message: [tf.data service] Autotune uncompression and server-side prefetching.
diff: @@ -238,13 +238,9 @@ def _from_dataset_id(processing_mode,
  job_name=job_name,
  max_outstanding_requests=max_outstanding_requests,
  task_refresh_interval_hint_ms=task_refresh_interval_hint_ms)
  - # TODO(b/157105111): Make this an autotuned parallel map when we have a way
  - # to limit memory usage.
  - # The value 16 is chosen based on experience with pipelines that require
  - # more than 8 parallel calls to prevent this stage from being a bottleneck.
  dataset = dataset.map(
  lambda x: compression_ops.uncompress(x, output_spec=element_spec),
  - num_parallel_calls=16)
  + num_parallel_calls=dataset_ops.AUTOTUNE)
  # Disable autosharding for shared jobs.
  if job_name:
  @@ -537,11 +533,7 @@ def register_dataset(service, dataset):
  dataset = dataset.map(
  lambda *x: compression_ops.compress(x),
  num_parallel_calls=dataset_ops.AUTOTUNE)
  - # Prefetch one compressed element to reduce latency when requesting data
  - # from tf.data workers.
  - # TODO(b/157105111): Set this to autotune when we have a way to limit
  - # memory usage
  - dataset = dataset.prefetch(1)
  + dataset = dataset.prefetch(dataset_ops.AUTOTUNE)
  # Apply options so that the dataset executed in the tf.data service will
  # be optimized and support autotuning.
  dataset = dataset._apply_options() # pylint: disable=protected-access
  