diff --git a/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py b/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py
index fc3a5b2ddae..c52b55ab482 100644
--- a/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py
+++ b/sdk/python/v1beta1/kubeflow/katib/api/katib_client.py
@@ -140,6 +140,7 @@ def tune(
         parameters: Dict[str, Any],
         base_image: str = constants.BASE_IMAGE_TENSORFLOW,
         namespace: Optional[str] = None,
+        env_per_trial: Optional[Union[Dict[str, str], List[Union[client.V1EnvVar, client.V1EnvFromSource]]]] = None,
         algorithm_name: str = "random",
         algorithm_settings: Union[dict, List[models.V1beta1AlgorithmSetting], None] = None,
         objective_metric_name: str = None,
@@ -172,6 +173,12 @@ def tune(
                 objective function.
             base_image: Image to use when executing the objective function.
             namespace: Namespace for the Experiment.
+            env_per_trial: Environment variable(s) to be attached to each trial container. 
+                You can specify a dictionary as a mapping object representing the environment variables. 
+                Otherwise, you can specify a list, in which the element can either be a kubernetes.client.models.V1EnvVar (documented here: 
+                https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1EnvVar.md)
+                or a kubernetes.client.models.V1EnvFromSource (documented here:
+                https://github.com/kubernetes-client/python/blob/master/kubernetes/docs/V1EnvFromSource.md)
             algorithm_name: Search algorithm for the HyperParameter tuning.
             algorithm_settings: Settings for the search algorithm given.
                 For available fields, check this doc: https://www.kubeflow.org/docs/components/katib/experiment/#search-algorithms-in-detail.
@@ -318,6 +325,15 @@ def tune(
                 requests=resources_per_trial,
                 limits=resources_per_trial,
             )
+        
+        if isinstance(env_per_trial, dict):
+            env, env_from = [client.V1EnvVar(name=str(k), value=str(v)) for k, v in env_per_trial.items()] or None, None
+        
+        if env_per_trial:
+            env = [x for x in env_per_trial if isinstance(x, client.V1EnvVar)] or None
+            env_from = [x for x in env_per_trial if isinstance(x, client.V1EnvFromSource)] or None
+        else:
+            env, env_from = None, None
 
         # Create Trial specification.
         trial_spec = client.V1Job(
@@ -336,6 +352,8 @@ def tune(
                                 image=base_image,
                                 command=["bash", "-c"],
                                 args=[exec_script],
+                                env=env,
+                                env_from=env_from,
                                 resources=resources_per_trial,
                             )
                         ],