How to only train some of the models in TabularPrediction.fit()

In my task the neural network component is adding a lot to the training time - which isn’t a big deal - and a lot to the inference time - which is more important. The NN component isn’t adding a lot to the already excellent performance of the axis-aligned models in this case.

I’d like to fit the model without the NN. It appears that models can be specified in the hyperparameter option as

hyperparameters={'custom':['GBM']}

but that only GBM is supported at the moment. Can this be extended to the other models?

[jwmueller]

Note: if you mainly care about prediction time (inference latency), you also don’t necessarily need to prevent AutoGluon from training all of its models. If you don’t require a high-accuracy ensemble, you can instead just tell AutoGluon to use the fastest of these trained models at inference time, like this:

First, invoke your default predictor = task.fit() call without specifying the auto_stack, hyperparameter_tune or bagging-related arguments. Here there is no need to specify the hyperparameters dict, so AutoGluon will train all of its models (if the fit() time is ok with you; if you find the fit() time too long, then specify only a subset of the models in the hyperparameters dict as I outlined in the previous comment).

Second, call predictor.leaderboard(only_pareto_frontier=True) and find the model with the smallest value of ‘pred_time_val’ (this model is the fastest for predictions), suppose its name is: MODEL_NAME.

Note: make sure you’ve installed the latest version of autogluon from the master branch to access this functionality.

To use solely this faster model for your predictions, simply call: predictor.predict(test_data, model = MODEL_NAME)

If you care about online inference with individual test data-points one-at-a-time, then you should first persist the models in memory like this (Note: this functionality is still under active development and not documented):

predictor._learner.persist_trainer()
pred1 = predictor.predict(test_datapoint1, model=MODEL_NAME)
pred2 = predictor.predict(test_datapoint2, model=MODEL_NAME)
...

[jwmueller]

Apologies we should clarify our TabularPrediction documentation. The hyperparameters dict currently has the following possible keys: ‘NN’, ‘GBM’, ‘CAT’, ‘RF’, ‘XT’, ‘KNN’, ‘custom’ corresponding to 7 different models that can be trained. To omit one of these models during task.fit(), you simply drop that key from the
hyperparameters dict. For example, you can tell AutoGluon to only consider RF and GBM models (with their default hyperparameter settings) via:

task.fit(..., hyperparameters={'RF':{}, 'GBM':{}})

The ‘custom’ key offers different functionality than you described. You should not modify the GBM value for this key, but you can drop the key from hyperparameters if you’d like to omit the custom model. If you include ‘custom’ in hyperparameters, then AutoGluon will train additional custom models. For now, the only additional custom model is a second GBM model with different (preset) hyperparameter settings than the default GBM model that gets trained when hyperparameters contains the GBM key.