diff --git a/docs/manage/security/tls.rst b/docs/manage/security/tls.rst index 361cb219516..6340856b26b 100644 --- a/docs/manage/security/tls.rst +++ b/docs/manage/security/tls.rst @@ -106,7 +106,7 @@ The following websites provide more information about installing snapd and Certb - `Installing snap on Red Hat Enterprise Linux (RHEL) `_ - `Installing snap on CentOS `_ -- `certbot instructions `_ +- `certbot instructions `_ Adding EPEL to RHEL 8 --------------------- @@ -335,5 +335,5 @@ the following command: Most Certbot installations come with automatic renewal. Visit `Setting up automated renewals `__ to find out more. To learn how to test automatic renewal, visit the Certbot instructions (`CentOS - `__ or `Debian/Ubuntu - `__). + `__ or `Debian/Ubuntu + `__). diff --git a/harness/determined/keras/_tf_keras_trial.py b/harness/determined/keras/_tf_keras_trial.py index bdd3a929b16..e8b7c01c2dc 100644 --- a/harness/determined/keras/_tf_keras_trial.py +++ b/harness/determined/keras/_tf_keras_trial.py @@ -1046,8 +1046,9 @@ def build_training_data_loader(self) -> keras.InputData: a tuple of either ``(inputs, targets)`` or ``(inputs, targets, sample_weights)``. 4) A `keras.utils.Sequence - `__ returning a tuple - of either ``(inputs, targets)`` or ``(inputs, targets, sample weights)``. + `__ + returning a tuple of either ``(inputs, targets)`` + or ``(inputs, targets, sample weights)``. When using ``tf.data.Dataset``, you must wrap the dataset using :meth:`determined.keras.TFKerasTrialContext.wrap_dataset`. This wrapper is used @@ -1081,8 +1082,9 @@ def build_validation_data_loader(self) -> keras.InputData: a tuple of either ``(inputs, targets)`` or ``(inputs, targets, sample_weights)``. 4) A `keras.utils.Sequence - `__ returning a tuple - of either ``(inputs, targets)`` or ``(inputs, targets, sample weights)``. + `__ + returning a tuple of either ``(inputs, targets)`` + or ``(inputs, targets, sample weights)``. When using ``tf.data.Dataset``, you must wrap the dataset using :meth:`determined.keras.TFKerasTrialContext.wrap_dataset`. This wrapper is used