try: for i in range(1, 6): training_pipeline = TrainingPipeline(name='csvtest{0}'.format(i)) try: # Add a datasource. This will automatically track and version it. ds = CSVDatasource(name='my_csv_datasource', path=os.path.join(csv_root, "my_dataframe.csv")) except AlreadyExistsException: ds = repo.get_datasource_by_name("my_csv_datasource") training_pipeline.add_datasource(ds) # 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(TFFeedForwardTrainer( batch_size=1,
from zenml.exceptions import AlreadyExistsException # Define the training pipeline training_pipeline = TrainingPipeline() # Add a datasource. This will automatically track and version it. try: ds = CSVDatasource(name='Pima Indians Diabetes', path='gs://zenml_quickstart/diabetes.csv') except AlreadyExistsException: ds = Repository.get_instance().get_datasource_by_name( 'Pima Indians Diabetes') 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': {} }] }
# Define the training pipeline training_pipeline = TrainingPipeline() # Add a datasource. This will automatically track and version it. try: ds = CSVDatasource(name='Pima Indians Diabetes', path='gs://zenml_quickstart/diabetes.csv') except AlreadyExistsException: ds = Repository.get_instance().get_datasource_by_name( 'Pima Indians Diabetes') training_pipeline.add_datasource(ds) # Add a split training_pipeline.add_split( RandomSplit(split_map={ 'train': 0.7, 'eval': 0.3 }).with_backend(processing_backend)) # 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': {}