Open strickvl opened 7 months ago
@christianversloot if I'm not completely mistaken, we had a conversation about this in Slack? Unfortunately the slack history hasn't been retained.
Correct, but that was for SageMaker orchestrator. There, I believe we limit name length to circumvent this error. It could perhaps be inspiration for a fix here :)
@christianversloot ah yes! https://github.com/zenml-io/zenml/pull/1505 was the PR.
@strickvl @christianversloot I would like to work on this... Please assign!
has the bug been solved? if not i would like to solve it
The bug has not been solved yet @Merthoshan. @ashutosh887 are you working on it, if not, can let @Merthoshan take a shot.
Where can we discuss if we're facing some issues understanding the bug? @strickvl I've worked it
Here is the place.
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What happened?
A user encountered an issue where the Vertex scheduler in ZenML does not accept pipeline names longer than 64 characters. This limitation requires users to shorten their pipeline names to successfully schedule their pipelines on Vertex AI, which can be restrictive and inconvenient.
Task Description
Investigate and address the issue with the Vertex scheduler's character limit for pipeline names in ZenML. The goal is to either increase the character limit or implement a mechanism that automatically handles longer names without user intervention.
Expected Outcome
Users should be able to schedule pipelines on Vertex AI without the constraint of a 64-character limit on pipeline names. ZenML should provide a seamless experience, either by accommodating longer names or by intelligently managing them within the existing Vertex scheduler limitations. This fix will enhance user convenience and flexibility in naming pipelines.
Steps to Implement
Additional Context
This issue is crucial for users who require descriptive and potentially lengthy names for their pipelines, especially in complex or large-scale MLOps environments.
Code of Conduct