def test_get_config_override_profile(): config = DatabricksConfig.from_token("yo", "lo") update_and_persist_config(TEST_PROFILE, config) try: provider = ProfileConfigProvider(TEST_PROFILE) set_config_provider(provider) config = get_config() assert config.host == "yo" assert config.token == "lo" finally: set_config_provider(None)
def test_get_config_override_custom(): class TestConfigProvider(DatabricksConfigProvider): def get_config(self): return DatabricksConfig.from_token("Override", "Token!") try: provider = TestConfigProvider() set_config_provider(provider) config = get_config() assert config.host == "Override" assert config.token == "Token!" finally: set_config_provider(None)
def test_get_config_bad_override(): with pytest.raises(Exception): set_config_provider("NotAConfigProvider")
# MAGIC # MAGIC Now train a binary classifier (using random forests) for predciting the target condition. We use MLFlow for tracking and registering the model, and use hyperopt for distributed hyper parameter tuning. # COMMAND ---------- # DBTITLE 0,Temp fix for mlflow error message through load balancer from databricks_cli.configure.provider import get_config_provider, set_config_provider, DatabricksConfigProvider base_provider = get_config_provider() class DynamicConfigProvider(DatabricksConfigProvider): def get_config(self): base_config = base_provider.get_config() base_config.insecure = True return base_config set_config_provider(DynamicConfigProvider()) import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) # COMMAND ---------- from pyspark.ml.classification import LogisticRegression # Create initial LogisticRegression model lr = LogisticRegression(labelCol="label", featuresCol="features", maxIter=10) # Train model with Training Data lrModel = lr.fit(training_encounters)