Use an already trained Keras model to predict on lots of data
#35 aberto em 31 de ago. de 2018
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Description
A common approach is to train on a bit of data and then use that trained model to predict on lots of data. We could do this using ParallelPostFit in dask-ml, or we can use X.map_blocks or df.map_partitions. In either case we might want to be a bit careful about avoiding repeated serializations costs. For example, in the following case I suspect that we include the serialized model in every task
# maybe bad?
model = load_model()
predictions = X.map_blocks(model.predict)
It's probably better to encourage the user to keep the model delayed
# maybe bad?
model = dask.delayed(load_model)()
predictions = X.map_blocks(model.predict)
We should also ensure that dask-ml does this correctly, and includes the model as a single task in the graph so that it gets sent around appropriately (cc @TomAugspurger )
I'm also generally curious if a Keras model that lives on the GPU will eventually make its way back onto the GPU when deserializing.