Hello, I’m trying to deploy an instance of Rasa X 0.24.6 with Rasa 1.6.1 using Docker. The model has been trained on a different machine using local Rasa 1.6.1 installation without Docker and is loaded into Rasa X. docker-compose.yml is the default version, the contents of docker-compose.override.yml version: '3.4' services: app: image: myimage:latest environment: - PYTHONPATH=/app/ volumes: - ./actions:/app/actions expose: - "5055" The app uses a custom component customNLU.py and is packed with the following Dockerfile: FROM rasa/rasa-sdk:1.6.1 COPY ./* /app/ ENV PYTHONPATH=$PYTHONPATH:/app/ That is, the structure of the app is pretty flat: - app - __init__.py - actions - __init__.py - actions.py - customNLU.py - config.yml - [everything else] The content of config.yml: language: de_core_news_sm pipeline: - name: "SpacyNLP" - name: "SpacyTokenizer" - name: "SpacyFeaturizer" - name: "RegexFeaturizer" - name: "CRFEntityExtractor" - name: "EntitySynonymMapper" - name: "SklearnIntentClassifier" - name: "customNLU.MinimalKeywordSearch" policies: - name: MemoizationPolicy - name: MappingPolicy - name: KerasPolicy Every docker-compose up call throws a sequence of errors: rasa-production_1 | 2020-01-29 14:58:02 DEBUG rasa.core.agent - Found new model with fingerprint f31b006c6e2504bcdc051c4b3f532bb6. Loading... rasa-x_1 | INFO:rasax.community.services.event_consumers.pika_consumer:Start consuming queue 'rasa_production_events' on pika host 'rabbit'. rabbit_1 | 14:58:11.73 INFO ==> Stopping RabbitMQ... rasa-production_1 | 2020-01-29 14:58:12 ERROR rasa.core.agent - Could not load model due to Failed to find module 'customNLU'. rasa-production_1 | No module named 'customNLU'. rasa-production_1 | [2020-01-29 14:58:12 +0000] [1] [ERROR] Experienced exception while trying to serve rasa-production_1 | Traceback (most recent call last): rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/nlu/registry.py", line 154, in get_component_class rasa-production_1 | return class_from_module_path(component_name) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/utils/common.py", line 195, in class_from_module_path rasa-production_1 | m = importlib.import_module(module_name) rasa-production_1 | File "/usr/local/lib/python3.6/importlib/__init__.py", line 126, in import_module rasa-production_1 | return _bootstrap._gcd_import(name[level:], package, level) rasa-production_1 | File " ", line 994, in _gcd_import rasa-production_1 | File " ", line 971, in _find_and_load rasa-production_1 | File " ", line 953, in _find_and_load_unlocked rasa-production_1 | ModuleNotFoundError: No module named 'customNLU' rasa-production_1 | rasa-production_1 | During handling of the above exception, another exception occurred: rasa-production_1 | rasa-production_1 | Traceback (most recent call last): rasa-production_1 | File "/build/lib/python3.6/site-packages/sanic/app.py", line 1133, in run rasa-production_1 | serve(**server_settings) rasa-production_1 | File "/build/lib/python3.6/site-packages/sanic/server.py", line 857, in serve rasa-production_1 | trigger_events(before_start, loop) rasa-production_1 | File "/build/lib/python3.6/site-packages/sanic/server.py", line 634, in trigger_events rasa-production_1 | loop.run_until_complete(result) rasa-production_1 | File "uvloop/loop.pyx", line 1456, in uvloop.loop.Loop.run_until_complete rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/run.py", line 247, in load_agent_on_start rasa-production_1 | action_endpoint=endpoints.action, rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/agent.py", line 248, in load_agent rasa-production_1 | model_server, rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/agent.py", line 64, in load_from_server rasa-production_1 | await _update_model_from_server(model_server, agent) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/agent.py", line 126, in _update_model_from_server rasa-production_1 | _load_and_set_updated_model(agent, model_directory, new_model_fingerprint) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/agent.py", line 87, in _load_and_set_updated_model rasa-production_1 | interpreter = RasaNLUInterpreter(model_directory=nlu_path) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/interpreter.py", line 272, in __init__ rasa-production_1 | self._load_interpreter() rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/core/interpreter.py", line 295, in _load_interpreter rasa-production_1 | self.interpreter = Interpreter.load(self.model_directory) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/nlu/model.py", line 301, in load rasa-production_1 | return Interpreter.create(model_metadata, component_builder, skip_validation) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/nlu/model.py", line 323, in create rasa-production_1 | components.validate_requirements(model_metadata.component_classes) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/nlu/components.py", line 38, in validate_requirements rasa-production_1 | component_class = registry.get_component_class(component_name) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/nlu/registry.py", line 180, in get_component_class rasa-production_1 | raise ModuleNotFoundError(exception_message) rasa-production_1 | ModuleNotFoundError: Failed to find module 'customNLU'. rasa-production_1 | No module named 'customNLU' rasa-production_1 | Starting Rasa X in production mode... 🚀 rasa-production_1 | Traceback (most recent call last): rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/nlu/registry.py", line 154, in get_component_class rasa-production_1 | return class_from_module_path(component_name) rasa-production_1 | File "/build/lib/python3.6/site-packages/rasa/utils/common.py", line 195, in class_from_module_path rasa-production_1 | m = importlib.import_module(module_name) rasa-production_1 | File "/usr/local/lib/python3.6/importlib/__init__.py", line 126, in import_module rasa-production_1 | return _bootstrap._gcd_import(name[level:], package, level) rasa-production_1 | File " ", line 994, in _gcd_import rasa-production_1 | File " ", line 971, in _find_and_load rasa-production_1 | File " ", line 953, in _find_and_load_unlocked rasa-production_1 | ModuleNotFoundError: No module named 'customNLU' and the same error within rasa-worker. I checked out similar issues here on the forum and github and tried putting PYTHONPATH in various containers, I can see /app/actions when executing echo $PYTHONPATH within the running app container. However, the whole service never starts completely and shuts down eventually. Is there any clue what could go wrong here? Thanks!

Helm charts are used to create a kubernetes deployment. They still use docker images, so passing the same custom image in the helm chart as you were passing in docker-compose should work fine. In that case, you would update your values.yml like this: rasa: name: "<your-custom-image-name e.g. mydockerhubaccount/rasa" # tag of the Rasa Open Source image to use tag: " I believe you’d need to use a configMap to create the volume to mount.

I’m still looking into this, but since the custom component is used during the training/execution of the model and not just the action server, it somehow needs to be added to the rasa services, not the app service. Will update if I find something out.

@sureshvarman @mgalkin as @mloubser said, the file needs to be mounted to the rasa containers. You can achieve this in the docker-compose.override.yml: version: '3.4' services: rasa-production: volumes: - ./customNLU.py:/app/customNLU.py rasa-worker: volumes: - ./customNLU.py:/app/customNLU.py

@sureshvarman @mgalkin did my comment help you resolve your issue?

@erohmensing I followed your advise after adding a pre-trained sentiment analyzer (nltk) as a custom component. It is mounted to the containers and trained, but now the interactive learning does not react, predicts no intent and loads forever. When using the share feature to test the model the user’s messages are not even shown. Locally everything works fine. EDIT: It seems like it has to do with the requirements of the component. I want to use the vader lexicon, which requires me to run a command nltk.download('vader_lexicon)'. How can I add the requirements to the rasa containers? I can pip install by accessing the containers, but having to do this every time I recompose is not fun. Also there is still no way to download the lexicon into the containers.

Just came across this again - You could also load in the lexicon as a volume, you’d just need to put it in a directory NLTK is aware of. Then you wouldn’t have to download it inside the container.

Thanks, I actually already solved this problem. It indeed saved the lexicon in an inaccessible location, so I just moved it to the usr folder.

Hi @erohmensing and @mloubser how can the same thing be achieved when deployment is done by use of Helm chart? I am assuming I’ll have to add a line in Dockerfile to copy the required files to rasa-production and rasa-worker pods. Please correct me if I am wrong. Thanks in advance!

Yes, you could extend the rasa image to include copying the lexicon into the image.

Hey @mloubser, I am not having much background about the working of helm charts. I saw a lot of discussions in the forum for rasa x deployed with docker. So I am not very clear about where exactly will the files copying should be done and how… :pensive: All I could make out from the discussions is - the copying should be specified here or here. I am NOT sure where it will sit also, I am not sure how to specify it. It will be really very helpful if you could help!! :pray:

Rasa X Docker ModuleNotFoundError with a Custom Component

[Deprecated] Rasa X Community Edition

sureshvarman (suresh varman) February 11, 2020, 10:17am 3

+1 for this.

Basic question regarding custom connectors/channels and Kubernetes deployment

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Rasa X Docker ModuleNotFoundError with a Custom Component

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