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

I just disabled all of them with auto_connect_frameworks=False

Is there any restriction on what can be in preprocess.py?

Yes, this is exactly how I solved it at the end

This is very different than from in the xgboost example:

So the only thing left is models

(latest)

well, it just shoved the dataset files with cryptic data_1/data_2 names among the artifacts.

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 }

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 guess so, this was done by our DevOps guy and he said he is following instructions

If I do this it still autorecords the sklearn one

This receives the payload from the server and turns it into something that can be fed to the model. This in out case depends on a data structure that is stored on the clearml server as an artifact. I need to communicate this to the class so it can pick it up and use it when called

clearml-serving needs a "Preprocess" class in a file that is added to the endpoint.

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

task = Task.init(...) for foo in foos: data = foo.run() for key,value in data.items(): if key in 'auc', 'f1', etc: logger.log(key, value) elif key.startswith('model'): savemodel etc

Apparently our devops guy figured it out that you needed to have a different port number and a different docker container given 8080 was already occupied

docker-compose I guess

this is a bit WIP but we save it with the design of the model:
` parameters = dict(self.parameters, model_type='XGBClassifier')
...

output_model.update_design(config_dict=parameters) `

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 I figured this out but now I have a problem:

auto_connect_frameworks={ 'xgboost': False, 'scikitlearn': False }

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

if I swap it to Framework then it autocrecords both

apparently it did not work

TBH ClearML doesn't seem to be picking the model up so I need to do it manually

I didn't realise that pickling is what triggers clearml to pick it up. I am actually saving a dictionary that contains the model as a value (+ training datasets)

Why do I need an output_uri for the model saving? The dataset API can figure this out on its own

while in our own code:
if model_type == 'XGBClassifier': model = XGBClassifier() model.load_model(filename)

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)

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