from zenml.core.steps.trainer.feedforward_trainer import FeedForwardTrainer artifact_store_path = 'gs://your-bucket-name/optional-subfolder' project = 'PROJECT' # the project to launch the VM in cloudsql_connection_name = f'{project}:REGION:INSTANCE' mysql_db = 'DATABASE' mysql_user = '******' mysql_pw = 'PASSWORD' training_job_dir = artifact_store_path + '/gcaiptrainer/' training_pipeline = TrainingPipeline(name='GCP Orchestrated') # 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': {}}]}} ))
from examples.gan.gan_functions import CycleGANTrainer from examples.gan.preprocessing import GANPreprocessor repo: Repository = Repository().get_instance() gan_pipeline = TrainingPipeline(name="whynotletitfly", enable_cache=False) try: ds = ImageDatasource( name="gan_images", base_path="/Users/nicholasjunge/workspaces/maiot/ce_project/images_mini" ) except: ds = repo.get_datasource_by_name('gan_images') gan_pipeline.add_datasource(ds) gan_pipeline.add_split( CategoricalDomainSplit(categorical_column="label", split_map={ "train": [0], "eval": [1] })) gan_pipeline.add_preprocesser(GANPreprocessor()) # gan_pipeline.add_preprocesser(transform_step) gan_pipeline.add_trainer(CycleGANTrainer(epochs=5)) gan_pipeline.run()