Unit test to create Singularity runner config with docker-based MLCub…

…e. (#331)

runners/mlcube_singularity/mlcube_singularity/tests/resources/docker_mlcube.yaml

@@ -0,0 +1,43 @@
+name: mlcube-name
+description: mlcube-description
+authors:
+  - { name: "First Second" }
+
+platform:
+  accelerator_count: 0
+
+docker:
+  # Image name.
+  image: hasan7/mock-prep-sep:0.0.0
+  # Docker build context relative to $MLCUBE_ROOT.
+  build_context: "../project"
+  # Docker file name within docker build context, default is `Dockerfile`.
+  build_file: "Dockerfile"
+
+tasks:
+  prepare:
+    parameters:
+      inputs:
+        data_path: vids_files/
+        labels_path: labels_files/
+        parameters_file: parameters.yaml
+      outputs:
+        output_path: data/
+        output_labels_path: labels/
+
+  sanity_check:
+    parameters:
+      inputs:
+        data_path: data/
+        labels_path: labels/
+        parameters_file: parameters.yaml
+  statistics:
+    parameters:
+      inputs:
+        data_path: data/
+        labels_path: labels/
+        parameters_file: parameters.yaml
+      outputs:
+        output_path:
+          type: file
+          default: statistics.yaml

runners/mlcube_singularity/mlcube_singularity/tests/test_singularity_runner.py

@@ -58,6 +58,7 @@
     "{IMAGE_DIRECTORY}", _IMAGE_DIRECTORY.as_posix()
 )
 
+
 _sync_workspace_fn: t.Optional[t.Callable] = None
 
 
@@ -141,3 +142,15 @@ def test_mlcube_custom_entrypoints(self):
         SingularityRun(mlcube, task=None).configure()
         SingularityRun(mlcube, task="ls").run()
         SingularityRun(mlcube, task="free").run()
+
+    @unittest.skipUnless(client is not None, reason="No singularity available.")
+    def test_mlcube_no_singularity_section_config(self):
+        mlcube: t.Union[DictConfig, t.Dict] = MLCubeConfig.create_mlcube_config(
+            (Path(__file__).parent / "resources" / "docker_mlcube.yaml").as_posix(),
+            runner_config=Config.DEFAULT,
+            runner_cls=SingularityRun,
+        )
+        self.assertIsInstance(mlcube, DictConfig)
+
+        mlcube = OmegaConf.to_container(mlcube, resolve=True)
+        self.assertIsInstance(mlcube, dict)