Failed to train model

My data and feature set has been successful built, but having the following error:

 layer start
Layer 0.8.10 using https://beta.layer.co
πŸ“ Loading the project under /Users/codebrain/Documents/layer_fraud_detection
πŸ”Ž Found 1 dataset, 1 featureset and 1 model
πŸ“” Session logs at /Users/codebrain/.layer/logs/20211027T104426-session-1e038dec-2a
df-488c-b592-1bc67e3611df.log
πŸ’Ύ Starting at 2021-10-27 10:44:28...
πŸ”΅ Pipeline run id: a8c5003a-2e5f-48b2-b275-39f02b7cd8be
βœ… 2021-10-27 10:44:28 | dataset     fraud_detection_data           ━━ DONE      …
βœ… 2021-10-27 10:44:34 | featureset  fraud_detection_features       ━━ DONE      …
                                     https://beta.layer.co/features/4d600178-4b4a-
                                     4236-9f0e-80a0cd80b2a1
β Ή  2021-10-27 10:48:58 | model       fraud_detection_model          ━━ ERROR     …
                                     Exception('Process exit code: 134. Inferred
                                     interrupt signal SIGABRT(6): Aborted')
                                     Aborting...
LAYER RUN FAILED after 335404ms:
Failed to train model 'b1715a99-df8f-439d-9614-d7b71ab0a54c': Exception('Process
exit code: 134. Inferred interrupt signal SIGABRT(6): Aborted')

I am trying to build my model using XGBoost. I am keen on hearing from you.

The link of my project can be found here: GitHub - codebrain001/layer_fraud_detection

Thank you for sharing the issue.

Error on your CLI output points to a memory allocation issue.
Could you try to run the model with a f-medium Fabric type?
Fabric usage is explained here: Fabrics | Layer Documentation

Your model.yaml file will look like:

...
training:
  name: "fraud_detection_model_training"
  description: "Fraud Detection Model Training"
  fabric: "f-medium"
...

Hi Brain,

Thank you very much for raising this issue with us! Since we haven’t heard from you in a while, I am going to assume Alper’s suggestion worked for you.

Please don’t hesitate to open another thread if you are still facing issues.

Thanks,
Dimitar