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FAQ

Below are a list of commonly answered questions.

By default, all cards can be registered, listed, loaded and updated outside of a context manager. In fact, this was done on purpose to not lock users into a particular style. The context manager comes into play when using RunCards as they provide a means to group artifacts (Cards, metrics, params) under a specific project run. Technically, this grouping can still be achieved by using RunCards directly but this comes at the expense of more lines of code. The context manager tends to be a more convenient way of logging and tracking artifacts. In addition, when using an OpsmlProject, all artifacts are automatically viewable in the Opsml UI.

Recommendation based on needs

  • I'd like to be able to view all artifacts, metrics, graphs, reports in a UI
    • Use the OpsmlProject context manager
  • I don't really need to see all of the artifacts, I care more that they are tracked and callable when needed
    • Use whatever you prefer
  • I like grouping runs/experiments by projects. UI doesn't really matter.
    • Use the OpsmlProject context manager

How do I turn off onnx conversion?

Auto onnx conversion is turned off by default. It can be turned on by setting to_onnx=True in the ModelCard constructor.

How do I supply my own onnx definition?

If you'd like to create your onnx model yourself and associate that with the ModelCard, you will need to provide your own implementation of the onnx_model arg. An example of this can be seen here.

What's with using name, repository, and contact or CardInfo in the examples?

Every ArtifactCard requires a name, repository and contact. For convenience, you can instead provide a CardInfo instance.

How is OpsML different than other products out there?

A key difference between OpsML and other products is that OpsML was not designed to be a platform or lock you in to any specific way of doing things. Instead, the goal and initial idea behind OpsML was to provide tooling and an interface that stitches, standardizes and automates some of the key building blocks (storage, tracking, versioning) to any machine learning workflow or platform. Thus, OpsML is capable of being used with other platforms or machine learning workflow systems. Or you can build your own!