# Add a datasource. This will automatically track and version it. ds = CSVDatasource(name='Pima Indians Diabetes', path='gs://zenml_quickstart/diabetes.csv') training_pipeline.add_datasource(ds) # Add a split training_pipeline.add_split(RandomSplit( split_map={'train': 0.7, 'eval': 0.3})) # Add a preprocessing unit training_pipeline.add_preprocesser( StandardPreprocesser( features=['times_pregnant', 'pgc', 'dbp', 'tst', 'insulin', 'bmi', 'pedigree', 'age'], labels=['has_diabetes'], overwrite={'has_diabetes': { 'transform': [{'method': 'no_transform', 'parameters': {}}]}} )) # Add a trainer training_pipeline.add_trainer(FeedForwardTrainer( loss='binary_crossentropy', last_activation='sigmoid', output_units=1, metrics=['accuracy'], epochs=20)) # Add an evaluator training_pipeline.add_evaluator( TFMAEvaluator(slices=[['has_diabetes']],
# Add a split training_pipeline.add_split( CategoricalDomainSplit(categorical_column="name", split_map={ 'train': ["arnold", "nicholas"], 'eval': ["lülük"] })) # Add a preprocessing unit training_pipeline.add_preprocesser( StandardPreprocesser(features=["name", "age"], labels=['gpa'], overwrite={ 'gpa': { 'transform': [{ 'method': 'no_transform', 'parameters': {} }] } })) # Add a trainer training_pipeline.add_trainer( FeedForwardTrainer(batch_size=1, loss='binary_crossentropy', last_activation='sigmoid', output_units=1, metrics=['accuracy'], epochs=i)) # Run the pipeline locally