chore: import upstream snapshot with attribution

doc/source/serve/doc_code/managing_deployments.py

@@ -0,0 +1,80 @@
+from ray import serve
+import time
+import os
+
+
+# __updating_a_deployment_start__
+@serve.deployment(name="my_deployment", num_replicas=1)
+class SimpleDeployment:
+    pass
+
+
+# Creates one initial replica.
+serve.run(SimpleDeployment.bind())
+
+
+# Re-deploys, creating an additional replica.
+# This could be the SAME Python script, modified and re-run.
+@serve.deployment(name="my_deployment", num_replicas=2)
+class SimpleDeployment:
+    pass
+
+
+serve.run(SimpleDeployment.bind())
+
+# You can also use Deployment.options() to change options without redefining
+# the class. This is useful for programmatically updating deployments.
+serve.run(SimpleDeployment.options(num_replicas=2).bind())
+# __updating_a_deployment_end__
+
+
+# __scaling_out_start__
+# Create with a single replica.
+@serve.deployment(num_replicas=1)
+def func(*args):
+    pass
+
+
+serve.run(func.bind())
+
+# Scale up to 3 replicas.
+serve.run(func.options(num_replicas=3).bind())
+
+# Scale back down to 1 replica.
+serve.run(func.options(num_replicas=1).bind())
+# __scaling_out_end__
+
+
+# __autoscaling_start__
+@serve.deployment(
+    autoscaling_config={
+        "min_replicas": 1,
+        "initial_replicas": 2,
+        "max_replicas": 5,
+        "target_ongoing_requests": 10,
+    }
+)
+def func(_):
+    time.sleep(1)
+    return ""
+
+
+serve.run(
+    func.bind()
+)  # The func deployment will now autoscale based on requests demand.
+# __autoscaling_end__
+
+
+# __configure_parallism_start__
+@serve.deployment
+class MyDeployment:
+    def __init__(self, parallelism: str):
+        os.environ["OMP_NUM_THREADS"] = parallelism
+        # Download model weights, initialize model, etc.
+
+    def __call__(self):
+        pass
+
+
+serve.run(MyDeployment.bind("12"))
+# __configure_parallism_end__