启用shuffle时，tf.dataset.cache在分布式训练中是否仍具收益？

Great question! The short answer is yes—cache() still delivers meaningful performance gains in this scenario, even when each worker gets different data order in subsequent epochs. Here's why:

• Cache stores raw/preprocessed data, not order
  When you use cache(), TensorFlow saves the dataset (either raw or after preprocessing, depending on where you place the cache() call) to each worker's local RAM. The shuffle() operation runs after caching, so every epoch will re-shuffle the already-cached data in memory. This means you skip the expensive file system reads entirely after the first epoch, while still getting the random order you need for training.

• Distributed data sharding works seamlessly with cache
  In distributed training setups (like using MirroredStrategy or MultiWorkerMirroredStrategy), the dataset is split into unique shards assigned to each worker. Each worker only caches its own assigned shard—there's no overlap or shared cache between workers. So when subsequent epochs run, each worker shuffles its own cached shard independently, maintaining per-worker randomness while avoiding disk IO.

• Shuffle randomness isn't compromised
  As long as your shuffle buffer size is set appropriately (a common best practice is to use a size larger than your dataset shard, or at least large enough to ensure good randomness), caching doesn't hurt shuffle quality. Each epoch still generates a new random order from the cached data, just like it would if you were reading from disk—only much faster.

A quick example to illustrate the pipeline flow:

# Distributed training setup (simplified)
strategy = tf.distribute.MultiWorkerMirroredStrategy()

with strategy.scope():
    dataset = tf.data.Dataset.from_tensor_slices(...)
    dataset = dataset.map(preprocess_fn)  # Preprocess once, then cache
    dataset = dataset.cache()             # Cache preprocessed data to RAM
    dataset = dataset.shuffle(buffer_size=10000)  # Shuffle cached data each epoch
    dataset = dataset.batch(32).repeat()

In this setup, the first epoch will read and preprocess data from disk, then cache it. Every epoch after that will pull the preprocessed data directly from RAM and shuffle it—no more disk reads or preprocessing overhead.

内容的提问来源于stack exchange，提问作者Jaylin