I appreciate it!

Hey @<1523701949617147904:profile|PricklyRaven28> , So as discussed above there were 2 issues. The first one is still waiting on the second, it's on the backlog of our devs and should be done soon(tm).

That said, in the meantime I also wanted to do fun stuff with transformers, so I've written a quick hack that deals with the bug. It's bascially 2 functions that keep track of which types of keys are in the dict.

def cast_keys_to_string(d, changed_keys=dict()):
    nd = dict()
    for key in d.keys():
        if not isinstance(key, str):
            casted_key = str(key)
            changed_keys[casted_key] = key
        else:
            casted_key = key
        if isinstance(d[key], dict):
            nd[casted_key], changed_keys = cast_keys_to_string(d[key], changed_keys)
        else:
            nd[casted_key] = d[key]
    return nd, changed_keys

def cast_keys_back(d, changed_keys):
    nd = dict()
    for key in d.keys():
        if key in changed_keys:
            original_key = changed_keys[key]
        else:
            original_key = key
        if isinstance(d[key], dict):
            nd[original_key], changed_keys = cast_keys_back(d[key], changed_keys)
        else:
            nd[original_key] = d[key]
    return nd, changed_keys

You can then use them like this:

        training_args = TrainingArguments(
            output_dir="my_awesome_model",
            learning_rate=2e-5,
            per_device_train_batch_size=16,
            per_device_eval_batch_size=16,
            dataloader_num_workers=0,
            num_train_epochs=2,
            weight_decay=0.01,
            evaluation_strategy="epoch",
            save_strategy="epoch",
            load_best_model_at_end=True
        )

        # 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())
        training_args = args_class(**cast_keys_back(args, changed_keys)[0])

        self.trainer = Trainer(
            model=self.model,
            args=training_args,
            train_dataset=tokenized_dataset["train"],
            eval_dataset=tokenized_dataset["test"],
            tokenizer=self.tokenizer,
            data_collator=data_collator,
            compute_metrics=self.compute_metrics,
        )

        self.trainer.train()

This "hack" in combination with the patch to Huggingface from above should work 🙂 That said, it is a hack, so a production version of this should be there soon. I'll let you know when that happens!

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

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

@<1523701118159294464:profile|ExasperatedCrab78>
Hey again 🙂
I believe that the transformers patch wasn’t released yet right? we are getting into a problem where we need new features from transformers but can’t use because of this

S3 as it should be

@<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()

@<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.

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

I'm working with the patch, and installing transformers from github

for now we downgraded to 1.7.2, but of course prefer not to stay that way

No worries! And thanks for putting in the time.

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

` 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'

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

SmugDolphin23 SuccessfulKoala55 ^

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

tnx! keep me posted

Looks like the first issue has been solved 🙂

i think the second one still consists, still checking

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 🙂

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/

Hi PricklyRaven28 , can you try with 1.9.1rc0?

hi, yes we tried with the same result

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

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 

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 😅 )

We'll check it out 👍

Hi @<1523701949617147904:profile|PricklyRaven28> just letting you know I still have this on my TODO, I'll update you as soon as I have something!

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

SmugDolphin23 BTW, this is using clearml and huggingface’s automatic logging… didn’t log something manual