[autoscaler] remove worker_default_node_type that is useless. (#13588)

doc/source/cluster/autoscaling.rst

@@ -111,9 +111,6 @@ An example of configuring multiple node types is as follows `(full example) <htt
     # Specify the node type of the head node (as configured above).
     head_node_type: cpu_4_ondemand
 
-    # Specify the default type of the worker node (as configured above).
-    worker_default_node_type: cpu_16_spot
-
 
 The above config defines two CPU node types (``cpu_4_ondemand`` and ``cpu_16_spot``), and two GPU types (``gpu_1_ondemand`` and ``gpu_8_ondemand``). Each node type has a name (e.g., ``cpu_4_ondemand``), which has no semantic meaning and is only for debugging. Let's look at the inner fields of the ``gpu_1_ondemand`` node type:
 

python/ray/autoscaler/_private/util.py

@@ -85,13 +85,6 @@ def validate_config(config: Dict[str, Any]) -> None:
         if config["head_node_type"] not in config["available_node_types"]:
             raise ValueError(
                 "`head_node_type` must be one of `available_node_types`.")
-        if "worker_default_node_type" not in config:
-            raise ValueError("You must specify `worker_default_node_type` if "
-                             "`available_node_types is set.")
-        if (config["worker_default_node_type"] not in config[
-                "available_node_types"]):
-            raise ValueError("`worker_default_node_type` must be one of "
-                             "`available_node_types`.")
 
 
 def prepare_config(config):
@@ -123,7 +116,6 @@ def rewrite_legacy_yaml_to_available_node_types(
             },
         }
         config["head_node_type"] = NODE_TYPE_LEGACY_HEAD
-        config["worker_default_node_type"] = NODE_TYPE_LEGACY_WORKER
 
     return config
 

python/ray/autoscaler/aws/example-multi-node-type.yaml

@@ -55,9 +55,6 @@ available_node_types:
 # Specify the node type of the head node (as configured above).
 head_node_type: cpu_4_ondemand
 
-# Specify the default type of the worker node (as configured above).
-worker_default_node_type: cpu_16_spot
-
 # The default settings for the head node. This will be merged with the per-node
 # type configs given above.
 head_node:

python/ray/autoscaler/kubernetes/operator_configs/cluster_crd.yaml

@@ -25,7 +25,6 @@ spec:
             required:
             - podTypes
             - headPodType
-            - workerDefaultPodType
             properties:
               maxWorkers:
                 description: The maximum number of workers nodes to launch in addition to the
@@ -4264,9 +4263,6 @@ spec:
               headPodType:
                 description: Specifies the head node type.
                 type: string
-              workerDefaultPodType:
-                description: Specifies the default worker node type.
-                type: string 
               headStartRayCommands:
                 description: Commands to start Ray on the head node.
                 type: array

python/ray/autoscaler/kubernetes/operator_configs/example_cluster.yaml

@@ -14,8 +14,6 @@ spec:
   idleTimeoutMinutes: 5
   # Specify the pod type for the ray head node (as configured below).
   headPodType: head-node
-  # Specify the default pod type for ray the worker nodes (as configured below).
-  workerDefaultPodType: worker-nodes
   # Specify the allowed pod types for this ray cluster and the resources they provide.
   podTypes:
   - name: head-node

python/ray/autoscaler/kubernetes/operator_configs/example_cluster2.yaml

@@ -14,8 +14,6 @@ spec:
   idleTimeoutMinutes: 5
   # Specify the pod type for the ray head node (as configured below).
   headPodType: head-node
-  # Specify the default pod type for ray the worker nodes (as configured below).
-  workerDefaultPodType: worker-nodes
   # Specify the allowed pod types for this ray cluster and the resources they provide.
   podTypes:
   - name: head-node

python/ray/autoscaler/ray-schema.json

@@ -254,10 +254,6 @@
             "type": "string",
             "description": "If using multiple node types, specifies the head node type."
         },
-        "worker_default_node_type": {
-            "type": "string",
-            "description": "If using multiple node types, specifies the default worker node type."
-        },
         "head_node": {
             "type": "object",
             "description": "Provider-specific config for the head node, e.g. instance type."

python/ray/autoscaler/staroid/example-multi-node-type.yaml

@@ -103,9 +103,6 @@ available_node_types:
 # Specify the node type of the head node (as configured above).
 head_node_type: cpu_4_ondemand
 
-# Specify the default type of the worker node (as configured above).
-worker_default_node_type: cpu_4_spot
-
 # The default settings for the head node. This will be merged with the per-node
 # type configs given above.
 #head_node:

python/ray/operator/operator_utils.py

@@ -17,7 +17,6 @@ CONFIG_FIELDS = {
     "upscalingSpeed": "upscaling_speed",
     "idleTimeoutMinutes": "idle_timeout_minutes",
     "headPodType": "head_node_type",
-    "workerDefaultPodType": "worker_default_node_type",
     "workerStartRayCommands": "worker_start_ray_commands",
     "headStartRayCommands": "head_start_ray_commands",
     "podTypes": "available_node_types"

python/ray/tests/test_resource_demand_scheduler.py

@@ -87,8 +87,7 @@ TYPES_A = {
 MULTI_WORKER_CLUSTER = dict(
     SMALL_CLUSTER, **{
         "available_node_types": TYPES_A,
-        "head_node_type": "empty_node",
+        "head_node_type": "empty_node"
-        "worker_default_node_type": "m4.large",
     })