I would think having a unique slug is a good idea so the team can communicate purely be that single number. Maybe we will call tasks as slug_yyyymmdd

So the only thing left is models

and immediately complained about a package missing, which apparently I can't specify when I establish the model endpoint but I need to re compose the docker container by passing an env variable to it????

BTW you are not exporting Framework in __ init __ so you need to import it like from clearml.model import Framework

no

This was not something I was expecting to break.

If you enable nbdime globally and switch virtual environments, then git diff will fail.

When you spin the model you can tell it any additional packages you might need

What does spin mean in this context?

clearml-serving ... ?

ahh, because task_id is the "real" id of a task

(latest)

https://github.com/allegroai/clearml-serving/blob/main/examples/xgboost/preprocess.py

but here I can tell them: return a dictionary of what you want to save

OSS version

everything is weird about this. I put two models in the same endpoint, then only one was running, then I started another docker container having a different port number and then the curls with the new model endpoint (with the new port) started working

interesting if I run the script from the repo main directory with
python code/run.py
it still gives me the same error message

clearml.Repository Detection - WARNING - Can't get diff information for git repo in repo/code

I think this is because of the version of xgboost that serving installs. How can I control these?

use_current_task is False by default, let me see if this helps

No, pickling is the only thing that will Not trigger clearml (it is just too generic to automagically log)

So what is the mechanism that you "automagically" pick things up (for information, I don't think this is relevant to our usecase)

I was just looking at the model example. How does output model store the binary? For example of an xgboost model

wait

python='python3' ~/anaconda3/envs/.venv/bin/python3

Python 3.8.13

yeah

clearml==1.7.1

What I try to do is that DSes have some lightweight baseclass that is independent of clearml they use and a framework have all the clearml specific code. This will allow them to experiment outside of clearml and only switch to it when they are in an OK state. This will also help not to pollute clearml spaces with half backed ideas

also random tasks are popping up in the DevOps project in the UI

I am not sure I want to do that

docker-compose I guess

apparently it did not work

ahh, ok, well, I tried to find an example that I can extend but this was the only reference I found: https://github.com/allegroai/clearml/blob/ca384aa75c236e0a8af7c5dd85406a359c3eb703/clearml/model.py#L35

I know there is a aux cfg with key value pairs but how can use it in the python code?
"auxiliary_cfg": { "TASK_ID": "b5f339077b994a8ab97b8e0b4c5724e1", "V": 132 }