chore: import upstream snapshot with attribution

doc/source/serve/doc_code/replica_scheduling.py

@@ -0,0 +1,109 @@
+import ray
+
+# __max_replicas_per_node_start__
+from ray import serve
+
+
+@serve.deployment(num_replicas=6, max_replicas_per_node=2, ray_actor_options={"num_cpus": 0.1})
+class MyDeployment:
+    def __call__(self, request):
+        return "Hello!"
+
+
+app = MyDeployment.bind()
+# __max_replicas_per_node_end__
+
+# __placement_group_start__
+from ray import serve
+
+
+@serve.deployment(
+    ray_actor_options={"num_cpus": 0.1},
+    placement_group_bundles=[{"CPU": 0.1}, {"CPU": 0.1}],
+    placement_group_strategy="STRICT_PACK",
+)
+class MultiCPUModel:
+    def __call__(self, request):
+        return "Processed with 2 CPUs"
+
+
+multi_cpu_app = MultiCPUModel.bind()
+# __placement_group_end__
+
+# __placement_group_labels_start__
+@serve.deployment(
+    ray_actor_options={"num_cpus": 0.1},
+    placement_group_bundles=[{"CPU": 0.1, "GPU": 1}],
+    placement_group_bundle_label_selector=[
+        {"ray.io/accelerator-type": "A100"}
+    ]
+)
+def PlacementGroupBundleLabelSelector(request):
+    return "Running in PG on A100"
+
+pg_label_app = PlacementGroupBundleLabelSelector.bind()
+# __placement_group_labels_end__
+
+# __label_selectors_start__
+from ray import serve
+
+
+# Schedule only on nodes with A100 GPUs
+@serve.deployment(ray_actor_options={"label_selector": {"ray.io/accelerator-type": "A100"}})
+class A100Model:
+    def __call__(self, request):
+        return "Running on A100"
+
+
+# Schedule only on nodes with T4 GPUs
+@serve.deployment(ray_actor_options={"label_selector": {"ray.io/accelerator-type": "T4"}})
+class T4Model:
+    def __call__(self, request):
+        return "Running on T4"
+
+
+a100_app = A100Model.bind()
+t4_app = T4Model.bind()
+# __label_selectors_end__
+
+# __fallback_strategy_start__
+@serve.deployment(
+    ray_actor_options={
+        "label_selector": {"zone": "us-west-2a"},
+        "fallback_strategy": [{"label_selector": {"zone": "us-west-2b"}}]
+    }
+)
+class SoftAffinityDeployment:
+    def __call__(self, request):
+        return "Scheduling to a zone with soft constraints!"
+
+soft_affinity_app = SoftAffinityDeployment.bind()
+# __fallback_strategy_end__
+
+# __label_selector_main_start__
+if __name__ == "__main__":
+    # RayCluster with resources to run example tests.
+    ray.init(
+        labels={
+            "ray.io/accelerator-type": "A100",
+            "zone": "us-west-2b",
+        },
+        num_cpus=16,
+        num_gpus=1,
+        resources={"my_custom_resource": 10},
+    )
+
+    serve.run(a100_app, name="a100", route_prefix="/a100")
+# __label_selector_main_end__
+
+    # Run remaining doc code.
+    serve.run(MyDeployment.options(max_replicas_per_node=6).bind(), name="max_replicas", route_prefix="/max_replicas")
+
+    serve.run(multi_cpu_app, name="multi_cpu", route_prefix="/multi_cpu")
+
+    serve.run(pg_label_app, name="pg_label", route_prefix="/pg_label")
+
+    serve.run(soft_affinity_app, name="soft_affinity", route_prefix="/soft_affinity")
+
+    serve.shutdown()
+    ray.shutdown()