[GSoC] Add unit tests for tune API

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

@@ -415,7 +415,10 @@ class name in this argument.
             experiment.spec.max_failed_trial_count = max_failed_trial_count
 
         # If users choose to use a custom objective function.
-        if objective is not None:
+        if objective is not None or parameters is not None:
+            if objective is None or base_image is None or parameters is None:
+                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.
@@ -504,9 +507,9 @@ class name in this argument.
         # If users choose to use external models and datasets.
         else:
             if (
-                not model_provider_parameters
-                or not dataset_provider_parameters
-                or not trainer_parameters
+                model_provider_parameters is None
+                or dataset_provider_parameters is None
+                or trainer_parameters is None
             ):
                 raise ValueError("One of the required parameters is None")
 
@@ -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,