# Worker Management¶

On-premises Valohai Enterprise installation allows managing your local Valohai workers.

A single physical or virtual machine can have any number of workers but we recommend allocating at least one GPU per worker if you are utilizing graphic cards in your machine learning workloads.

Tip

Depending on the setup, you might be required to invoke the following commands using sudo <COMMAND>. You can also call sudo su to switch to administrator user for the duration of the current terminal session.

At its core, Valohai worker is a systemd-based service called peon.

service "peon@*" status    # see status of all local workers
service "peon@*" stop      # stop all workers e.g. if you want to do an update
service "peon@*" start     # start all workers
service "peon@*" restart   # restart all workers


You can also control individual workers. Worker numbering starts from 0.

service "peon@0" status
service "peon@1" stop
service "peon@2" start


These worker will take new executions from your work queue as they appear. You can queue as much work as you wish and the workers will process them as they become free.

Valohai worker runs encapsulated workloads in Docker containers, here are helpful commands that you can use to managed those:

Note

Valohai also includes a cleanup service called peon-clean so running the following Docker maintenance commands is not necessary except in special cases.

docker ps         # see all containers; note that all might not be from Valohai
docker stop <id>  # kill a running container
docker rm <id>    # remove container history information to free up space
docker rmi <name> # remove a cached image to free up space