'config.pbtxt' could not be inferred. please provide specific config.pbtxt definition.

This basically means there is no configuration on how to serve the mode, i.e. size/type of lower (input) layer and output layer.
You can wither store the configuration on the creating Task, like is done here:
https://github.com/allegroai/clearml-serving/blob/b5f5d72046f878bd09505606ca1147d93a5df069/examples/keras/keras_mnist.py#L51
Or you can provide it as standalone file when registering the model with clearml-serving, an example for config.pbtxt :
https://github.com/triton-inference-server/server/blob/main/qa/python_models/identity_fp32/config.pbtxt

Just to confirm AgitatedDove14 - clearml doesn’t do any “magic” in regard to this for tensorflow, pytorch etc right?

Ah, just saw from the example that even that is doing the config pbtxt stuff - https://github.com/allegroai/clearml-serving/blob/main/examples/keras/keras_mnist.py#L51

Hi TrickySheep9 , can you provide more info on your specific use-case?

Here’s an example error I get trying it out on one of the example models:
Error: Requested Model project=ClearML Examples name=autokeras imdb example with scalars tags=None not found. 'config.pbtxt' could not be inferred. please provide specific config.pbtxt definition.

So for adding a model for serve with endpoint you can use

clearml-serving triton --endpoint "<your endpoint>" --model-project "<your project>" --model-name "<your model name>"
when the model is getting updated, is should use the new one

Hi TrickySheep9 , is this model register in your clearml app?

Yes. It's a pickle file that I have added via OutputModel

And other question is clearml-serving ready for serious use?

AgitatedDove14 - any thoughts on this?

But you have to do config.pbtxt stuff right?

And other question is clearml-serving ready for serious use?

Define serious use? KFserving support is in the pipeline, if that helps.
Notice that clearml-serving is basically a control plane for the serving engine, not to neglect the importance of it, the heavy lifting is done by Triton 🙂 (or any other backend we will integrate with, maybe Seldon)

Sure, got it. Will play around with it 🙂

Thanks

👍

Hey SuccessfulKoala55 Like I mentioned, I have a spacy ner model that I need to serve for inference.

clearml doesn’t do any “magic” in regard to this for tensorflow, pytorch etc right?

No 😞 and if you have an idea on how, that will be great.
Basically the problem is that there is no "standard" way to know which layer is in/out

Found the custom backend aspect of Triton - https://github.com/triton-inference-server/python_backend
Is that the right way?