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()