Hi! I'm working through the simple ML flyte demo. ...
# ask-the-community
p
Hi! I'm working through the simple ML flyte demo. I am trying to figure out how far you can go with the
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❯ 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?
Looks like my question is probably directly addressed by the docs... working through this now https://docs.flyte.org/projects/cookbook/en/latest/getting_started/creating_flyte_project.html#creating-a-flyte-project
k
you can also build a new image with custom dependencies, and use
pyflyte run --image <new_image_name> …
p
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?
k
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
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