Hi! I'm working through the simple ML flyte demo. I am trying to figure out how far you can go with the

❯ pyflyte run --remote example.py training_workflow --hyperparameters '{"C": 0.1}'
Go to <http://localhost:30080/console/projects/flytesnacks/domains/development/executions/fa1c65afdde414c7d961> to see execution in the console.

syntax when you have custom dependencies as specified in

requirements.txt

. I got excited that the run command was somehow passing

scikit-learn

along, but now I see that

scikit-learn

is simply preinstalled in the base image. I am curious if there a way to handle custom dependencies without dropping out of the simple

pyflyte run

command and without having to have a "kitchen sink" style base image?

you can also build a new image with custom dependencies, and use

pyflyte run --image <new_image_name> …

cool! I saw you can also include a

@task(..., container_image=myimage)

but I think that gets ignored when you use

pyflyte run

is that accurate? Those get honored when you use

pyflyte register

only?

by default, all the tasks will use the image you pass in pyflyte run. if you want to use different image for specific task, you could pass the image in task decorator