MLFlow Logging Implementations

Hello all!
My group is using MLFlow for there experiment tracking and I am trying to figure out how to use MLFlow with Pytorch Lightning. Unfortunately the documentation is lacking.

I was wondering if someone could give the me implementation to log/save the following in MLFlow:

  1. A param of text: {key=“description”, value={“this model/experiment does the following:…”} (this is a single item)
  2. A param of a metric like training accuracy for every epoch (this is a multi-item)
  3. An artifact of the model definition/class (for inference later)
  4. An artifact of a checkpoint (for inference later)
  5. An artifact of the testing datasets labels y
  6. An artifact of the testing datasets prediction labels

Anyone have experience with this?