Repository: getindata/kedro-kubeflow (python)

Issue:
Update CONTRIBUTING.md and add MR template to ensure Changelog is up to date.  
Explain what quality checks are in place and how to update changelog, so the documentation stays up to date.  

Pull request:
Add PR template and define code owners for branch protection policy.
Resolves #11 

Patch:
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
new file mode 100644
index 0000000..95045a2
--- /dev/null
+++ b/.github/CODEOWNERS
@@ -0,0 +1,2 @@
+# Define global code owners
+* @empe @szczeles
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 0000000..f5f00af
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,9 @@
+#### Description
+
+`describe the purpose of the change here` 
+
+Resolves `<issue nr here>`
+
+##### PR Checklist
+- [ ] Tests added
+- [ ] [Changelog](CHANGELOG.md) updated 
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9309bc5..4e5963f 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,13 +2,16 @@
 
 ## [Unreleased]
 
+- Support for inter-steps volume: setup (one volume per pipeline run), initial load (the content of `data/` directory from the image and mount to all the steps for artifacts passing.
+- `kubeflow init` command added to generate sample configuration file.
+
 ## [0.1.8] - 2021-01-05
 
 ### Added
 
 -   *Initial release* of kedro-kubeflow plugin
 -   Ability to run an anonymous pipeline once as within a specified experiment `kedro kubeflow run-once`.
--   Ability to upload pipeline `kedro kubeflow upload`.
+-   Ability to upload pipeline `kedro kubeflow upload-pipeline`.
 -   Method to schedule runs for most recent version of given pipeline `kedro kubeflow schedule` 
 -   Shortcut to open UI for pipelines using `kedro kubeflow ui` 
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 8ce2c91..01bae96 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -1,15 +1,24 @@
-# Release workflow
+
+## PR Guidelines
+1. Fork branch from `develop`.
+1. Ensure to provide unit tests for new functionality.
+2. Update documentation accordingly.
+3. Update [changelog](CHANGELOG.md) according to ["Keep a changelog"](https://keepachangelog.com/en/1.0.0/) guidelines.
+4. Squash changes with a single commit as much as possible and ensure verbose PR name.
+5. Open a PR against `develop`
+
+*We reserve the right to take over and modify or abandon PRs that do not match the workflow or are abandoned.* 
+
+## Release workflow
 
 1. Create the release candidate:
-    - Go to the `create-release-candidate action`
+    - Go to the [Prepare release](https://github.com/getindata/kedro-kubeflow/actions?query=workflow%3A%22Prepare+release%22) action.
     - Click "Run workflow"
-    - Enter the part of the version to bump (one of `<major>.<minor>.<patch>`)
+    - Enter the part of the version to bump (one of `<major>.<minor>.<patch>`). Minor (x.**x**.x) is a default. 
 2. If the workflow has run sucessfully:
     - Go to the newly openened PR named `Release candidate `<version>`
-    - Check that changelog and version have been properly updated.
-    - *(If everything is normal, skip this step)* Eventually pull the branch and make changes if necessary
+    - Check that changelog and version have been properly updated. If not pull the branch and apply manual changes if necessary.
     - Merge the PR to master
-3. Checkout the `publish workflow`to see if:
-    - The package has been uploaded on PyPI sucessfully
+3. Checkout the [Publish](https://github.com/getindata/kedro-kubeflow/actions?query=workflow%3APublish) workflow to see if:
+    - The package has been uploaded on PyPI successfully
     - The changes have been merged back to develop
-4. If the pipeline has failed, please raise an issue to correct the CI, and ensure merge on develop "by hand"".
diff --git a/README.md b/README.md
index c8f3c6f..cdedd74 100644
--- a/README.md
+++ b/README.md
@@ -28,9 +28,29 @@ Usage: kedro kubeflow [OPTIONS] COMMAND [ARGS]...
  
  Commands:
    compile          Translates Kedro pipeline into YAML file with Kubeflow pipeline definition
+   init             Initializes configuration for the plugin
    list-pipelines   List deployed pipeline definitions
    run-once         Deploy pipeline as a single run within given experiment.
    schedule         Schedules recurring execution of latest version of the pipeline
    ui               Open Kubeflow Pipelines UI in new browser tab
    upload-pipeline  Uploads pipeline to Kubeflow server
 ```
+
+## Configuration file
+
+`kedro init` generates configuration file for the plugin, but users may want
+to adjust it to the requirements of the environment:
+
+```
+host: http://10.43.77.224
+
+run_config:
+  image: new-kedro-project
+  experiment_name: New Kedro Project
+  run_name: New Kedro Project
+  wait_for_completion: False
+  volume:
+    storageclass: # default
+    size: 1Gi
+    access_modes: [ReadWriteOnce]
+```
diff --git a/kedro_kubeflow/kfpclient.py b/kedro_kubeflow/kfpclient.py
index af84ad4..4d5bd2f 100644
--- a/kedro_kubeflow/kfpclient.py
+++ b/kedro_kubeflow/kfpclient.py
@@ -16,7 +16,8 @@ from kedro.pipeline.node import Node
 
 IAP_CLIENT_ID = "IAP_CLIENT_ID"
 
-WAIT_TIMEOUT = 24*60*60
+WAIT_TIMEOUT = 24 * 60 * 60
+
 
 class KubeflowClient(object):
 
@@ -24,22 +25,33 @@ class KubeflowClient(object):
 
     def __init__(self, config, project_name, context):
         token = self.obtain_id_token()
-        self.host = config['host']
+        self.host = config["host"]
         self.client = Client(self.host, existing_token=token)
         self.project_name = project_name
         self.context = context
         dsl.ContainerOp._DISABLE_REUSABLE_COMPONENT_WARNING = True
+        self.volume_meta = config.get("run_config", {}).get("volume")
 
     def list_pipelines(self):
         pipelines = self.client.list_pipelines(page_size=30).pipelines
-        return tabulate(map(lambda x: [x.name, x.id], pipelines), headers=["Name", "ID"])
+        return tabulate(
+            map(lambda x: [x.name, x.id], pipelines), headers=["Name", "ID"]
+        )
 
-    def run_once(self, pipeline, image, experiment_name, run_name, wait, image_pull_policy="IfNotPresent") -> None:
+    def run_once(
+        self,
+        pipeline,
+        image,
+        experiment_name,
+        run_name,
+        wait,
+        image_pull_policy="IfNotPresent",
+    ) -> None:
         run = self.client.create_run_from_pipeline_func(
             self.generate_pipeline(pipeline, image, image_pull_policy),
             arguments={},
             experiment_name=experiment_name,
-            run_name=run_name
+            run_name=run_name,
         )
 
         if wait:
@@ -55,7 +67,9 @@ class KubeflowClient(object):
         jwt_token = None
 
         if not client_id:
-            self.log.info("No IAP_CLIENT_ID provided, skipping custom IAP authentication")
+            self.log.info(
+                "No IAP_CLIENT_ID provided, skipping custom IAP authentication"
+            )
             return jwt_token
 
         try:
@@ -63,31 +77,76 @@ class KubeflowClient(object):
             jwt_token = id_token.fetch_id_token(Request(), client_id)
             self.log.info("Obtained JWT token for MLFLOW connectivity.")
         except DefaultCredentialsError as ex:
-            self.log.warning(str(ex) + (" Note that this authentication method does not work with default credentials"
-                                   " obtained via 'gcloud auth application-default login' command. Refer to"
-                                   " documentation on how to configure service account locally "
-                                   "(https://cloud.google.com/docs/authentication/production#manually)"))
+            self.log.warning(
+                str(ex)
+                + (
+                    " Note that this authentication method does not work with default credentials"
+                    " obtained via 'gcloud auth application-default login' command. Refer to"
+                    " documentation on how to configure service account locally "
+                    "(https://cloud.google.com/docs/authentication/production#manually)"
+                )
+            )
         except Exception as e:
             self.log.error("Failed to obtain IAP access token. " + str(e))
         finally:
             return jwt_token
 
     def generate_pipeline(self, pipeline, image, image_pull_policy):
-        @dsl.pipeline(name=self.project_name, description="Kubeflow pipeline for Kedro project")
+        @dsl.pipeline(
+            name=self.project_name,
+            description="Kubeflow pipeline for Kedro project",
+        )
         def convert_kedro_pipeline_to_kfp() -> None:
             """Convert from a Kedro pipeline into a kfp container graph."""
 
-            node_dependencies = self.context.pipelines.get(pipeline).node_dependencies
-            kfp_ops = _build_kfp_ops(node_dependencies)
+            node_volumes = (
+                _setup_volumes() if self.volume_meta is not None else {}
+            )
+            node_dependencies = self.context.pipelines.get(
+                pipeline
+            ).node_dependencies
+            kfp_ops = _build_kfp_ops(node_dependencies, node_volumes)
             for node, dependencies in node_dependencies.items():
                 for dependency in dependencies:
                     kfp_ops[node.name].after(kfp_ops[dependency.name])
 
-        def _build_kfp_ops(node_dependencies: Dict[Node, Set[Node]]) -> Dict[str, dsl.ContainerOp]:
+        def _setup_volumes():
+            vop = dsl.VolumeOp(
+                name="data-volume-create",
+                resource_name="data-volume",
+                size=self.volume_meta.get("size", "1Gi"),
+                modes=self.volume_meta.get("access_modes", ["ReadWriteMany"]),
+                storage_class=self.volume_meta.get("storage_class"),
+            )
+            volume_init = dsl.ContainerOp(
+                name="data-volume-init",
+                image=image,
+                command=["sh", "-c"],
+                arguments=[
+                    " ".join(
+                        [
+                            "cp",
+                            "--verbose",
+                            "-r",
+                            "/home/kedro/data/*",
+                            "/home/kedro/datavolume",
+                        ]
+                    )
+                ],
+                pvolumes={"/home/kedro/datavolume": vop.volume},
+            )
+            volume_init.container.set_image_pull_policy(image_pull_policy)
+            return {"/home/kedro/data": volume_init.pvolume}
+
+        def _build_kfp_ops(
+            node_dependencies: Dict[Node, Set[Node]], node_volumes: Dict
+        ) -> Dict[str, dsl.ContainerOp]:
             """Build kfp container graph from Kedro node dependencies. """
             kfp_ops = {}
 
-            env = V1EnvVar(name=IAP_CLIENT_ID, value=os.environ.get(IAP_CLIENT_ID, ""))
+            env = V1EnvVar(
+                name=IAP_CLIENT_ID, value=os.environ.get(IAP_CLIENT_ID, "")
+            )
 
             for node in node_dependencies:
                 name = _clean_name(node.name)
@@ -96,25 +155,33 @@ class KubeflowClient(object):
                     image=image,
                     command=["kedro"],
                     arguments=["run", "--node", node.name],
+                    pvolumes=node_volumes,
                 )
                 kfp_ops[node.name].container.add_env_variable(env)
-                kfp_ops[node.name].container.set_image_pull_policy(image_pull_policy)
+                kfp_ops[node.name].container.set_image_pull_policy(
+                    image_pull_policy
+                )
 
             return kfp_ops
 
         return convert_kedro_pipeline_to_kfp
 
-
-    def compile(self, pipeline, image, output, image_pull_policy='IfNotPresent'):
-        Compiler().compile(self.generate_pipeline(pipeline, image, image_pull_policy), output)
+    def compile(
+        self, pipeline, image, output, image_pull_policy="IfNotPresent"
+    ):
+        Compiler().compile(
+            self.generate_pipeline(pipeline, image, image_pull_policy), output
+        )
         self.log.info("Generated pipeline definition was saved to %s" % output)
 
-    def upload(self, pipeline, image, image_pull_policy='IfNotPresent'):
+    def upload(self, pipeline, image, image_pull_policy="IfNotPresent"):
         pipeline = self.generate_pipeline(pipeline, image, image_pull_policy)
 
         if self._pipeline_exists(self.project_name):
             pipeline_id = self._get_pipeline_id(self.project_name)
-            version_id = self._upload_pipeline_version(pipeline, pipeline_id, self.project_name)
+            version_id = self._upload_pipeline_version(
+                pipeline, pipeline_id, self.project_name
+            )
             self.log.info("New version of pipeline created: %s", version_id)
         else:
             (pipeline_id, version_id) = self._upload_pipeline(
@@ -141,7 +208,11 @@ class KubeflowClient(object):
                 filter=json.dumps(
                     {
                         "predicates": [
-                            {"key": "name", "op": 1, "string_value": pipeline_name}
+                            {
+                                "key": "name",
+                                "op": 1,
+                                "string_value": pipeline_name,
+                            }
                         ]
                     }
                 )
@@ -150,7 +221,9 @@ class KubeflowClient(object):
             .id
         )
 
-    def _upload_pipeline_version(self, pipeline_func, pipeline_id, pipeline_name):
+    def _upload_pipeline_version(
+        self, pipeline_func, pipeline_id, pipeline_name
+    ):
         version_name = f"{_clean_name(pipeline_name)}-{uuid.uuid4()}"[:100]
         with NamedTemporaryFile(suffix=".yaml") as f:
             Compiler().compile(pipeline_func, f.name)
@@ -173,21 +246,18 @@ class KubeflowClient(object):
             )
             self.log.info(f"Existing experiment found: {experiment.id}")
         except:
-            experiment = self.client.create_experiment(
-                experiment_name
-            )
+            experiment = self.client.create_experiment(experiment_name)
             self.log.info(f"New experiment created: {experiment.id}")
 
         return experiment.id
 
-
     def schedule(self, experiment_name, cron_expression):
         experiment_id = self._ensure_experiment_exists(experiment_name)
         pipeline_id = self._get_pipeline_id(self.project_name)
         self._disable_runs(experiment_id, pipeline_id)
         job = self.client.create_recurring_run(
             experiment_id,
-            f'{self.project_name} on {cron_expression}',
+            f"{self.project_name} on {cron_expression}",
             cron_expression=cron_expression,
             pipeline_id=pipeline_id,
         )
@@ -196,7 +266,11 @@ class KubeflowClient(object):
     def _disable_runs(self, experiment_id, pipeline_id):
         runs = self.client.list_recurring_runs(experiment_id=experiment_id)
         if runs.jobs is not None:
-            my_runs = [job for job in runs.jobs if job.pipeline_spec.pipeline_id == pipeline_id]
+            my_runs = [
+                job
+                for job in runs.jobs
+                if job.pipeline_spec.pipeline_id == pipeline_id
+            ]
             for job in my_runs:
                 self.client.jobs.delete_job(job.id)
                 self.log.info(f"Previous schedule deleted {job.id}")
@@ -204,5 +278,3 @@ class KubeflowClient(object):
 
 def _clean_name(name: str) -> str:
     return re.sub(r"[\W_]+", "-", name).strip("-")
-
-
diff --git a/kedro_kubeflow/plugin.py b/kedro_kubeflow/plugin.py
index c77c95b..01d1dac 100644
--- a/kedro_kubeflow/plugin.py
+++ b/kedro_kubeflow/plugin.py
@@ -1,9 +1,11 @@
-import click
+from pathlib import Path
 
+import click
 from semver import VersionInfo
 from kedro import __version__ as kedro_version
 from kedro.config import MissingConfigException
 from .kfpclient import KubeflowClient
+from .utils import strip_margin
 
 KEDRO_VERSION = VersionInfo.parse(kedro_version)
 
@@ -17,7 +19,6 @@ if KEDRO_VERSION.match(">=0.17.0"):
         return metadata.project_name
 else: # <0.17
     from kedro.framework.context import load_context
-    from pathlib import Path
 
     def get_context(metadata, env):
         return load_context(Path.cwd(), env=env)
@@ -41,17 +42,22 @@ def kubeflow_group(ctx, metadata, env):
     """Interact with Kubeflow Pipelines"""
     ctx.ensure_object(dict)
     ctx.obj['kedro_ctx'] = get_context(metadata, env)
+    ctx.obj['project_name'] = get_project_name(metadata, env)
+
+
+def setup_context(ctx):
     ctx.obj['config'] = ctx.obj['kedro_ctx'].config_loader.get(CONFIG_FILE_PATTERN)
     if 'host' not in ctx.obj['config'].keys():
         raise MissingConfigException("No 'host' defined in kubeflow.yml")
 
-    ctx.obj['kfp_client'] = KubeflowClient(ctx.obj['config'], get_project_name(metadata, env), ctx.obj['kedro_ctx'])
+    ctx.obj['kfp_client'] = KubeflowClient(ctx.obj['config'], ctx.obj['project_name'], ctx.obj['kedro_ctx'])
 
 
 @kubeflow_group.command()
 @click.pass_context
 def list_pipelines(ctx):
     """List deployed pipeline definitions"""
+    setup_context(ctx)
     click.echo(ctx.obj['kfp_client'].list_pipelines())
 
 
@@ -65,6 +71,7 @@ def list_pipelines(ctx):
 @click.pass_context
 def run_once(ctx, image: str, pipeline: str, experiment_name: str, run_name: str, wait: bool, image_pull_policy: str):
     """Deploy pipeline as a single run within given experiment. Config can be specified in kubeflow.yml as well."""
+    setup_context(ctx)
     conf = ctx.obj['config']
     run_conf = conf.get("run_config", {})
     image = image if image else run_conf['image']
@@ -80,6 +87,7 @@ def run_once(ctx, image: str, pipeline: str, experiment_name: str, run_name: str
 @click.pass_context
 def ui(ctx) -> None:
     """Open Kubeflow Pipelines UI in new browser tab"""
+    setup_context(ctx)
     import webbrowser
     host = ctx.obj['config']['host']
     webbrowser.open_new_tab(host)
@@ -93,6 +101,7 @@ def ui(ctx) -> None:
 @click.pass_context
 def compile(ctx, image, pipeline, output, image_pull_policy) -> None:
     """Translates Kedro pipeline into YAML file with Kubeflow Pipeline definition"""
+    setup_context(ctx)
     conf = ctx.obj['config']
     run_conf = conf.get("run_config", {})
     image = image if image else run_conf['image']
@@ -106,6 +115,7 @@ def compile(ctx, image, pipeline, output, image_pull_policy) -> None:
 @click.pass_context
 def upload_pipeline(ctx, image, pipeline, image_pull_policy) -> None:
     """Uploads pipeline to Kubeflow server"""
+    setup_context(ctx)
     conf = ctx.obj['config']
     run_conf = conf.get("run_config", {})
     image = image if image else run_conf['image']
@@ -118,9 +128,35 @@ def upload_pipeline(ctx, image, pipeline, image_pull_policy) -> None:
 @click.pass_context
 def schedule(ctx, experiment_name: str, cron_expression: str):
     """Schedules recurring execution of latest version of the pipeline"""
+    setup_context(ctx)
     conf = ctx.obj['config']
     run_conf = conf.get("run_config", {})
     experiment_name = experiment_name if experiment_name else run_conf['experiment_name']
 
     ctx.obj['kfp_client'].schedule(experiment_name, cron_expression)
 
+@kubeflow_group.command()
+@click.argument("kfp_url", type=str)
+@click.option("-x", "--experiment-name", "experiment_name", type=str, help="Name of experiment associated with this run.", default="default")
+@click.pass_context
+def init(ctx, kfp_url: str, experiment_name: str):
+    """Initializes configuration for the plugin"""
+    image = ctx.obj['kedro_ctx'].project_path.name # default from kedro-docker
+    config = f"""
+    |host: {kfp_url}
+    |
+    |run_config:
+    |  image: {image}
+    |  experiment_name: {ctx.obj['project_name']}
+    |  run_name: {ctx.obj['project_name']}
+    |  wait_for_completion: False
+    |  volume:
+    |    storageclass: # default
+    |    #size: 1Gi
+    |    #access_modes: [ReadWriteOnce]
+    """
+    config_path = Path.cwd().joinpath('conf/base/kubeflow.yaml')
+    with open(config_path, 'w') as f:
+        f.write(strip_margin(config))
+
+    click.echo(f"Configuration generated in {config_path}")
diff --git a/kedro_kubeflow/utils.py b/kedro_kubeflow/utils.py
new file mode 100644
index 0000000..57ba283
--- /dev/null
+++ b/kedro_kubeflow/utils.py
@@ -0,0 +1,4 @@
+import re
+
+def strip_margin(text: str) -> str:
+    return re.sub("\n[ \t]*\\|", "\n", text).strip()


Test patch:
diff --git a/tests/test_mod.py b/tests/test_mod.py
index f1b02e0..4b93e76 100644
--- a/tests/test_mod.py
+++ b/tests/test_mod.py
@@ -10,6 +10,7 @@ import kfp
 from google.auth.exceptions import DefaultCredentialsError
 
 from kedro_kubeflow.kfpclient import KubeflowClient
+from kedro_kubeflow.utils import strip_margin
 from kedro.pipeline import Pipeline, node
 
 
@@ -18,10 +19,6 @@ def identity(input1: str):
 
 
 class TestKubeflowClient(unittest.TestCase):
-    @staticmethod
-    def _strip_margin(text: str) -> str:
-        return re.sub("\n[ \t]*\\|", "\n", text).strip()
-
     def create_pipeline(self):
         return Pipeline(
             [
@@ -76,7 +73,7 @@ class TestKubeflowClient(unittest.TestCase):
         |Name      ID
         |--------  ------
         |somename  someid"""
-        self.assertEqual(output, TestKubeflowClient._strip_margin(expected_output))
+        self.assertEqual(output, strip_margin(expected_output))
 
     def test_should_run_pipeline_without_waiting(self):
         # given
@@ -105,6 +102,7 @@ class TestKubeflowClient(unittest.TestCase):
         with kfp.dsl.Pipeline(None) as dsl_pipeline:
             args[0]()
 
+        assert len(dsl_pipeline.ops) == 2
         assert dsl_pipeline.ops["node1"].container.image == "unittest-image"
         assert dsl_pipeline.ops["node1"].container.image_pull_policy == "IfNotPresent"
 
@@ -339,16 +337,104 @@ class TestKubeflowClient(unittest.TestCase):
         self.kfp_client_mock.pipeline_uploads.upload_pipeline.assert_not_called()
         self.kfp_client_mock.pipeline_uploads.upload_pipeline_version.assert_called()
 
+    def test_should_support_inter_steps_volume_with_defauls(self):
+        # given
+        run_mock = unittest.mock.MagicMock()
+        self.kfp_client_mock.create_run_from_pipeline_func.return_value = run_mock
+        self.create_client({"volume": {}})
+
+        # when
+        self.client_under_test.run_once(
+            run_name="unittest",
+            pipeline="pipeline",
+            image="unittest-image",
+            experiment_name="experiment",
+            wait=False,
+        )
+
+        # then
+        self.kfp_client_mock.create_run_from_pipeline_func.assert_called()
+        run_mock.wait_for_run_completion.assert_not_called()
+        args, kwargs = self.kfp_client_mock.create_run_from_pipeline_func.call_args
+        assert kwargs == {
+            "arguments": {},
+            "experiment_name": "experiment",
+            "run_name": "unittest",
+        }
+
+        with kfp.dsl.Pipeline(None) as dsl_pipeline:
+            args[0]()
+
+        assert len(dsl_pipeline.ops) == 4
+        volume_spec = dsl_pipeline.ops["data-volume-create"].k8s_resource.spec
+        assert volume_spec.resources.requests["storage"] == "1Gi"
+        assert volume_spec.access_modes == ["ReadWriteMany"]
+        assert volume_spec.storage_class_name is None
+        volume_init_spec = dsl_pipeline.ops["data-volume-init"].container
+        assert volume_init_spec.image == "unittest-image"
+        assert volume_init_spec.image_pull_policy == "IfNotPresent"
+        assert volume_init_spec.args[0].startswith("cp --verbose -r")
+        for node in ["data-volume-init", "node1", "node2"]:
+            volumes = dsl_pipeline.ops[node].container.volume_mounts
+            assert len(volumes) == 1
+            assert volumes[0].name == "data-volume-create"
+
+    def test_should_support_inter_steps_volume_with_given_spec(self):
+        # given
+        run_mock = unittest.mock.MagicMock()
+        self.kfp_client_mock.create_run_from_pipeline_func.return_value = run_mock
+        self.create_client(
+            {
+                "volume": {
+                    "storage_class": "nfs",
+                    "size": "1Mi",
+                    "access_modes": ["ReadWriteOnce"],
+                }
+            }
+        )
+
+        # when
+        self.client_under_test.run_once(
+            run_name="unittest",
+            pipeline="pipeline",
+            image="unittest-image",
+            experiment_name="experiment",
+            wait=False,
+        )
+
+        # then
+        self.kfp_client_mock.create_run_from_pipeline_func.assert_called()
+        run_mock.wait_for_run_completion.assert_not_called()
+        args, kwargs = self.kfp_client_mock.create_run_from_pipeline_func.call_args
+        assert kwargs == {
+            "arguments": {},
+            "experiment_name": "experiment",
+            "run_name": "unittest",
+        }
+
+        with kfp.dsl.Pipeline(None) as dsl_pipeline:
+            args[0]()
+
+        assert len(dsl_pipeline.ops) == 4
+        volume_spec = dsl_pipeline.ops["data-volume-create"].k8s_resource.spec
+        assert volume_spec.resources.requests["storage"] == "1Mi"
+        assert volume_spec.access_modes == ["ReadWriteOnce"]
+        assert volume_spec.storage_class_name == "nfs"
+
     def tearDown(self):
         os.environ["IAP_CLIENT_ID"] = ""
 
     @patch("kedro_kubeflow.kfpclient.Client")
-    def setUp(self, kfp_client_mock):
+    def create_client(self, config, kfp_client_mock):
         project_name = "my-awesome-project"
         context = type(
             "obj", (object,), {"pipelines": {"pipeline": self.create_pipeline()}},
         )
         self.client_under_test = KubeflowClient(
-            {"host": "http://unittest"}, project_name, context
+            {"host": "http://unittest", "run_config": config}, project_name, context
         )
+        self.client_under_test.client = kfp_client_mock
         self.kfp_client_mock = self.client_under_test.client
+
+    def setUp(self):
+        self.create_client({})