Vertex AI Experiment Tracker Integration

[nkhusainov]

Describe changes

This PR contains implementation of Vertex AI Experiment Tracker

TO DO

• Add docs
• Add examples

Pre-requisites

Please ensure you have done the following:

• I have read the CONTRIBUTING.md document.
• I have added tests to cover my changes.
• I have based my new branch on develop and the open PR is targeting develop. If your branch wasn't based on develop read Contribution guide on rebasing branch to develop.
• IMPORTANT: I made sure that my changes are reflected properly in the following resources:
  • ZenML Docs
  • Dashboard: Needs to be communicated to the frontend team.
  • Templates: Might need adjustments (that are not reflected in the template tests) in case of non-breaking changes and deprecations.
  • Projects: Depending on the version dependencies, different projects might get affected.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)
• Other (add details above)

[CLAassistant]

All committers have signed the CLA.

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[schustmi]

This looks great already @nkhusainov!

[hyperlint-ai]

The style guide flagged several spelling errors that seemed like false positives. We skipped posting inline suggestions for the following words:

• MLOps

---

Note: We resolved prior Hyperlint review comments because:

We updated our inline suggestion AI.

We do this to avoid keeping outdated or irrelevant comments around. We'll leave a new review with current comments below.

[htahir1]

Reviewed the docs only. Looks so great!

[htahir1]

This is great - but just as a small wish I'd love to see a screenshot of how it looks like on Vertex just to show it to users.

Also there is nothing about Tensorboard, and I know that that is a parameter in the implementation. Should that maybe stand out a bit as a special case?

[nkhusainov]

Thanks for the feedback, good suggestions! I'll add Tensorboard examples and several screenshots

[nkhusainov]

I've added 2 examples:

1. Logging Metrics Using Built-in Methods
This example works well.
2. Uploading TensorBoard Logs
This example is an interesting use case as it allows uploading TensorBoard logs. However, there are several technical challenges to address, and I’d like your advice on how best to resolve them:
   1. Accessing Experiment and Run Names
The start_upload_tb_log method requires experiment and run names, which are set at runtime.
      • Code Reference 1
      • Code Reference 2
        Question:
What is the best way to access these variables?
One option is to set them as object attributes during prepare_step_run, but this approach feels suboptimal. Are there cleaner alternatives?
   2. Handling Credentials
Currently, there is no way to pass credentials explicitly to the start_upload_tb_log method. It relies on GCP default credentials, which are environment-dependent.
Question:
What would be a better approach to manage credentials for this method?

[htahir1]

Hmm good questions. I'm also not sure - probably the best way right now is to set them in the experiment tracker class and then fetch them using client.active_stack.experiment_tracker.XYZ...

In case of credentials, one could leverage service connectors, but I think its a safe assumption that the vertex execution role has the required credentials .. so i'd leave that be for now

[nkhusainov]

Done

[hyperlint-ai]

The style guide flagged several spelling errors that seemed like false positives. We skipped posting inline suggestions for the following words:

• MLOps

[nkhusainov]

@htahir1 , @schustmi Vertex AI Experiment Tracker requires google-cloud-aiplatform[tensorboard,autologging] to be installed. Should I update zenml gcp integration ?

[htahir1]

@nkhusainov i think you can check for it maybe in code and throw an error rather than having it as part of the gcp integration

[nkhusainov]

@nkhusainov i think you can check for it maybe in code and throw an error rather than having it as part of the gcp integration

At which point in time do you see this check happening?

• During the experiment tracker initialization ? (e.g., here)
• or at prepare_step_run? prepare_step_run

Do you have a utility I can reuse (maybe this one ) Or should I create my own?

[htahir1]

Probably I’d do during the initialization of the run and check if they passed in tensorboard dir and if so throw an error if the module isn’t installed ? I think we have some utilities in source_utils that you can leverage, e.g., [this one](https://github.com/zenml-io/zenml/blob/c68275beec2a5571117a4c47500890fbc9c675b1/src/zenml/utils/source_utils.py#L567)