Which is defined, it seems, here: https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/trainer_tf.py#L459

OK, neat! Any advice on how to edit the training loop to do that? Because the code I'm using doesn't offer easy access to the training loop, see here: https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/examples/pytorch/language-modeling/run_mlm.py#L469

trainer.train() just does the training loop automagically, and saves a checkpoint once in a while. When it saves a checkpoint, clearML uploads all the other files. How can I hook into... whatever triggers that, and upload this file also?

What I'm curious about is how clearML hooks into that to know to upload the other artifacts such as http://optimizer.pt .

My other question is: how does it decide what to upload automatically? It picked up almost everything, just not trainer_state.json. Which I'm actually not quite sure is necessary

I'll give it a shot!

Yeah, we don't even get to line 480, all the training loop is within line 469, I think.

https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/trainer_pt_utils.py#L954
specifically called here:
https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/examples/pytorch/language-modeling/run_mlm.py#L480
Maybe after this line add:
Task.current_task().upload_artifact('trainer_state.json, name='state') `wdyt?

I guess I could try and edit that, somehow. Hmm

lol TIL: https://stackoverflow.com/questions/5626193/what-is-monkey-patching

Could I use "register artifact"

I think this is somewhat deprecated and we should probably replace it with something similar to what you mentioned (i.e. watch a file change).
Right now the easiest way would e to manually upload the trainer_state.json every checkpoint:
Task.current_task().upload_artifact('trainer_state.json, name='state') `

training loop is within line 469, I think.

I think the model state is just post training loop (not inside the loop), no?

Basically it hooks into any torch.save function (monkey patching in realtime)

oooh, that's awesome lol. Never thought to do it that way

I think the model state is just post training loop (not inside the loop), no?

trainer_state.json gets updated every time a "checkpoint" gets saved. I've got that set to once an epoch.

My testing indicates that if the training gets interrupted, I can resume training from a saved checkpoint folder that includes trainer_state.json. It uses the info to determine which data to skip, where to pick back up again, etc

Hmm pseudo stack:
https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/trainer_tf.py#L779

https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/feature_extraction_utils.py#L285

https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/feature_extraction_utils.py#L470

Oh, here's an example, a screenshot I took of the files in my Colab instance:

I'm not sure I follow. Can you elaborate what you mean? Pseudo stack?

If you cannot change the "TrainerState" (i.e. inherit and pass it into the code)
you cloud also monkey-patch it, something like
` class OurTrainerState(TrainerState):
def init(...)
...
def load_from_json(cls, json_path: str):
super().load_from_json(json_path))
Task.current_task().upload_artifact(...)

trainer.state = OurTrainerState(trainer.state) `

OK, I added

Task.current_task().upload_artifact(name='trainer_state', artifact_object=os.path.join(output_dir, "trainer_state.json"))

after this line:

https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/trainer.py#L1531

And it seems to be working.

Presumably the correct way to do this is to fork the transformers library, make the change, and add that version to my requirements.txt

yep 🙂

Hi SmallDeer34
The any generally any pytorch.save(...) is logged/uploaded by clearml automatically. specifically in your case I think the only missing one is the trainer_sate.json, which I assume is general json file, and I imagine is part of huggingface framework. You can easily upload it as additional artifact with Task.upload_artifact wdyt?

Could I use "register artifact" to get it to update every time there's a new checkpoint created?

Oh, that's a neat tip! I just set that in the Task settings? I didn't know that was possible

Sorry, you are correct this is where the json is created:
https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/feature_extraction_utils.py#L470

other links are the function calling it. make sense ?

So in theory we could hook into one of those functions and add a line to have ClearML upload that particular json we want

Oh, interesting

Alas, no luck. Uploaded the same things, did not upload trainer_state.json