Project-MONAI / Project-MONAI/MONAI

Integrate AzureML functionality into Auto3dSeg

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Contribution wanted Feature request
Dominant language
Python
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Forks
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Avg merge
5d 1h
Merged PRs (30d)
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Description

Is your feature request related to a problem? Please describe.
No.

Describe the solution you'd like
We would like to enable the option to run Auto3dSeg training and inference in AzureML. This is as Auto3dSeg is a fantastic tool for clinical use-cases, but many of the users who could use this tool do not have the compute resources available to train and host the models.

Microsoft Research Cambridge have developed the hi-ml-azure package for running medical imaging related machine learning pipelines in AzureML. By integrating features of this package into Auto3dSeg we would be able to provide the option to run all training and inference in the cloud.

Additional context
Here the milestones we have identified, each of which is a feature that we hope to include in Auto3dSeg with no impact to the existing local user experience:

  • Running AutoRunner training in AzureML.
  • Running AutoRunner inference on ensemble model in AzureML.
  • Running inference on single Auto3dSeg algorithm in AzureML
  • Running AutoRunner modules independently in AzureML.

Furthermore, I have begun work on this in my own fork and am hoping to open a PR for the first milestone above before the end of next week.

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the AutoRunner training entry point and the hi-ml-azure package, then review the first-milestone work in the linked fork. The initial goal is to run AutoRunner training in AzureML while preserving the existing local user experience; later milestones cover inference and independent modules.

Written by the indexing model from the issue text.

Assessment

Tech stack
azure, python
Domain
cloud, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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