I have been building my muscle memory for MLOps and the progress has been good so far. Thanks to Coursera’s Specialization, ML Design Patterns book, and Vertex AI’s neat examples.
I wanted to build a simple Vertex AI pipeline that should train a custom model and deploy it. TFX pipelines seemed like a way easier choice for this than KFP pipelines.
I am now referring to this stock example:
I see loads of argument parsing here and there, especially in the model building utilities. For reference, here’s a snippet that creates
Trainer in the initial TFX pipeline:
# Brings data into the pipeline. example_gen = tfx.components.CsvExampleGen(input_base=data_root) # Uses user-provided Python function that trains a model. trainer = tfx.components.Trainer( module_file=module_file, examples=example_gen.outputs['examples'], train_args=tfx.proto.TrainArgs(num_steps=100), eval_args=tfx.proto.EvalArgs(num_steps=5))
run_fn only takes
fn_args as its arguments. I am wondering how the arguments passed and mapped inside
I will be grateful for an elaborate answer.