[GSoC] Add unit tests for tune API

.github/workflows/test-python.yaml

@@ -21,7 +21,17 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v5
         with:
-          python-version: 3.11
+          python-version: '3.10'
+
+      - name: Install Katib SDK
+        shell: bash
+        run: pip install --prefer-binary -e sdk/python/v1beta1
+
+      - name: Install Training Operator SDK
+        shell: bash
+        run: |
+          pip install git+https://github.com/kubeflow/training-operator.git@v1.8-branch#subdirectory=sdk/python
+          pip install peft==0.3.0 datasets==2.15.0 transformers==4.38.0
 
       - name: Run Python test
         run: make pytest

Makefile

@@ -172,6 +172,7 @@ pytest: prepare-pytest prepare-pytest-testdata
 	pytest ./test/unit/v1beta1/suggestion --ignore=./test/unit/v1beta1/suggestion/test_skopt_service.py
 	pytest ./test/unit/v1beta1/earlystopping
 	pytest ./test/unit/v1beta1/metricscollector
+	pytest ./test/unit/v1beta1/tune-api
 	cp ./pkg/apis/manager/v1beta1/python/api_pb2.py ./sdk/python/v1beta1/kubeflow/katib/katib_api_pb2.py
 	cp ./pkg/apis/manager/v1beta1/python/api_pb2_grpc.py ./sdk/python/v1beta1/kubeflow/katib/katib_api_pb2_grpc.py
 	sed -i "s/api_pb2/kubeflow\.katib\.katib_api_pb2/g" ./sdk/python/v1beta1/kubeflow/katib/katib_api_pb2_grpc.py

sdk/python/v1beta1/kubeflow/katib/api/katib_client.py

@@ -416,6 +416,9 @@ class name in this argument.
 
         # If users choose to use a custom objective function.
         if objective is not None:
+            if not base_image or not parameters:
+                raise ValueError("One of the required parameters is None")
+
             # Add metrics collector to the Katib Experiment.
             # Up to now, we only support parameter `kind`, of which default value
             # is `StdOut`, to specify the kind of metrics collector.
@@ -633,6 +636,8 @@ class name in this argument.
                     model_provider_parameters.model_uri,
                     "--transformer_type",
                     model_provider_parameters.transformer_type.__name__,
+                    "--num_labels",
+                    str(model_provider_parameters.num_labels),
                     "--model_dir",
                     VOLUME_PATH_MODEL,
                     "--dataset_dir",
@@ -643,7 +648,11 @@ class name in this argument.
                     f"'{training_args}'",
                 ],
                 volume_mounts=[STORAGE_INITIALIZER_VOLUME_MOUNT],
-                resources=resources_per_trial.resources_per_worker,
+                resources=(
+                    resources_per_trial.resources_per_worker
+                    if resources_per_trial
+                    else None
+                ),
             )
 
             # Create the worker and the master pod.
@@ -677,7 +686,10 @@ class name in this argument.
                 ),
             )
 
-            if resources_per_trial.num_procs_per_worker:
+            if (
+                resources_per_trial is not None
+                and resources_per_trial.num_procs_per_worker
+            ):
                 pytorchjob.spec.nproc_per_node = str(
                     resources_per_trial.num_procs_per_worker
                 )
@@ -689,7 +701,7 @@ class name in this argument.
                 )
             )
 
-            if resources_per_trial.num_workers > 1:
+            if resources_per_trial is not None and resources_per_trial.num_workers > 1:
                 pytorchjob.spec.pytorch_replica_specs["Worker"] = (
                     training_models.KubeflowOrgV1ReplicaSpec(
                         replicas=resources_per_trial.num_workers - 1,