Azure Machine Learning
An Azure machine learning service for building and deploying models.
3,178 questions
Azure ML studio designer python executor is outdated
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Wasim Khan
20
Reputation points
Hey Team,
Explored a lot on Azure ML studio service and found the designer is helpful. Still an issue when I am using the Execute Python Script in the designer it is import all libraries older version and when checked its python version it was also outdated.
How can we upgrade to the latest still not able to figure it out.
{count} votes
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Wasim Khan 20 Reputation points
2025-03-04T10:59:04.1966667+00:00 I am using azure ml studio designer(classic) > Create a new pipeline using classic prebuilt components > components > Execute Python Script
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kothapally Snigdha 1,715 Reputation points Microsoft External Staff2025-03-05T22:34:55.29+00:00 Hi Wasim Khan
- In Azure Machine Learning, there are two types of environments: curated and custom. Curated environments come with popular ML frameworks and tools and are available by default in your workspace. They are regularly updated by Azure for the latest releases and security patches. Custom environments, on the other hand, are user-defined and can be created using the
az ml environment createcommand. - Curated environments are supported by cached Docker images, which help lower job preparation costs and speed up model deployment. You can use these environments directly for training or deployment by referencing them with the
azure ml:<curated-environment-name>:<version>orazure ml:<curated-environment-name>@latestsyntax. Additionally, you can use these curated environments as a base for your custom environments by modifying their Docker files. - You can see the set of available curated environments in the Azure Machine Learning studio UI, or by using the CLI (v2) via az ml environment list. https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-serverless-compute?view=azureml-api-2&tabs=python And this: https://learn.microsoft.com/en-us/python/api/overview/azure/ai-ml-readme?view=azure-python
- We are excited to introduce the GA of Azure Machine Learning Python SDK v2. The Python SDK v2 introduces new SDK capabilities like standalone local jobs, reusable components for pipelines and managed online/batch inferencing. Python SDK v2 allows you to move from simple to complex tasks easily and incrementally. This is enabled by using a common object model which brings concept reuse and consistency of actions across various tasks. The SDK v2 shares its foundation with the CLI v2 which is also GA. Source code | Package (PyPI) | Package (Conda) | API reference documentation | Product documentation | Samples This package has been tested with Python 3.7, 3.8, 3.9 and 3.10. For a more complete set of Azure libraries, see https://aka.ms/azsdk/python/all
I hope these helps you. Thank you!
- In Azure Machine Learning, there are two types of environments: curated and custom. Curated environments come with popular ML frameworks and tools and are available by default in your workspace. They are regularly updated by Azure for the latest releases and security patches. Custom environments, on the other hand, are user-defined and can be created using the
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kothapally Snigdha 1,715 Reputation points Microsoft External Staff
2025-03-08T01:13:07.55+00:00 Hi Wasim Khan
Following up to see if the response was helpful.
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Wasim Khan 20 Reputation points
2025-03-10T05:46:08.3666667+00:00 The question here is how can I run my custom python script for model training and reschedule it on weekly basis and the python script component in the Designer is outdated what are the alternate options available. I created a custom script job but that cannot be scheduled.
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kothapally Snigdha 1,715 Reputation points Microsoft External Staff
2025-03-11T18:31:25.2533333+00:00 Hi Wasim Khan
To run your custom Python script for model training and schedule it on a weekly basis in Azure Machine Learning, consider the following steps.
- Instead of relying on the Execute Python Script component in the designer, you can create a pipeline that includes your custom Python script. This allows for more flexibility and the ability to incorporate various components.
- Once you've created your pipeline, you can schedule it to run at regular intervals, such as weekly. This can be done using the Azure Machine Learning CLI, SDK, or the studio UI. You can create, retrieve, update, and deactivate schedules for your pipeline jobs.
- If you prefer using custom script jobs, you can still schedule them by wrapping them in a pipeline. This way, you can leverage the scheduling capabilities of Azure Machine Learning while executing your custom scripts.
I hope these helps you. Thank you!
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Wasim Khan 20 Reputation points
2025-03-12T14:11:19.14+00:00 Understood kothapally,
I have a Python script that works when using a custom script option in a job, but I am unable to find the option to schedule it.
Could you provide me with the steps of the process you mentioned above?
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