How would you partition a large dataset for multi-host TPU training

Question

Can i know How would you partition a large dataset for multi-host TPU training?

Answer 1

Partitioning a large dataset for multi-host TPU training ensures each host processes a distinct data shard, enabling efficient parallelism and reducing communication overhead.

Here is the code snippet you can refer to:

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

• TPUClusterResolver: Detects TPU configuration and host context.

• num_hosts: Derived from total replicas to calculate dataset splits.

• shard(): Ensures non-overlapping data slices per host for training efficiency.

• task_id: Identifies which shard belongs to the current host.

• prefetch() + shuffle() ensure optimal input pipeline performance.

Hence, dataset partitioning across TPU hosts enables scalable, efficient multi-host training with independent data processing per worker.