Good to hear 🙏

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])

Thanks! I'm checking now, but might take a little (meeting in between)

This is the next step not being able to find the output of the last step

ValueError: Could not retrieve a local copy of artifact return_object, failed downloading 

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

tnx! keep me posted

Saw it was merged 🙂 One down, one to go

Allright, a bit of searching later and I've found 2 things:

• You were right about the task! I've staged a fix here . It basically detects whether a task is already running (e.g. from the pipelinedecorator component) and if so, uses that task instead. We should probably do this for all of our integrations.
• But then I found another bug. Basically the pipeline decorator task would mess up the internal nested dict of the label mapping inside of the model config. You will probably have the same issue if you run the pipeline with my fix above.
  So for now, we're looking into the 2nd bug, because it breaks with Hugging Face models in a pipeline. Until we sort that out, I'm going to hold off on opening a PR to HF with the first fix. Makes sense?

Thanks a lot for the example, it helped tons to be able to reproduce!

It should, but please check first. This is some code I quickly made for myself. It did make tests for it, but it would be nice to hear from someone else that it worked (as evidenced by the error above 😅 )

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!

SmugDolphin23 SuccessfulKoala55 ^

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

Hi PricklyRaven28 , can you try with 1.9.1rc0?

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.

I appreciate it!

@<1523701118159294464:profile|ExasperatedCrab78>
Here is an example that reproduces the second error

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, AutoModelForSequenceClassification, TrainingArguments, Trainer
    from datasets import load_dataset
    import numpy as np
    import evaluate
    from pathlib import Path

    dataset = load_dataset("yelp_review_full")

    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")


    def tokenize_function(examples):
        return tokenizer(examples["text"], padding="max_length", truncation=True)
    
    
    def compute_metrics(eval_pred):
        logits, labels = eval_pred
        predictions = np.argmax(logits, axis=-1)
        return metric.compute(predictions=predictions, references=labels)

    
    small_train_dataset = dataset["train"].shuffle(seed=42).select(range(10))
    small_eval_dataset = dataset["test"].shuffle(seed=42).select(range(10))
    
    small_train_dataset = small_train_dataset.map(tokenize_function, batched=True)
    small_eval_dataset = small_eval_dataset.map(tokenize_function, batched=True)

    model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)

    training_args = TrainingArguments(
        output_dir="test_trainer", 
        evaluation_strategy="epoch",
        # num_train_epoch=1,
    )
    
    metric = evaluate.load("accuracy")
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=small_train_dataset,
        eval_dataset=small_eval_dataset,
        compute_metrics=compute_metrics,
    )
    
    trainer.train()
    
    return Path('test_trainer')

@PipelineDecorator.component(task_type=TaskTypes.data_processing, cache=True)
def second_step(some_param):
    print("Success!")
    
@PipelineDecorator.pipeline(name="StuffToDelete", project=".Dev", version="0.0.2", pipeline_execution_queue="aws_cpu")
def pipeline():
    data = run_demo()
    second_step(data)

if __name__ == '__main__':
    PipelineDecorator.set_default_execution_queue("aws_cpu")
    
    PipelineDecorator.run_locally()
    
    pipeline()

Hey @<1523701949617147904:profile|PricklyRaven28> I'm checking! Have you updated anything else and on which exact commit of transformers are you now?

@<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.

i believe this is because of transformer’s integration:

Automatic ClearML logging enabled.
ClearML Task has been initialized.

when a task already exists

of course

i believe this is because of this code
None

Which initialized the task if clearml is installed… but a task already exists (because of the pipeline), it will replace it

will check

No worries! And thanks for putting in the time.

The version is v1.9.1

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

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)

thank Lior

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

When creating it, I found that this hack should be on our side, not on Huggingface's. So I'm only going to fix issue 1 with the PR, issue 2 is ours 🙂

However, I actually do think I can already open the Huggingface PR in the meantime. It has actually relatively little to do with the second bug.