Hyperparameter Tuning using Keras Tuner with TPU distributed strategy

How do i run tpu distributed strategy with keras tuner?.
do i write bash script for all the replicas? that would be 8 bash scripts? and 1 for chief-worker?
and the content for tuning.py would be the same for all the workers and chief right?
please help!
any suggestions would be appreciated

Hi @Sohail_Mohammad,

I think you don’t necessarily need separate bash scripts for each replica, as the distribution strategy and the tuner can be managed within your main training script.

To implement TPU distributed training with Keras Tuner, begin by designing a model-building function that accounts for hyperparameters. Establish a TPU cluster using tf.distribute.TPUStrategy and craft your model within this scope. Define the preferred optimization metric, such as validation accuracy. Instantiate a Keras Tuner instance, indicating the model function and target metric. Construct a search function where the model is built and trained within the TPU strategy context. Initiate the tuner’s search procedure to identify optimal hyperparameters, harnessing the parallel training capabilities of TPUs for enhanced efficiency and performance enhancement.

I hope this helps!


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