not integration'

To run tests including integration tests, you first need to install the manager in the same virtualenv.

~$ git clone manager
~$ cd agent
~/agent (venv-agent) $ pip install -e ../manager
~/agent (venv-agent) $ scripts/ python -m pytest


Running from a command line

The minimal command to execute:

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


user = user
stopsignal = TERM
stopasgroup = true
command = /home/user/


source /home/user/venv/bin/activate
exec python -m ai.backend.agent.server \
     --etcd-addr localhost:2379 \
     --namespace my-cluster


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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
backend.ai_agent-1.4.0-py3-none-any.whl (35.7 kB) Copy SHA256 hash SHA256 Wheel py3 (32.1 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page