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mohitfoundazure's avatar
mohitfoundazure
Copper Contributor
Aug 10, 2023

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...
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Kidd_Ip's avatar
Kidd_Ip
MVP
Aug 24, 2025

Consider this:

 

  1. 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)

 

  1. 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.

  1. 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

 

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