CODEWITHSUNDEEP
tensorflowgoogle-cloud-mlhyperparameters
Benjamin Trendelkamp-Schroer
Benjamin Trendelkamp-Schroer

Reputation: 121

Google Cloud ML: How can I enforce a pure grid-search for a hyperparameter tuning job

Google Cloud ML uses Bayesian optimisation to mitigate the curse of dimensionality. In specific situations I have hyperparameter tuning jobs in which I want to enforce an exhaustive search over a grid of hyperparameters in a hyperparameter-tuning job. How can I do this?

My motivation for enforcing a pure grid-search is: I have observed that a hyperparameter-tuning job for hyperparameters which are exclusively of DISCRETE type, evaluates the same combination of hyperparameters more than once, which I do not want. I am suspecting it has to do with the use of Bayesian optimisation. This is why I would like to enforce a pure grid-search for those cases.

Upvotes: 1

Views: 117

Answers (1)

Chris Meyers
Chris Meyers

Reputation: 1426

There is not currently an argument available to enforce a grid search.

The best workaround currently is probably to submit multiple jobs, with the specific hyperparameters set for each one. This can be done without changing the code, as you can specify the values as user command line arguments. You should be able to submit all the jobs in a loop, and Google Cloud ML will queue them if there are too many to run at once. The downside is that you'll have to figure out which is the best.

Upvotes: 1

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