I'd like to know if there is a way(or some sort of code example) to load an encoded pre-trained model (written in python) inside a Flink streaming application. So I can fit the model using the weights loaded from the file system and the data coming from from stream.
Thank you in advance
You can do this in a number of different ways. Generally, the simplest way would be to simply invoke the code that downloads the model from some external storage like s3 for example in the open method of your function. Then You can use the library of Your choice to load the pre-trained weights and process the data. You can look for some inspiration here, this is the code for loading model serialized with protobuf read from Kafka, but You can use it to understand the principles.
Normally I wouldn't recommend reading the model from the file system as it's much less flexible and troublesome to maintain. But that can be possible too, depending on Your infrastructure setup. The only thing, in that case, would be to assert that the file with the model is available on the machine that Pipeline will run on.
