Feat support continue training

[guenthermi]

Add support for continue the training

This PR enables to continue the training form an existing artifact of a fine-tuned model

train_data = 'path/to/another/data.csv'

run2 = finetuner.fit(
    model='efficientnet_b0',
    train_data=train_data,
    model_artifact=run.artifact_id,
)
print(f'Run name: {run2.name}')
print(f'Run status: {run2.status()}')

Since we need the model name to detect the task in order to construct the training dataset correctly, users still need to add the model property to the fit function.

• This PR references an open issue
• I have added a line about this change to CHANGELOG

[guenthermi]

Tests are failing because I did not commit any requirements change for commons and stubs yet.

[guenthermi]

I added a pre-release to see if the tests are running. Everything seems to work as expected. One test is failing because loss-optimizers are not added yet. This should work when #664 is merged.

[bwanglzu]

LGTM! while why CI is failing?

[bwanglzu]

do we still need the model?

[bwanglzu]

okay now i see the hint, do i understand correly that the model type must be identical to artifact model type?

[guenthermi]

We need the model name to detect the task, so the task they are used for must be equal, so basically yes. If you use a docarray dataset, it probably does not matter

[LMMilliken]

Once the pr for the new pooling and loss options is merged, everything should pass.
LGTM!

[LMMilliken]

Suggested change

	:class: hint
	When you want to continue training, you still need to provide the `model` parameter
	beside the `model_artifact` parameter for Finetuner to correctly configure the new run.
	:class: hint
	When you want to continue training, you still need to provide the `model` parameter
	as well as the `model_artifact` parameter for Finetuner to correctly configure the new run.

[LMMilliken]

Just to make sure I understand, this means that if there is a MODEL_ARTIFACT, then name is set to None?

[guenthermi]

yes, because George implemented the jsonschema in a way that both arguments are mutually exclusive, which makes somehow sense, because otherwise it would be a little bit hard to see what will happen, e.g., if a new model is constructed or the artifact is used. So in this way, the final config to be send to the API is more clear on what a run should do.

[guenthermi]

LGTM! while why CI is failing?

as said in the comment above, the stubs version includes already the ArcFace changes and specifically, the loss-optimizers etc. The tests therefore failing but it will be addressed by Louis PR. So I need to merge main into this branch here after Louis finished its PR then it should not fail anymore

[github-actions]

📝 Docs are deployed on https://ft-feat-support-continue-training--jina-docs.netlify.app 🎉