` 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'