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TF2 was released and currently a new GPflow is being developed as well. TF2 is a major change and it seems to make a lot of things easier.
Looking at the development of GPflow, I believe we will be able to make a compatible version again. Unfortunately, we'll need to redesign and rethink the structure of GPflowOpt:
We can delete a lot code, include the awful modelwrapper
GPflow 2.0 seems to further decrease the uniformity in the way models deal with data and optimization of hyperparameters. This may still be improved but I don't think we should rely on this.
My idea now is to create an abstract BO class which implements the main flow and calls a bunch of abstract methods along the way. We provide an implementation class for vanilla BO which uses a GPR, more complex strategies are not supported out of the box. Some things to consider:
using tf bijectors for scaling
Rewrite domain as a tf.Module and use tf optimizers (need a transform for the bounds)?
Include tf.summary for TensorBoard
Use checkpointing to safeguard long running BO runs against crashes.
The text was updated successfully, but these errors were encountered:
TF2 was released and currently a new GPflow is being developed as well. TF2 is a major change and it seems to make a lot of things easier.
Looking at the development of GPflow, I believe we will be able to make a compatible version again. Unfortunately, we'll need to redesign and rethink the structure of GPflowOpt:
My idea now is to create an abstract BO class which implements the main flow and calls a bunch of abstract methods along the way. We provide an implementation class for vanilla BO which uses a GPR, more complex strategies are not supported out of the box. Some things to consider:
The text was updated successfully, but these errors were encountered: