Can an Azure ML pipeline be triggered from my local system i.e. not using an Azure ML VM?

Question

Due to several factors beyond, my org does not allow git integration with azure ML compute/VMs. So I was wondering if it is possible to develop on my local system and trigger pipelines from there?

I have experimented with this but it turns out I don't have right credentials?

# using python sdk v2
pipeline_job = ml_client.jobs.create_or_update(
    pipeline_job, experiment_name="LocalTest"
)

error log:

Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
ClientAuthenticationError                 Traceback (most recent call last)
Cell In[22], line 1
----> 1 pipeline_job = ml_client.jobs.create_or_update(
      2     pipeline_job, experiment_name="LocalTest"
      3 )

File c:\ProgramData\Anaconda3\envs\azureml\lib\site-packages\azure\core\tracing\decorator.py:78, in distributed_trace.<locals>.decorator.<locals>.wrapper_use_tracer(*args, **kwargs)
     76 span_impl_type = settings.tracing_implementation()
     77 if span_impl_type is None:
---> 78     return func(*args, **kwargs)
     80 # Merge span is parameter is set, but only if no explicit parent are passed
     81 if merge_span and not passed_in_parent:

File c:\ProgramData\Anaconda3\envs\azureml\lib\site-packages\azure\ai\ml\_telemetry\activity.py:350, in monitor_with_telemetry_mixin.<locals>.monitor.<locals>.wrapper(*args, **kwargs)
    348 dimensions = {**parameter_dimensions, **(custom_dimensions or {})}
    349 with log_activity(logger, activity_name or f.__name__, activity_type, dimensions) as activityLogger:
--> 350     return_value = f(*args, **kwargs)
    351     if not parameter_dimensions:
    352         # collect from return if no dimensions from parameter
    353         activityLogger.activity_info.update(_collect_from_return_value(return_value))

File c:\ProgramData\Anaconda3\envs\azureml\lib\site-packages\azure\ai\ml\operations\_job_operations.py:645, in JobOperations.create_or_update(self, job, description, compute, tags, experiment_name, skip_validation, **kwargs)
    642         self._validate(job, raise_on_failure=True)
    644     # Create all dependent resources
...
	SharedTokenCacheCredential: SharedTokenCacheCredential authentication unavailable. No accounts were found in the cache.
	AzureCliCredential: Azure CLI not found on path
	AzurePowerShellCredential: Failed to invoke PowerShell
	AzureDeveloperCliCredential: Azure Developer CLI could not be found. Please visit https://aka.ms/azure-dev for installation instructions and then,once installed, authenticate to your Azure account using 'azd auth login'.
To mitigate this issue, please refer to the troubleshooting guidelines here at https://aka.ms/azsdk/python/identity/defaultazurecredential/troubleshoot.

TIA

Answer 1

Hi @Das Gupta, Abhijeet ,

Thank you for reaching out to Microsoft Q&A forum!

Azure ML pipeline cannot be triggered from local system. It is possible only for Automated Machine learning and not for Azure ML pipelines and designer.

Please look into the supported compute targets.

Hope this helps. Do let us know if you any further queries.

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