I have a rasa project deployed to k8s cluster. In one commit I added few training phrases + intents, changed the config.yaml and updated some custom actions. I run the bot locally and it worked as expected. But when deployed to the cluster the bot fails to predict intents. ▶ The logs from the remote instance I suppose the prediction fails due to Failed to load ABCMeta from model storage. Resource 'train_DIETClassifier5' doesn't exist. Or Failed to load ABCMeta from model storage. Resource 'train_TEDPolicy2' doesn't exist. When I run the bot locally there are no these messages and there’s a log ‘Finished loading the model.’. Local versions: Rasa Version : 3.6.10 Minimum Compatible Version: 3.5.0 Rasa SDK Version : 3.6.2 Python Version : 3.10.7 Operating System : macOS-12.2.1-arm64-arm-64bit Remote versions: Rasa Version : 3.6.10 Minimum Compatible Version: 3.5.0 Rasa SDK Version : 3.6.2 Python Version : 3.10.6 Operating System : Linux-5.4.0-148-generic-x86_64-with-glibc2.35 My latest config - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer alias: "cvf-word" - name: DIETClassifier epochs: 100 constrain_similarities: true model_confidence: softmax - name: EntitySynonymMapper - name: FallbackClassifier threshold: 0.3 ambiguity_threshold: 0.02 policies: - name: RulePolicy priority: 2 enable_fallback_prediction: False - name: AugmentedMemoizationPolicy priority: 3 max_history: 10 - name: TEDPolicy priority: 1 epochs: 100 constrain_similarities: true nlu_fallback_threshold: 0.3 core_fallback_threshold: 0.3 core_fallback_action_name: action_default_fallback enable_fallback_prediction: true My previous config - name: LexicalSyntacticFeaturizer - name: CountVectorsFeaturizer alias: "cvf-word" - name: CountVectorsFeaturizer analyzer: char_wb min_ngram: 1 max_ngram: 4 - name: DIETClassifier epochs: 100 constrain_similarities: true model_confidence: softmax - name: EntitySynonymMapper - name: ResponseSelector featurizers: ["cvf-word"] epochs: 100 constrain_similarities: true model_confidence: softmax - name: FallbackClassifier threshold: 0.2 ambiguity_threshold: 0.02 policies: - name: RulePolicy enable_fallback_prediction: False - name: AugmentedMemoizationPolicy max_history: 0 - name: TEDPolicy epochs: 100 constrain_similarities: true nlu_fallback_threshold: 0.2 core_fallback_threshold: 0.2 core_fallback_action_name: action_default_fallback enable_fallback_prediction: true And it’s not just that the bot works locally and doesn’t remotely. Another thing is that I tried to retrain model with the previous config but with the rest of the latest changes and it doesn’t work exactly the same as it doesn’t work with the latest config. Although when I deploy the previous version of the code all together - it works fine. That’s confusing, cause the action code changes shouldn’t af…

There’s a recently discovered issue training on arm under 2.6 and then using the model on amd64. This seems to have been introduced in 3.6.x and does not happen with 3.5.x

@nik202 Hello, I’m sorry to bother you, but maybe you could take a look

As a temporary workaround I added the step to the Dockerfile building in CI/CD pipeline and it started to work: RUN rasa train --domain /app/domain.yml --data /app/data --out /app/models Still not sure why it doesn’t work when the model is stored in files.

@xxxwarrior is it working using docker ?

Yes. Dockerfile for rasa looks like this now, but it worked without the training step before. I trained the model locally. FROM rasa/rasa:3.6.10 COPY . /app USER root RUN python -m pip install -r /app/requirements.txt COPY entrypoint-rasa.sh /app/entrypoint-rasa.sh RUN chmod +x /app/entrypoint-rasa.sh RUN chown -R 1001:1001 /app RUN rasa train --domain /app/domain.yml --data /app/data --out /app/models --debug USER 1001

@xxxwarrior have you looked my thread for docker deployment?

@xxxwarrior try this thread and make the necessary changes as per the rasa version and let me know [image] Dockerizing my rasa chatbot application that has botfront Rasa Open Source @mangesh Please find the steps as per our discussion: For Wordpress: For creating a docker image for Wordpress site for deployment purpose only; running on localhost 8000 Quick Wordpress Setup With Docker - YouTube Deploying rasa on Wordpress site please follow this link DEPLOY RASA CHATBOT TO WORDPRESS WEBSITE | WORDPRESS | INNOVATE YOURSELF - YouTube For Docker Environment: Create the Dockerhub ID from https://hub.docker.com Create a Docker image for action server by the name Dock…

@stephens I overlooked the fact that I did updated the local rasa version recently and that sounds like it could be the cause since the bot works as expected if the model is trained while building the image. Thank you for mentioning that here, I’ll mark it as a solution.

@nik202 Your Dockerfile also contains the training step in it, which I thought could be avoided. However if @ stephens is right - I guess training during building is the easiest way to go. This is how things go from a temporary workaround to a somewhat permanent solution :grinning: Anyway, thank you for your reply, have a good one, cheers

[image] xxxwarrior: Your Dockerfile also contains the training step in it, which I thought could be avoided. @xxxwarrior Then how you are thinking to train the model if you updated any code, or intents or examples? Please enlighten me if you explored any other way around it will help the audience too. Cheers!

I used to train the model locally and then use the same model remotely. Another way is to train the model, store it somewhere like s3 and use it on a deployed instance. Both ways you don’t have to train the model during the build.

Failed to load ABCMeta from model storage. Resource 'train_DIETClassifier5' doesn't exist

Rasa Open Source

nik202 (NiK202) September 29, 2023, 10:39am 7

@xxxwarrior try this thread and make the necessary changes as per the rasa version and let me know

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Failed to load ABCMeta from model storage. Resource 'train_DIETClassifier5' doesn't exist

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