I don't understand how the Trainer component works

Question

As far as I understand, we need to supply the Trainer component training examples directly (with the "examples" parameter) from the output of an ExampleGen or a Transform component, but at the same time you also need to supply it with a "module_file" that has a run_fn which should take care of training and saving the model. My question is: since the run_fn only receive what it needs in the from of a FnArgs parameter, where exactly does it get the training (or evaluation) data?

To clarify, in the official tutorial (in the section titled "Write model training code"), the run_fn relies on an _input_fn that converts the data provided in the fn_args.train_files into a Dataset object, but where exactly is the fn_args.train_files provided? does the Trainer component under the hood infer this from the examples parameter and supplies that (besides other things needed) in the from of the fn_args parameter to the run_fn? if we're already supplying the examples directly to the Trainer component why there's no mechanism for the run_fn to access those directly? It's all very confusing! :(

Thanks in advance for your help!

Answer 1

The Trainer component is responsible for training the machine learning model.

It takes as input the output of a data preprocessing component such as ExampleGen or Transform and the model defined in the module_file and it outputs a trained model.

The run_fn is a function defined in the module_file provided to the Trainer component.

It receives inputs via a fn_args parameter, which includes necessary information for the training process.

This fn_args parameter typically includes things like the locations of input data files, model checkpoints, hyper parameters, and other configurations needed for training.

These parameters, fn_args.train_files is provided by the trainer component and contains the file paths to the training data.

This is where the data loaded by the Trainer component is made available to the run_fn.

Inside the run_fn, the input_fn is responsible for creating input data pipelines for training. The input_fn function reads these files and prepares the data for training.