BentoML

In BentoML, the BentoSerivce `@api`  function is where we recommend putting the preprocessing code, here you can find some example projects: <https://github.com/bentoml/gallery>

You don’t need custom artifact for that

You can load a tf.SavedModel from disk directly as well, check out the docs here:  <https://docs.bentoml.org/en/latest/frameworks.html#tensorflow-2-0>

My model subclasses tf.keras.Model and then overrides the train_step and test_step. I don't think savedmodel serializes these methods yet, so I have to build a new model and load the weights. Doesn't seem like the present artifact works like that, no?

I think this is supported out of the box, I might be wrong - cc <@UQF2L5GQ0>

<@U01EWTTBWVA>
`tf.savedmodel` serializes methods that marked by `tf.function` . If your model is built with Keras API (including subclasses of `tf.keras.Model`) without a  `tf.function`.  `tf.savedmodel` would serializes the default method `__call__` with optional args `training`.

`TensorFlowSavedModelArtifact` is using `tf.savedmodel` to serialize models.

Yep. TensorFlow always gets things complex at beginning:thinking_face:. We just have some new examples using tf.keras API, it may help you: <https://github.com/bentoml/gallery/blob/master/tensorflow/imdb/imdb_text_classification.ipynb>

So would it be okay for me to create a class incorporating the artifacts in the `__init__` of `BentoService`?

Just tried it and it looks like I cannot. Is there any workaround? The reason that I ask is that I already have all my preprocessing packaged in a wrapper class around my tf.keras model. it would be nice to be able to reuse that code

Hey <@U01EWTTBWVA>  - what’s the reason you want to do that?

You can just import that class in your bentoservice API function

what do you mean `create a class incorporating the artifacts in the `__init__` of `BentoService`?`

i have a class with a model instance where the model instance is of type tf.keras.Model

the way my code is designed, this class manages preprocessing and post processing. so it would need to take artifacts.model in its constructor

just use this class in your BentoService class then

you just need to add custom logic in `__init__` right?

As long as you call `super` ’s init method in your overrided init method, it should work

Are these related to your issue?
<https://github.com/bentoml/BentoML/issues/1065>
<https://github.com/bentoml/BentoML/issues/1037>

you can also just lazily initialize this class in your BentoService API callback function

those apply, but lazy initializing works fine for me. that's what I ended up trying

got it, thanks Luke, I will be working on a fix for those two tickets soon, but probably after the next release is out