# Rasa.train and Rasa.model\_training.train\_nlu runs indefinitely on jupyter notebook

**URL:** <https://forum.rasa.com/t/rasa-train-and-rasa-model-training-train-nlu-runs-indefinitely-on-jupyter-notebook/52292>\
**Category:** Rasa Open Source\
**Created:** [March 31, 2022, 6:21pm UTC](https://forum.rasa.com/t/rasa-train-and-rasa-model-training-train-nlu-runs-indefinitely-on-jupyter-notebook/52292 "2022-03-31T18:21:51Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![edheinen](https://dub1.discourse-cdn.com/flex013/user_avatar/forum.rasa.com/edheinen/32/19880_2.png) [@edheinen](https://forum.rasa.com/u/edheinen)\
**Post date:** [March 31, 2022, 6:21pm UTC](https://forum.rasa.com/t/rasa-train-and-rasa-model-training-train-nlu-runs-indefinitely-on-jupyter-notebook/52292/1 "2022-03-31T18:21:51Z")

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Hi.

I’m working on a jupyter notebook to train, test and compare many models using Rasa, as fast as python would allow, and producing some custom reports I think can help my team choose between pipelines.

However, I always gets stuck after training the first model. Using [rasa.train](https://rasa.com/docs/rasa/2.x/reference/rasa/train/) or [rasa.model\_training.train\_nlu](https://rasa.com/docs/rasa/2.x/reference/rasa/model_training#train_nlu) my jupyter notebook just keeps running indefinitely after saving the trained model, never returning anything.

I’m using Rasa 2.8.26, python 3.8.12 and training models with:

```auto
model_path = rasa.train(
    domain = domain_file, 
    config = f"config/{config_file}", 
    training_files = [training_files], 
    output = 'models/', 
    persist_nlu_training_data = True,
    fixed_model_name = config_file[:-4])

```

or

```auto
model_path = rasa.model_training.train_nlu(
    domain = domain_file,
    config = f"config/{config_file}", 
    nlu_data = training_files, 
    output = 'models/', 
    persist_nlu_training_data = True,
    fixed_model_name = config_file[:-4],
    force_training = True)

```

I don’t have any stories available right now and just want to train and compare NLU models. At the beginning of training I get the message `No stories present. Just a Rasa NLU model will be trained.`

At the end of `rasa.train` I receive the message `NLU model training completed. Your Rasa model is trained and saved at [...]'` but the cell keeps running indefinitely.

So my questions are:

1. how should I train models in jupyter notebooks?
2. Should [rasa.train](https://rasa.com/docs/rasa/2.x/reference/rasa/train/) or [rasa.model\_training.train\_nlu](https://rasa.com/docs/rasa/2.x/reference/rasa/model_training#train_nlu) run indefinitely or am I not using it properly?
3. And, on a side note, how to run “async” functions like [compare\_nlu\_models](https://rasa.com/docs/rasa/2.x/reference/rasa/model_testing#plot_nlu_results) in jupyter notebooks?

Thanks and keep up the amazing work!

---

<div class="post-metadata">

**Author:** ![nik202](https://dub1.discourse-cdn.com/flex013/user_avatar/forum.rasa.com/nik202/32/16098_2.png) [@nik202](https://forum.rasa.com/u/nik202)\
**Post date:** [April 1, 2022, 11:33am UTC](https://forum.rasa.com/t/rasa-train-and-rasa-model-training-train-nlu-runs-indefinitely-on-jupyter-notebook/52292/2 "2022-04-01T11:33:51Z")

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@edheinen

1. how should I train models in jupyter notebooks?

I am not sure, why you are using JN. I’d recommend to you rasa open source only or rasa x for running the bot. You can train and save the model in the project file. If you are thinking that while using COLab OR JN it will train fast, then I don’t think so.

1. Should [rasa.train](https://rasa.com/docs/rasa/2.x/reference/rasa/train/) or [rasa.model\_training.train\_nlu](https://rasa.com/docs/rasa/2.x/reference/rasa/model_training#train_nlu) run indefinitely or am I not using it properly?

I guess both are same.

1. And, on a side note, how to run “async” functions like [compare\_nlu\_models](https://rasa.com/docs/rasa/2.x/reference/rasa/model_testing#plot_nlu_results) in jupyter notebooks?

Ref this link : [Testing Your Assistant](https://rasa.com/docs/rasa/testing-your-assistant/) everything is mentioned in detail.

I hope this will give you more clearity and good luck!

---

<div class="post-metadata">

**Author:** ![edheinen](https://dub1.discourse-cdn.com/flex013/user_avatar/forum.rasa.com/edheinen/32/19880_2.png) [@edheinen](https://forum.rasa.com/u/edheinen)\
**Post date:** [April 1, 2022, 12:30pm UTC](https://forum.rasa.com/t/rasa-train-and-rasa-model-training-train-nlu-runs-indefinitely-on-jupyter-notebook/52292/3 "2022-04-01T12:30:38Z")

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It’s not the training that I hope to run faster in JN but the testing. CLI’s rasa test takes ~1h to test only one model while I can load the model and make predictions for the same dataset in 2 mins with pandas. That’s also the reason I asked about the async functions, I plan to classify the whole dataset loading the model as a rasa interpreter, applying it to a pandas dataset with the examples and then using rasa methods to produce the classification reports.

Thanks for the answer I’ll train models using !rasa train […] and try to test them using JN.

edit1: I see the link you sent also uses the CLI, what I wanted to learn is how to run the async functions from the JN and use them to produce the classification reports from a pandas dataframe with the model’s predictions. I also wanted to train many models programmatically, loading all configs in a folder and training them one by one through the night, that’s why I was looking for a _non-CLI_ way of training and testing.

edit2: just found out I can pass variables as part of the shell commands in JN, like

```auto
config_file = ...
!rasa train --config {config_file}

```

The async functions and JN tests are still unanswered though.