diff --git a/.github/workflows/raydp.yml b/.github/workflows/raydp.yml
index e57f51a2..97bb2c72 100644
--- a/.github/workflows/raydp.yml
+++ b/.github/workflows/raydp.yml
@@ -105,8 +105,7 @@ jobs:
       - name: Test with pytest
         run: |
           ray start --head --num-cpus 6
-          pytest python/raydp/tests/ -v -m"not error_on_custom_resource"
-          pytest python/raydp/tests/ -v -m"error_on_custom_resource"
+          pytest python/raydp/tests/ -v
           ray stop --force
       - name: Test Examples
         run: |
diff --git a/core/raydp-main/src/main/java/org/apache/spark/raydp/SparkOnRayConfigs.java b/core/raydp-main/src/main/java/org/apache/spark/raydp/SparkOnRayConfigs.java
index da062f33..d00088c4 100644
--- a/core/raydp-main/src/main/java/org/apache/spark/raydp/SparkOnRayConfigs.java
+++ b/core/raydp-main/src/main/java/org/apache/spark/raydp/SparkOnRayConfigs.java
@@ -1,12 +1,12 @@
 package org.apache.spark.raydp;
 
-import scala.deprecated;
 
 public class SparkOnRayConfigs {
     @Deprecated
     public static final String RAY_ACTOR_RESOURCE_PREFIX = "spark.ray.actor.resource";
 
-    public static final String SPARK_EXECUTOR_ACTOR_RESOURCE_PREFIX = "spark.ray.raydp_spark_executor.actor.resource";
+    public static final String SPARK_EXECUTOR_ACTOR_RESOURCE_PREFIX =
+            "spark.ray.raydp_spark_executor.actor.resource";
     public static final String SPARK_MASTER_ACTOR_RESOURCE_PREFIX =
             "spark.ray.raydp_spark_master.actor.resource";
     /**
@@ -24,7 +24,8 @@ public class SparkOnRayConfigs {
      * This is different from spark.executor.cores, which defines the task parallelism
      * inside a stage.
      */
-    public static final String SPARK_EXECUTOR_ACTOR_CPU_RESOURCE = SPARK_EXECUTOR_ACTOR_RESOURCE_PREFIX + ".cpu";
+    public static final String SPARK_EXECUTOR_ACTOR_CPU_RESOURCE =
+            SPARK_EXECUTOR_ACTOR_RESOURCE_PREFIX + ".cpu";
 
     public static final int DEFAULT_SPARK_CORES_PER_EXECUTOR = 1;
 
diff --git a/doc/spark_on_ray.md b/doc/spark_on_ray.md
index bc39b682..85b1b0f1 100644
--- a/doc/spark_on_ray.md
+++ b/doc/spark_on_ray.md
@@ -32,7 +32,7 @@ available_node_types:
 ### Spark executor actors node affinity
 
 Similar to master actors node affinity, you can also schedule Spark executor to a specific set of nodes
-using custom resource:
+using custom resource, using configuration `spark.ray.raydp_spark_executor.actor.resource.[RESOURCE_NAME]`:
 
 ```python
 import raydp
@@ -51,7 +51,7 @@ And here is the cluster YAML with the customer resource:
 available_node_types:
   spark_on_spot:  # Spark only nodes
     resources:
-      spark_executor: 100 # custom resource indicates these node group is for Spark only
+      spark_executor: 100 # custom resource, with name matches the one set in spark.ray.raydp_spark_executor.actor.resource.*
     min_workers: 2
     max_workers: 10  # changing this also need to change the global max_workers
     node_config:
@@ -76,7 +76,7 @@ spark = raydp.init_spark(app_name='RayDP Oversubscribe Example',
                          executor_memory=1 * 1024 * 1024 * 1024,
                          configs = {
                              # ...
-                             'spark.ray.raydp_spark_executor.actor.resource.spark_executor': 1,  # The actor only occupy 1 logical CPU slots from Ray
+                             'spark.ray.raydp_spark_executor.actor.resource.cpu': 1,  # The actor only occupy 1 logical CPU slots from Ray
                          })
 ```
 
diff --git a/python/raydp/tests/conftest.py b/python/raydp/tests/conftest.py
index 5e6fc356..c54fe587 100644
--- a/python/raydp/tests/conftest.py
+++ b/python/raydp/tests/conftest.py
@@ -78,7 +78,7 @@ def stop_all():
 def spark_on_ray_2_executors(request):
     ray.shutdown()
     if request.param == "local":
-        ray.init(address="local", num_cpus=10, include_dashboard=False)
+        ray.init(address="local", num_cpus=6, include_dashboard=False)
     else:
         ray.init(address=request.param)
     node_ip = ray.util.get_node_ip_address()