TFX custom config argument in trainer not working

TensorFlow General Discussion
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1

This question is based on the TFX recommender tutorial. Please note that the code is being orchestrated by LocalDagRunner rather than run interactively in a notebook.

In the Trainer, we pass in a custom_config with the transformed ratings and movies:

trainer = tfx.components.Trainer(
    module_file=os.path.abspath(_trainer_module_file),
    examples=ratings_transform.outputs['transformed_examples'],
    transform_graph=ratings_transform.outputs['transform_graph'],
    schema=ratings_transform.outputs['post_transform_schema'],
    train_args=tfx.proto.TrainArgs(num_steps=500),
    eval_args=tfx.proto.EvalArgs(num_steps=10),
    custom_config={
        'epochs':5,
        'movies':movies_transform.outputs['transformed_examples'],
        'movie_schema':movies_transform.outputs['post_transform_schema'],
        'ratings':ratings_transform.outputs['transformed_examples'],
        'ratings_schema':ratings_transform.outputs['post_transform_schema']
        })

The problem is that all of the outputs passed into custom_config seem to be empty. This results in errors, for example

class MovielensModel(tfrs.Model):

  def __init__(self, user_model, movie_model, tf_transform_output, movies_uri):
    super().__init__()
    self.movie_model: tf.keras.Model = movie_model
    self.user_model: tf.keras.Model = user_model

    movies_artifact = movies_uri.get()[0]

complains that movie_uri.get() is empty. The same is true for ratings. Ratings passed in through the examples parameter however are not empty (the artefact uri is available), so it seems as though this custom_config is ‘breaking things’.

I have tried debugging it but to no avail. I did notice that arguments in custom_config are serialised and deserialised, but this didn’t seem to be the cause of the problem. Does anyone know why this happens and how to resolve this?

3

That seems really odd. I’ve reached out to see if anyone has any ideas, but there are a couple of things you can try. You can add print statements (logging.info, etc) to inspect the outputs from the components in the custom config. And you can also set a breakpoint (maybe using pdb) and inspect. If they’re empty then it suggests that the trainer isn’t waiting for them to finish, and there’s a race condition. It will also help to insert print statements in those components to verify when they run.