@<1523701435869433856:profile|SmugDolphin23> @<1523701087100473344:profile|SuccessfulKoala55> Yes, the second issue still consists, currently breaking our pipeline

in the meantime, we should have fixed this. I will ping you when 1.9.1 is out to try it out!

I tried to work on a reproducible script but then i get errors that my clearml task is already initialized (also doesn’t happen on 1.7.2)

@<1523701118159294464:profile|ExasperatedCrab78>
Ok. bummer to hear that it won't be included automatically in the package.

I am now experiencing a bug with the patch, not sure it's to blame... but i'm unable to save models in the pipeline.. checking if it's related

I'm getting really weird behavior now, the task seems to report correctly with the patch... but the step doesn't say "uploading" when finished... there is a "return" artifact but it doesn't exist on S3 (our file server configuration)

@<1523701949617147904:profile|PricklyRaven28> Please use this patch instead of the one previously shared. It excludes the dict hack :)

Hi @<1523701949617147904:profile|PricklyRaven28> sorry that this is happening. I tried to run your minimal example, but get a IndexError: Invalid key: 5872 is out of bounds for size 0 error. That said, I get the same error without the code running in a pipeline. There seems to be no difference between simply running the code and the pipeline (for me). Do you have an updated example, maybe also including getting a local copy of an artifact, so I can check?

The version is v1.9.1

Good to hear 🙏

It's been accepted in master, but was not released yet indeed!

As for the other issue, it seems like we won't be adding support for non-string dict keys anytime soon. I'm thinking of adding a specific example/tutorial on how to work with Huggingface + ClearML so people can do it themselves.

For now (using the patch) the only thing you need to be careful about is to not connect a dict or object with ints as keys. If you do need to (e.g. ususally huggingface models need the id2label dict somewhere) just make sure to cast it to string before connecting it to ClearML and casting it back to int directly after. So that when ClearML changes the value, it's properly taken care of 🙂 My previous sample code is still valid!

I appreciate it!

Hi @<1523701949617147904:profile|PricklyRaven28> ! We released ClearmlSDK 1.9.1 yesterday. Can you please try it?

of course

i believe this is because of transformer’s integration:

Automatic ClearML logging enabled.
ClearML Task has been initialized.

when a task already exists

I am currently on vacation, I'll ask my team mates. But if not I'll get to it next week

@<1523701118159294464:profile|ExasperatedCrab78> Sorry only saw this now,
Thanks for checking it!
Glad to see you found the issue, hope you find a way to fix the second one. for now we will continue using the previous version.
Would be glad if you can post when everything is fixed so we can advance our version.

Could you please run the misbehaving example, try to add a breakpoint in clearml/backend_interface/task/task.py in Task.update_output_model on the line with url = output_model.update_weights( , and tell me what the value of model_path is? In case you're using virtual environments, clearml library should be installed somewhere in <virtual env directory>/lib/python3.10/site-packages/clearml/

confirming that only downgrading to transformers==4.21.3 without the patch worked....
This is a time bomb that eventually we won't be able to ignore... we will need to use new transformers code

Yes, and the old version only works without the patch.
I see the model on the artifacts tab, but it's not actually uploaded.

Just for reference, the main issue is that ClearML does not allow non-string types as dict keys for its configuration. Usually the labeling mapping does have ints as keys. Which is why we need to cast them to strings first, then pass them to ClearML then cast them back.

Nothing that i think is relevant, I'm using latest from master. It might be a new bug on their side, wasn't sure.

that makes more sense 🙂
would this work now as a workaround until the version is released?

Saw it was merged 🙂 One down, one to go

Hi PricklyRaven28 , can you try with 1.9.1rc0?

Damn it, you're right 😅

        # Allow ClearML access to the training args and allow it to override the arguments for remote execution
        args_class = type(training_args)
        args, changed_keys = cast_keys_to_string(training_args.to_dict())
        Task.current_task().connect(args)
        training_args = args_class(**cast_keys_back(args, changed_keys)[0])

sounds good 🙂 I’ll soon check if this fixes our issue and update you

BTW the code above is from clearml github so it’s the latest

No worries! And thanks for putting in the time.

yeah, it gets to that error because the previous issue is saved…i’ll try to work on a new example

` from clearml.automation import PipelineDecorator
from clearml import TaskTypes

@PipelineDecorator.component(task_type=TaskTypes.data_processing, cache=True)
def run_demo():
from transformers import AutoTokenizer, DataCollatorForTokenClassification, AutoModelForTokenClassification, TrainingArguments, Trainer
from datasets import load_dataset

dataset = load_dataset("conllpp")

model_checkpoint = 'bert-base-cased'
lr = 2e-5
num_train_epochs  = 5
weight_decay = 0.01
seed = 1234

ner_feature = dataset["train"].features["ner_tags"]
label_names = ner_feature.feature.names
id2label = {str(i): label for i, label in enumerate(label_names)}
label2id = {v: k for k, v in id2label.items()}

tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)

data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)

model = AutoModelForTokenClassification.from_pretrained(
    model_checkpoint,
    id2label=id2label,
    label2id=label2id,
)
trainer_args = TrainingArguments(
    './tmp',
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=lr,
    num_train_epochs=num_train_epochs,
    weight_decay=weight_decay,
    seed=seed,
    data_seed=seed,
    load_best_model_at_end=True,
)

trainer = Trainer(
    model=model,
    args=trainer_args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
    data_collator=data_collator,
    tokenizer=tokenizer,
)
trainer.train()

@PipelineDecorator.pipeline(name="StuffToDelete", project=".Dev", version="0.0.2", pipeline_execution_queue="aws_cpu")
def pipeline():
run_demo()

if name == 'main':
PipelineDecorator.set_default_execution_queue("aws_cpu")

PipelineDecorator.run_locally()

pipeline() `

This isn’t a real working example, but it shows that on clearml 1.7.2 it passed initialization part (and has an error on training stuff which is ok)

And on 1.9.0 it errors before on
TypeError: unsupported operand type(s) for +=: 'NoneType' and 'str'