Would that be with Beam or Spark or TF_CONFIG? And if choice of hyperparameters is delegated to the trials, how do you do global optimization of hyperparameters?
Fascinating to see people looking at Keras Tuner and saying, "you can't distribute that", when in fact distribution has been a primary design concern, and we're going to provide a single-command way to go from a local search to a massively distributed search
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question in「deep learning with python」2.2.10 「The time axis is always the second axis」 「an entire day of trading is encoded as a 2D tensor of shape(390, 3) (there are 390 minutes in a trading day)」 390 is the time、so why not (3, 390)? (forgive my poor english)
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Nice.
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There are two levels of distribution possible. 1. Hyperparameter level 2. Model level Are you saying that both strategies can work with Keras tuner. If yes, then you can proudly say that it is distributed distributed or massively distributed.
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