fixed prev error Dependabot/pip/kedro 0.19.6

.github/actions/e2e_test/action.yml

@@ -48,7 +48,7 @@ runs:
           shell: bash
           run: |
               pip install $(find "./dist" -name "*.tar.gz")
-              kedro new --starter spaceflights --config tests/conf/${{ inputs.e2e_config }}/starter-config.yml --verbose
+              kedro new --starter spaceflights-pandas --config tests/conf/${{ inputs.e2e_config }}/starter-config.yml --verbose
 
         - name: Install starter requirements
           shell: bash

kedro_azureml/auth/utils.py

@@ -8,10 +8,11 @@ def get_azureml_credentials():
         # On a AzureML compute instance, the managed identity will take precedence,
         # while it does not have enough permissions.
         # So, if we are on an AzureML compute instance, we disable the managed identity.
-        is_azureml_managed_identity = "MSI_ENDPOINT" in os.environ
-        credential = DefaultAzureCredential(
-            exclude_managed_identity_credential=is_azureml_managed_identity
-        )
+        # is_azureml_managed_identity = "MSI_ENDPOINT" in os.environ
+        # credential = DefaultAzureCredential(
+        #     exclude_managed_identity_credential=is_azureml_managed_identity
+        # )
+        credential = DefaultAzureCredential()
         # Check if given credential can get token successfully.
         credential.get_token("https://management.azure.com/.default")
     except Exception:

kedro_azureml/client.py

@@ -52,8 +52,7 @@ def run(
             ), f"Cluster {config.compute['__default__'].cluster_name} does not exist"
 
             logger.info(
-                f"Creating job on cluster {cluster.name} ({cluster.size}, min instances: {cluster.min_instances}, "
-                f"max instances: {cluster.max_instances})"
+                f"Creating job on cluster {cluster.name} ({cluster.size})"
             )
 
             pipeline_job = ml_client.jobs.create_or_update(

kedro_azureml/hooks.py

@@ -18,7 +18,9 @@ def after_context_created(self, context) -> None:
 
     @hook_impl
     def after_catalog_created(self, catalog):
-        for dataset_name, dataset in catalog._data_sets.items():
+        print(dir(catalog))
+        print(catalog.__dict__)
+        for dataset_name, dataset in catalog._datasets.items():
             if isinstance(dataset, AzureMLAssetDataset):
                 dataset.azure_config = self.azure_config
                 catalog.add(dataset_name, dataset, replace=True)
@@ -31,7 +33,8 @@ def before_pipeline_run(self, run_params, pipeline, catalog):
             pipeline: The ``Pipeline`` object representing the pipeline to be run.
             catalog: The ``DataCatalog`` from which to fetch data.
         """
-        for dataset_name, dataset in catalog._data_sets.items():
+        print(catalog)
+        for dataset_name, dataset in catalog._datasets.items():
             if isinstance(dataset, AzureMLAssetDataset):
                 if AzurePipelinesRunner.__name__ not in run_params["runner"]:
                     # when running locally using an AzureMLAssetDataset

kedro_azureml/manager.py

@@ -4,7 +4,7 @@
 
 from kedro.config import (
     AbstractConfigLoader,
-    ConfigLoader,
+    OmegaConfigLoader,
     MissingConfigException,
 )
 from kedro.framework.session import KedroSession
@@ -54,7 +54,7 @@ def plugin_config(self) -> KedroAzureMLConfig:
                 obj = None
 
         if obj is None:
-            if not isinstance(cl, ConfigLoader):
+            if not isinstance(cl, OmegaConfigLoader):
                 raise ValueError(
                     f"You're using a custom config loader: {cl.__class__.__qualname__}{os.linesep}"
                     f"you need to add the azureml config to it.{os.linesep}"
@@ -74,8 +74,11 @@ def plugin_config(self) -> KedroAzureMLConfig:
         return KedroAzureMLConfig.parse_obj(obj)
 
     def __enter__(self):
+        # self.session = KedroSession.create(
+        #     self.package_name, env=self.env, extra_params=self.extra_params
+        # )
         self.session = KedroSession.create(
-            self.package_name, env=self.env, extra_params=self.extra_params
+            project_path=None, env=self.env, extra_params=self.extra_params
         )
         return self
 

kedro_azureml/runner.py

@@ -84,7 +84,7 @@ def create_default_data_set(self, ds_name: str) -> AbstractDataset:
             )
         else:
             # TODO: handle credentials better (probably with built-in Kedro credentials
-            #  via ConfigLoader (but it's not available here...)
+            #  via OmegaConfigLoader (but it's not available here...)
             dataset_cls = KedroAzureRunnerDataset
             if is_distributed_environment():
                 logger.info("Using distributed dataset class as a default")