chore: import upstream snapshot with attribution

doc/source/serve/doc_code/gang_scheduling.py

@@ -0,0 +1,183 @@
+from ray import serve
+
+# __basic_gang_start__
+from ray import serve
+from ray.serve.config import GangSchedulingConfig
+
+
+@serve.deployment(
+    num_replicas=8,
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(gang_size=4),
+)
+class Gang:
+    def __call__(self, request):
+        return "Hello!"
+
+
+app = Gang.bind()
+# __basic_gang_end__
+
+# __gang_context_start__
+@serve.deployment(
+    num_replicas=4,
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(gang_size=2),
+)
+class GangWithContext:
+    def __init__(self):
+        ctx = serve.get_replica_context()
+        gc = ctx.gang_context
+        self.rank = gc.rank
+        self.world_size = gc.world_size
+        self.gang_id = gc.gang_id
+        self.member_ids = gc.member_replica_ids
+
+    def __call__(self, request):
+        return {
+            "gang_id": self.gang_id,
+            "rank": self.rank,
+            "world_size": self.world_size,
+        }
+
+
+gang_context_app = GangWithContext.bind()
+# __gang_context_end__
+
+# __pack_strategy_start__
+from ray import serve
+from ray.serve.config import GangPlacementStrategy, GangSchedulingConfig
+
+
+@serve.deployment(
+    num_replicas=4,
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(
+        gang_size=4,
+        gang_placement_strategy=GangPlacementStrategy.PACK,
+    ),
+)
+class PackedGang:
+    def __call__(self, request):
+        return "Packed on same node"
+
+
+packed_app = PackedGang.bind()
+# __pack_strategy_end__
+
+# __spread_strategy_start__
+@serve.deployment(
+    num_replicas=4,
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(
+        gang_size=2,
+        gang_placement_strategy=GangPlacementStrategy.SPREAD,
+    ),
+)
+class SpreadGang:
+    def __call__(self, request):
+        return "Spread across nodes"
+
+
+spread_app = SpreadGang.bind()
+# __spread_strategy_end__
+
+# __options_start__
+@serve.deployment
+class BaseGang:
+    def __call__(self, request):
+        return "Hello!"
+
+
+app_with_gang = BaseGang.options(
+    num_replicas=8,
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(gang_size=4),
+).bind()
+# __options_end__
+
+# __autoscaling_start__
+@serve.deployment(
+    autoscaling_config={
+        "min_replicas": 4,
+        "max_replicas": 16,
+        "initial_replicas": 8,
+        "target_ongoing_requests": 5,
+    },
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(gang_size=4),
+)
+class AutoscaledGang:
+    def __call__(self, request):
+        return "Hello!"
+
+
+autoscaled_app = AutoscaledGang.bind()
+# __autoscaling_end__
+
+# __fault_tolerance_start__
+from ray import serve
+from ray.serve.config import GangRuntimeFailurePolicy, GangSchedulingConfig
+
+
+@serve.deployment(
+    num_replicas=8,
+    ray_actor_options={"num_cpus": 0.25},
+    gang_scheduling_config=GangSchedulingConfig(
+        gang_size=4,
+        runtime_failure_policy=GangRuntimeFailurePolicy.RESTART_GANG,
+    ),
+)
+class FaultTolerantGang:
+    def __call__(self, request):
+        return "Hello!"
+
+
+fault_tolerant_app = FaultTolerantGang.bind()
+# __fault_tolerance_end__
+
+# __placement_group_bundles_start__
+@serve.deployment(
+    num_replicas=4,
+    ray_actor_options={"num_cpus": 0},
+    placement_group_bundles=[{"CPU": 1, "GPU": 1}],
+    gang_scheduling_config=GangSchedulingConfig(gang_size=2),
+)
+class GangWithSingleBundleReplica:
+    def __call__(self, request):
+        return "Running on reserved GPUs"
+
+
+gang_single_bundle_replica_app = GangWithSingleBundleReplica.bind()
+# __placement_group_bundles_end__
+
+# __multi_placement_group_bundles_start__
+@serve.deployment(
+    num_replicas=4,
+    ray_actor_options={"num_cpus": 1},
+    placement_group_bundles=[{"CPU": 1, "GPU": 1}, {"GPU": 1}],
+    gang_scheduling_config=GangSchedulingConfig(gang_size=2),
+)
+class GangWithMultiBundlesReplica:
+    def __call__(self, request):
+        return "Running on reserved GPUs"
+
+
+gang_multi_bundles_replica_app = GangWithMultiBundlesReplica.bind()
+# __multi_placement_group_bundles_end__
+
+# __label_selector_start__
+@serve.deployment(
+    num_replicas=4,
+    ray_actor_options={"num_cpus": 0},
+    placement_group_bundles=[{"CPU": 1, "GPU": 1}],
+    placement_group_bundle_label_selector=[{"ray.io/accelerator-type": "A100"}],
+    gang_scheduling_config=GangSchedulingConfig(gang_size=2),
+)
+class GangOnA100:
+    def __call__(self, request):
+        return "Running on A100"
+
+
+gang_a100_app = GangOnA100.bind()
+# __label_selector_end__