Release 0.7.4

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.3
+current_version = 0.7.4
 
 [bumpversion:file:pyproject.toml]
 

.copier-answers.yml

@@ -8,7 +8,7 @@ description: Kedro plugin with Kubeflow Pipelines support
 docs_url: https://kedro-kubeflow.readthedocs.io/
 full_name: Kedro Kubeflow Plugin
 github_url: https://github.com/getindata/kedro-kubeflow
-initial_version: 0.7.3
+initial_version: 0.7.4
 keywords:
 - kedro-plugin
 - kubeflow

.github/workflows/test.yml

@@ -40,7 +40,7 @@ jobs:
 
     - name: Test with tox
       run: |
-        pip install tox-pip-version tox-gh-actions
+        pip install tox-pip-version tox-gh-actions "tox<4.0.0"
         tox -v
 
     - name: Store coverage reports

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
 - repo: https://github.com/pycqa/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
   - id: isort
     args: ["--profile", "black", "--line-length=79"]

CHANGELOG.md

@@ -2,6 +2,11 @@
 
 ## [Unreleased]
 
+## [0.7.4] - 2023-02-27
+
+-   Removed field validation from resources configuration field - now it can take any custom parameters such as "nvidia.com/gpu":1
+-   Added support for kedro namespaces for parameters
+
 ## [0.7.3] - 2022-09-23
 
 -   Fixed plugin config provider so it respects environment provided by the user
@@ -167,7 +172,9 @@
 -   Method to schedule runs for most recent version of given pipeline `kedro kubeflow schedule` 
 -   Shortcut to open UI for pipelines using `kedro kubeflow ui` 
 
-[Unreleased]: https://github.com/getindata/kedro-kubeflow/compare/0.7.3...HEAD
+[Unreleased]: https://github.com/getindata/kedro-kubeflow/compare/0.7.4...HEAD
+
+[0.7.4]: https://github.com/getindata/kedro-kubeflow/compare/0.7.3...0.7.4
 
 [0.7.3]: https://github.com/getindata/kedro-kubeflow/compare/0.7.2...0.7.3
 

kedro_kubeflow/__init__.py

@@ -1 +1 @@
-__version__ = "0.7.3"
+__version__ = "0.7.4"

kedro_kubeflow/config.py

@@ -178,9 +178,13 @@ def __getitem__(self, key):
         )
 
 
-class ResourceConfig(BaseModel):
-    cpu: Optional[str]
-    memory: Optional[str]
+class ResourceConfig(dict):
+    def __getitem__(self, key):
+        defaults: dict = super().get("__default__")
+        this: dict = super().get(key, {})
+        updated_defaults = defaults.copy()
+        updated_defaults.update(this)
+        return updated_defaults
 
 
 class TolerationConfig(BaseModel):
@@ -286,9 +290,15 @@ def _create_default_dict_with(
 
     @validator("resources", always=True)
     def _validate_resources(cls, value):
-        return RunConfig._create_default_dict_with(
-            value, ResourceConfig(cpu="500m", memory="1024Mi")
+        default = ResourceConfig(
+            {"__default__": {"cpu": "500m", "memory": "1024Mi"}}
         )
+        if isinstance(value, dict):
+            default.update(value)
+        elif value is not None:
+            logger.error(f"Unknown type for resource config {type(value)}")
+            raise TypeError(f"Unknown type for resource config {type(value)}")
+        return default
 
     @validator("retry_policy", always=True)
     def _validate_retry_policy(cls, value):

kedro_kubeflow/generators/one_pod_pipeline_generator.py

@@ -12,6 +12,7 @@
     customize_op,
     is_local_fs,
     maybe_add_params,
+    merge_namespaced_params_to_dict,
 )
 
 
@@ -26,8 +27,10 @@ def __init__(self, config, project_name, context):
         self.catalog = context.config_loader.get("catalog*")
 
     def generate_pipeline(self, pipeline, image, image_pull_policy):
+        merged_params = merge_namespaced_params_to_dict(self.context.params)
+
         @dsl.pipeline(self.project_name, self.run_config.description)
-        @maybe_add_params(self.context.params)
+        @maybe_add_params(merged_params)
         def convert_kedro_pipeline_to_kfp() -> None:
             dsl.get_pipeline_conf().set_ttl_seconds_after_finished(
                 self.run_config.ttl
@@ -39,13 +42,16 @@ def convert_kedro_pipeline_to_kfp() -> None:
                 self.run_config,
                 self.context,
             ):
-                self._build_kfp_op(pipeline, image, image_pull_policy)
+                self._build_kfp_op(
+                    pipeline, merged_params, image, image_pull_policy
+                )
 
         return convert_kedro_pipeline_to_kfp
 
     def _build_kfp_op(
         self,
         pipeline,
+        params,
         image,
         image_pull_policy,
     ) -> dsl.ContainerOp:
@@ -59,9 +65,7 @@ def _build_kfp_op(
                 f"--pipeline {pipeline} "
                 f"--config config.yaml"
             ),
-            arguments=create_arguments_from_parameters(
-                self.context.params.keys()
-            ),
+            arguments=create_arguments_from_parameters(params.keys()),
             container_kwargs={"env": create_container_environment()},
             file_outputs={
                 output: f"/home/kedro/{self.catalog[output]['filepath']}"

kedro_kubeflow/generators/pod_per_node_pipeline_generator.py

@@ -15,6 +15,7 @@
     customize_op,
     is_local_fs,
     maybe_add_params,
+    merge_namespaced_params_to_dict,
 )
 
 
@@ -36,11 +37,13 @@ def configure_max_cache_staleness(self, kfp_ops):
                 )
 
     def generate_pipeline(self, pipeline, image, image_pull_policy):
+        merged_params = merge_namespaced_params_to_dict(self.context.params)
+
         @dsl.pipeline(
             name=self.project_name,
             description=self.run_config.description,
         )
-        @maybe_add_params(self.context.params)
+        @maybe_add_params(merged_params)
         def convert_kedro_pipeline_to_kfp() -> None:
             """Convert from a Kedro pipeline into a kfp container graph."""
 
@@ -58,7 +61,11 @@ def convert_kedro_pipeline_to_kfp() -> None:
                 self.context,
             ):
                 kfp_ops = self._build_kfp_ops(
-                    pipeline, node_dependencies, image, image_pull_policy
+                    pipeline,
+                    merged_params,
+                    node_dependencies,
+                    image,
+                    image_pull_policy,
                 )
 
                 self.configure_max_cache_staleness(kfp_ops)
@@ -71,6 +78,7 @@ def convert_kedro_pipeline_to_kfp() -> None:
     def _build_kfp_ops(
         self,
         pipeline,
+        params,
         node_dependencies: Dict[Node, Set[Node]],
         image,
         image_pull_policy,
@@ -129,9 +137,7 @@ def _build_kfp_ops(
                         f"--node {node.name} "
                         f"--config config.yaml"
                     ),
-                    arguments=create_arguments_from_parameters(
-                        self.context.params.keys()
-                    ),
+                    arguments=create_arguments_from_parameters(params.keys()),
                     pvolumes=node_volumes,
                     container_kwargs={"env": nodes_env},
                     file_outputs={

kedro_kubeflow/generators/utils.py

@@ -2,7 +2,7 @@
 import itertools
 import json
 import os
-from functools import wraps
+from functools import reduce, wraps
 from inspect import Parameter, signature
 
 import fsspec
@@ -40,6 +40,49 @@ def wrapper(*args, **kwargs):
     return decorator
 
 
+def merge_namespaced_params_to_dict(kedro_parameters):
+    def dict_merge(dct, merge_dct):
+        """Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
+        updating only top-level keys, dict_merge recurses down into dicts nested
+        to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
+        ``dct``.
+        :param dct: dict onto which the merge is executed
+        :param merge_dct: dct merged into dct
+        :return: None
+        """
+        for k, v in merge_dct.items():
+            if (
+                k in dct
+                and isinstance(dct[k], dict)
+                and isinstance(merge_dct[k], dict)
+            ):  # noqa
+                dict_merge(dct[k], merge_dct[k])
+            else:
+                dct[k] = merge_dct[k]
+
+        return dct
+
+    normalized_params = []
+    for key, param in kedro_parameters.items():
+        if "." in key:
+            nested_keys = key.split(".")
+            top_key = nested_keys.pop(0)
+            bottom_key = nested_keys.pop()
+            inner_dict = {bottom_key: param}
+            for nested_key in nested_keys[-1:]:
+                inner_dict = {nested_key: inner_dict}
+
+            inner_dict = {top_key: inner_dict}
+            normalized_params.append(inner_dict)
+        else:
+            normalized_params.append({key: param})
+
+    if normalized_params:
+        return reduce(dict_merge, normalized_params)
+    else:
+        return dict()
+
+
 def create_container_environment():
     env_vars = [
         k8s.V1EnvVar(
@@ -131,7 +174,7 @@ def customize_op(op, image_pull_policy, run_config: RunConfig):
             k8s.V1SecurityContext(run_as_user=run_config.volume.owner)
         )
 
-    resources = run_config.resources[op.name].dict(exclude_none=True)
+    resources = run_config.resources[op.name]
     op.container.resources = k8s.V1ResourceRequirements(
         limits=resources,
         requests=resources,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "kedro-kubeflow"
-version = "0.7.3"
+version = "0.7.4"
 description = "Kedro plugin with Kubeflow Pipelines support"
 readme = "README.md"
 authors = ['Mateusz Pytel <mateusz.pytel@getindata.com>', 'Mariusz Strzelecki <mariusz.strzelecki@getindata.com>']

sonar-project.properties

@@ -6,7 +6,7 @@ sonar.tests=tests/
 sonar.python.coverage.reportPaths=coverage.xml
 sonar.python.version=3.9
 
-sonar.projectVersion=0.7.3
+sonar.projectVersion=0.7.4
 sonar.projectDescription=Kedro plugin with Kubeflow Pipelines support
 sonar.links.homepage=https://kedro-kubeflow.readthedocs.io/
 sonar.links.ci=https://github.com/getindata/kedro-kubeflow/actions

tests/e2e/catalog.yml

@@ -20,64 +20,38 @@ model_input_table:
   filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/03_primary/model_input_table.csv
   layer: primary
 
-data_processing.preprocessed_companies:
+preprocessed_companies:
   type: pandas.CSVDataSet
   filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/02_intermediate/preprocessed_companies.csv
   layer: intermediate
 
-data_processing.preprocessed_shuttles:
+preprocessed_shuttles:
   type: pandas.CSVDataSet
   filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/02_intermediate/preprocessed_shuttles.csv
   layer: intermediate
 
-data_science.active_modelling_pipeline.X_train:
+X_train:
   type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/active_modelling_pipeline/05_model_input/X_train.pickle
+  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/05_model_input/X_train.pickle
   layer: model_input
 
-data_science.active_modelling_pipeline.y_train:
+y_train:
   type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/active_modelling_pipeline/05_model_input/y_train.pickle
+  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/05_model_input/y_train.pickle
   layer: model_input
 
-data_science.active_modelling_pipeline.X_test:
+X_test:
   type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/active_modelling_pipeline/05_model_input/X_test.pickle
+  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/05_model_input/X_test.pickle
   layer: model_input
 
-data_science.active_modelling_pipeline.y_test:
+y_test:
   type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/active_modelling_pipeline/05_model_input/y_test.pickle
+  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/05_model_input/y_test.pickle
   layer: model_input
 
-data_science.active_modelling_pipeline.regressor:
+regressor:
   type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/active_modelling_pipeline/06_models/regressor.pickle
-  versioned: true
-  layer: models
-
-data_science.candidate_modelling_pipeline.X_train:
-  type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/candidate_modelling_pipeline/05_model_input/X_train.pickle
-  layer: model_input
-
-data_science.candidate_modelling_pipeline.y_train:
-  type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/candidate_modelling_pipeline/05_model_input/y_train.pickle
-  layer: model_input
-
-data_science.candidate_modelling_pipeline.X_test:
-  type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/candidate_modelling_pipeline/05_model_input/X_test.pickle
-  layer: model_input
-
-data_science.candidate_modelling_pipeline.y_test:
-  type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/candidate_modelling_pipeline/05_model_input/y_test.pickle
-  layer: model_input
-
-data_science.candidate_modelling_pipeline.regressor:
-  type: pickle.PickleDataSet
-  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/candidate_modelling_pipeline/06_models/regressor.pickle
+  filepath: gs://gid-ml-ops-sandbox-plugin-tests/kube_${run_id}/06_models/regressor.pickle
   versioned: true
   layer: models