Forum Discussion
mohitfoundazure
Aug 10, 2023Copper Contributor
Build pipeline failure: Directory error
Hi I am facing an error while saving a trained model in the build pipeline. Here is my model training script (azure ml v1) to train the model Here is the error from the The path...
Kidd_Ip
Aug 24, 2025MVP
Consider this:
- Check if the Path Already Exists as a Directory
Before saving, add a check:
import os
file_path = '/mnt/azureml/.../iris_model.pkl'
# If a directory exists with the same name, remove it
if os.path.isdir(file_path):
os.rmdir(file_path)
# Now save the model
with open(file_path, 'wb') as file:
pickle.dump(model, file)
- Use a Dedicated Output Directory
Instead of saving directly into the working directory, create a subfolder:
output_dir = os.path.join(os.getcwd(), 'outputs')
os.makedirs(output_dir, exist_ok=True)
file_path = os.path.join(output_dir, 'iris_model.pkl')
with open(file_path, 'wb') as file:
pickle.dump(model, file)
This avoids naming collisions and aligns with Azure ML’s best practices.
- Avoid Hardcoding Paths
Use environment variables or Run.get_context() to dynamically get the correct output path:
from azureml.core.run import Run
run = Run.get_context()
output_dir = run.output_datasets['model_output'].path # if defined