update_freq documentation
@@ -2354,7 +2354,8 @@ class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):
same applies for `'epoch'`. If using an integer, let's say `1000`, the
callback will write the metrics and losses to TensorBoard every 1000
batches. Note that writing too frequently to TensorBoard can slow down
- your training.
+ your training. May not work when doing distributed training, as
+ currently only a subset of `tf.distribute.Strategy`s are supported.
profile_batch: Profile the batch(es) to sample compute characteristics.
profile_batch must be a non-negative integer or a tuple of integers.
A pair of positive integers signify a range of batches to profile.
@@ -2775,8 +2776,9 @@ class TensorBoard(Callback, version_utils.TensorBoardVersionSelector):
step=self._train_step,
)
- # `logs` is a `RemoteValue` when using asynchronous strategies, for now
- # we just disable `update_freq` entirely in those cases.
+ # `logs` is a `tf.distribute.experimental.coordinator.RemoteValue` when
+ # using asynchronous strategies, for now we just disable `update_freq`
+ # entirely in those cases.
if isinstance(logs, dict):
for name, value in logs.items():
tf.summary.scalar("batch_" + name, value, step=self._train_step)
