Hi - yeah sure. The modeldef file is a YAML file which defines, at its most basic, the input and output features. In this most basic of examples, we're telling Ludwig that the input features are either numerical or category. This then preprocessed the data to train our model.
More complex models may use a level of NLP to break down sentences or process images. This would all be specified in the input feature.
Equally, we may want to define how the output features are generated. Again, that's specified here.
In my final refinement above, I've also specified the training parameters.
So for a scenario specific question I'm running ludwig in colab and the format of the yaml file as described in the docs doesn't work the same way rendering errors. Have you had an opportunity to explore this?
I think the explanation at the top of the notebook may have been the issue you were having. I had been running from terminal so hadn't come across it. See Github issue for details.
The first bit is getting the data and just dropping NaNs. Also changed one column name as I couldn't work out how to get it working with column names with spaces in.
The rest is the training. The key is that the training is code free.
In both instances however you use a pre-built model_definition.yaml file. Any chance you would now how to create a simple model_definition.yaml file from scratch in-line? I kind of wanted to understand how that would work as it is not described in the documents so well.
It's not "pre-built" - you have to write the model definition yourself.
The docs at uber.github.io/ludwig/user_guide/#... explain the basics of the model definition file. It's just a yaml file defining the input and output features along with any additional parameters you want to override the Ludwig defaults.
Can you please elaborate setting up the definition of yaml for model definition??
Hi - yeah sure. The modeldef file is a YAML file which defines, at its most basic, the input and output features. In this most basic of examples, we're telling Ludwig that the input features are either numerical or category. This then preprocessed the data to train our model.
More complex models may use a level of NLP to break down sentences or process images. This would all be specified in the input feature.
Equally, we may want to define how the output features are generated. Again, that's specified here.
In my final refinement above, I've also specified the training parameters.
Documentation and plenty of examples can be found here - uber.github.io/ludwig/examples/
The final, full modeldef.yaml from the above example looks like gist.github.com/c-m-hunt/3271efb2a...
So for a scenario specific question I'm running ludwig in colab and the format of the yaml file as described in the docs doesn't work the same way rendering errors. Have you had an opportunity to explore this?
Took a bit of fiddling to get running in Colab but here it is...
colab.research.google.com/drive/1Z...
I think the explanation at the top of the notebook may have been the issue you were having. I had been running from terminal so hadn't come across it. See Github issue for details.
The first bit is getting the data and just dropping NaNs. Also changed one column name as I couldn't work out how to get it working with column names with spaces in.
The rest is the training. The key is that the training is code free.
In both instances however you use a pre-built model_definition.yaml file. Any chance you would now how to create a simple model_definition.yaml file from scratch in-line? I kind of wanted to understand how that would work as it is not described in the documents so well.
It's not "pre-built" - you have to write the model definition yourself.
The docs at uber.github.io/ludwig/user_guide/#... explain the basics of the model definition file. It's just a yaml file defining the input and output features along with any additional parameters you want to override the Ludwig defaults.
Thank you. I figured it out. I just didn't realize how to start it off.