How can sharding datasets across TPU cores improve training performance Write the code to shard a dataset in TensorFlow

Question

Can you tell me How can sharding datasets across TPU cores improve training performance? Write the code to shard a dataset in TensorFlow.

Answer 1

Sharding datasets across TPU cores allows parallel data loading and processing, significantly speeding up training.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• tfds.load: Loads the MNIST dataset from TensorFlow Datasets.

• shard(): Splits the dataset into num_shards equal parts.

• index: Selects the shard for the current TPU core.

• Supports parallelism: Each core gets a unique slice, enabling concurrent training.

Hence, dataset sharding distributes the workload across TPU cores, boosting efficiency in model training.