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This repository has been archived by the owner on Jan 21, 2025. It is now read-only.
Will there be any future plans to allow users to add Custom Tensorflow Hooks such as tf.estimator.LoggingTensorHook to enable custom functions during the training/eval loop such as passing back metrics to 3rd Party Services
When constructing the TPU estimator model mesh_tensorflow/transformer/utils:tpu_estimator_model_fn, it's fairly complicated to override the training_hooks and evaluation_hooks that is passed to tpu_estimator.TPUEstimatorSpec
Currently, the method I'm using is to override mesh_tensorflow.ops.MtfCheckpointSaverListener functions to enable custom logging/monitoring during training, which doesn't get called as frequently to capture training loss.
The text was updated successfully, but these errors were encountered:
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Will there be any future plans to allow users to add Custom Tensorflow Hooks such as
tf.estimator.LoggingTensorHook
to enable custom functions during the training/eval loop such as passing back metrics to 3rd Party ServicesWhen constructing the TPU estimator model
mesh_tensorflow/transformer/utils:tpu_estimator_model_fn
, it's fairly complicated to override thetraining_hooks
andevaluation_hooks
that is passed totpu_estimator.TPUEstimatorSpec
Currently, the method I'm using is to override
mesh_tensorflow.ops.MtfCheckpointSaverListener
functions to enable custom logging/monitoring during training, which doesn't get called as frequently to capture training loss.The text was updated successfully, but these errors were encountered: