def test_to_from_config(equal_datasources):
first_ds = BaseDatasource(name="my_datasource")
config = dict({keys.PipelineKeys.STEPS: {}})
config[keys.PipelineKeys.STEPS][keys.DataSteps.DATA] = {"args": {}}
config[keys.PipelineKeys.DATASOURCE] = first_ds.to_config()
second_ds = BaseDatasource.from_config(config)
assert equal_datasources(first_ds, second_ds, loaded=True)
def test_get_datapoints(repo):
# reload a datasource from a saved config
p_config = random.choice(repo.get_pipeline_file_paths())
cfg = yaml_utils.read_yaml(p_config)
ds: BaseDatasource = BaseDatasource.from_config(
cfg[keys.GlobalKeys.PIPELINE])
csv_df = pd.read_csv(
os.path.join(TEST_ROOT, "test_data", "my_dataframe.csv"))
assert ds.get_datapoints() == len(csv_df.index)
def test_datasource_create(repo):
name = "my_datasource"
first_ds = BaseDatasource(name=name)
assert not first_ds._immutable
# reload a datasource from a saved config
p_config = random.choice(repo.get_pipeline_file_paths())
cfg = yaml_utils.read_yaml(p_config)
second_ds = BaseDatasource.from_config(cfg[keys.GlobalKeys.PIPELINE])
assert second_ds._immutable
def from_config(cls, config: Dict):
"""
Convert from pipeline config to ZenML Pipeline object.
All steps are also populated and configuration set to parameters set
in the config file.
Args:
config: a ZenML config in dict-form (probably loaded from YAML).
"""
# start with artifact store
artifact_store = ArtifactStore(config[keys.GlobalKeys.ARTIFACT_STORE])
# metadata store
metadata_store = ZenMLMetadataStore.from_config(
config=config[keys.GlobalKeys.METADATA_STORE]
)
# orchestration backend
backend = OrchestratorBaseBackend.from_config(
config[keys.GlobalKeys.BACKEND])
# pipeline configuration
p_config = config[keys.GlobalKeys.PIPELINE]
pipeline_name = p_config[keys.PipelineKeys.NAME]
pipeline_source = p_config[keys.PipelineKeys.SOURCE]
# populate steps
steps_dict: Dict = {}
for step_key, step_config in p_config[keys.PipelineKeys.STEPS].items():
steps_dict[step_key] = BaseStep.from_config(step_config)
# datasource
datasource = BaseDatasource.from_config(
config[keys.GlobalKeys.PIPELINE])
# enable cache
enable_cache = p_config[keys.PipelineKeys.ENABLE_CACHE]
class_ = source_utils.load_source_path_class(pipeline_source)
obj = class_(
name=cls.get_name_from_pipeline_name(pipeline_name),
pipeline_name=pipeline_name,
enable_cache=enable_cache,
steps_dict=steps_dict,
backend=backend,
artifact_store=artifact_store,
metadata_store=metadata_store,
datasource=datasource)
obj._immutable = True
logger.debug(f'Pipeline {pipeline_name} loaded and and is immutable.')
return obj
def test_get_one_pipeline(repo):
name = "my_datasource"
first_ds = BaseDatasource(name=name)
with pytest.raises(exceptions.EmptyDatasourceException):
_ = first_ds._get_one_pipeline()
# reload a datasource from a saved config
p_config = random.choice(repo.get_pipeline_file_paths())
cfg = yaml_utils.read_yaml(p_config)
second_ds = BaseDatasource.from_config(cfg[keys.GlobalKeys.PIPELINE])
assert second_ds._get_one_pipeline()
def test_sample_data(repo):
# reload a datasource from a saved config
p_config = random.choice(repo.get_pipeline_file_paths())
cfg = yaml_utils.read_yaml(p_config)
ds: BaseDatasource = BaseDatasource.from_config(
cfg[keys.GlobalKeys.PIPELINE])
sample_df = ds.sample_data()
csv_df = pd.read_csv(
os.path.join(TEST_ROOT, "test_data", "my_dataframe.csv"))
# TODO: This fails for floating point values other than 2.5 in GPA.
# Pandas floating point comp might be too strict
assert sample_df.equals(csv_df)
def test_sample_data():
# reload a datasource from a saved config
p_config = random.choice(repo.get_pipeline_file_paths())
cfg = yaml_utils.read_yaml(p_config)
ds: BaseDatasource = BaseDatasource.from_config(
cfg[keys.GlobalKeys.PIPELINE])
sample_df = ds.sample_data()
csv_df = pd.read_csv(
os.path.join(TEST_ROOT, "test_data", "my_dataframe.csv"))
# TODO: This fails on the test csv because the age gets typed as
# a float in datasource.sample_data() method
assert sample_df.equals(csv_df)
def from_config(cls, config: Dict):
"""
Convert from pipeline config to ZenML Pipeline object.
All steps are also populated and configuration set to parameters set
in the config file.
Args:
config: a ZenML config in dict-form (probably loaded from YAML).
"""
# populate steps
steps_dict: Dict = {}
for step_key, step_config in config[keys.GlobalKeys.STEPS].items():
steps_dict[step_key] = BaseStep.from_config(step_config)
env = config[keys.GlobalKeys.ENV]
pipeline_name = env[keys.EnvironmentKeys.EXPERIMENT_NAME]
name = BasePipeline.get_name_from_pipeline_name(
pipeline_name=pipeline_name)
backends_dict: Dict = {}
for backend_key, backend_config in env[
keys.EnvironmentKeys.BACKENDS].items():
backends_dict[backend_key] = BaseBackend.from_config(
backend_key, backend_config)
artifact_store = ArtifactStore(
env[keys.EnvironmentKeys.ARTIFACT_STORE])
metadata_store = ZenMLMetadataStore.from_config(
config=env[METADATA_KEY])
datasource = BaseDatasource.from_config(config)
from zenml.core.pipelines.pipeline_factory import pipeline_factory
pipeline_type = BasePipeline.get_type_from_pipeline_name(pipeline_name)
class_ = pipeline_factory.get_pipeline_by_type(pipeline_type)
# TODO: [MEDIUM] Perhaps move some of the logic in the init block here
# especially regarding inferring immutability.
return class_(name=name,
pipeline_name=pipeline_name,
enable_cache=env[keys.EnvironmentKeys.ENABLE_CACHE],
steps_dict=steps_dict,
backends_dict=backends_dict,
artifact_store=artifact_store,
metadata_store=metadata_store,
datasource=datasource)
def get_datasources(self) -> List:
"""
Get all datasources in this repo.
Returns: list of datasources used in this repo
"""
from zenml.core.datasources.base_datasource import BaseDatasource
datasources = []
datasources_name = set()
for file_path in self.get_pipeline_file_paths():
c = yaml_utils.read_yaml(file_path)
ds = BaseDatasource.from_config(c[keys.GlobalKeys.PIPELINE])
if ds and ds.name not in datasources_name:
datasources.append(ds)
datasources_name.add(ds.name)
return datasources
def test_get_data_file_paths(repo):
first_ds = BaseDatasource(name="my_datasource")
first_pipeline = BasePipeline(name="my_pipeline")
first_pipeline.add_datasource(first_ds)
# reload a datasource from a saved config
p_config = random.choice(repo.get_pipeline_file_paths())
cfg = yaml_utils.read_yaml(p_config)
second_ds = BaseDatasource.from_config(cfg[keys.GlobalKeys.PIPELINE])
with pytest.raises(AssertionError):
_ = second_ds._get_data_file_paths(first_pipeline)
real_pipeline = second_ds._get_one_pipeline()
paths = second_ds._get_data_file_paths(real_pipeline)
# TODO: Find a better way of asserting TFRecords
assert all(os.path.splitext(p)[-1] == ".gz" for p in paths)