Is it possible to upgrade Azure ML studio Serverless Compute to Python 3.9?

Question

Hello, Need to use python 3.9 in ML Studio using Serverless compute for a project, is this possible or does the serverless compute offered comes with a set instance of python that can't be changed? In some instances, you can have multiple versions of python running the same time with only one being the active one which can be selected on the fly. Thanks for any answers you may provide.

Answer 1

Hello @alvaro mantilla !  Welcome to Microsoft QnA!

based on the Docs : There are two types of environments in Azure Machine Learning: curated and custom environments. Curated environments are predefined environments containing popular ML frameworks and tooling. Custom environments are user-defined and can be created via az ml environment create. Curated environments are provided by Azure Machine Learning and are available in your workspace by default. Azure Machine Learning routinely updates these environments with the latest framework version releases and maintains them for bug fixes and security patches.

They're backed by cached Docker images, which reduce job preparation cost and model deployment time. You can use these curated environments out of the box for training or deployment by referencing a specific environment using the azureml:<curated-environment-name>:<version> or azureml:<curated-environment-name>@latest syntax. You can also use them as reference for your own custom environments by modifying the Dockerfiles that back these curated environments.

 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

So yes , create a Custom Environment :

https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-customize-environment-runtime?view=azureml-api-2

I hope this helps!

Kindly mark the answer as Accepted and Upvote in case it helped! Regards