chore: import upstream snapshot with attribution

doc/source/ray-core/doc_code/resources.py

@@ -0,0 +1,59 @@
+import ray
+
+# __specifying_node_resources_start__
+# This will start a Ray node with 3 logical cpus, 4 logical gpus,
+# 1 special_hardware resource and 1 custom_label resource.
+ray.init(num_cpus=3, num_gpus=4, resources={"special_hardware": 1, "custom_label": 1})
+# __specifying_node_resources_end__
+
+
+# __specifying_resource_requirements_start__
+# Specify the default resource requirements for this remote function.
+@ray.remote(num_cpus=2, num_gpus=2, resources={"special_hardware": 1})
+def func():
+    return 1
+
+
+# You can override the default resource requirements.
+func.options(num_cpus=3, num_gpus=1, resources={"special_hardware": 0}).remote()
+
+
+@ray.remote(num_cpus=0, num_gpus=1)
+class Actor:
+    pass
+
+
+# You can override the default resource requirements for actors as well.
+actor = Actor.options(num_cpus=1, num_gpus=0).remote()
+# __specifying_resource_requirements_end__
+
+
+# __specifying_fractional_resource_requirements_start__
+@ray.remote(num_cpus=0.5)
+def io_bound_task():
+    import time
+
+    time.sleep(1)
+    return 2
+
+
+io_bound_task.remote()
+
+
+@ray.remote(num_gpus=0.5)
+class IOActor:
+    def ping(self):
+        import os
+
+        print(f"CUDA_VISIBLE_DEVICES: {os.environ['CUDA_VISIBLE_DEVICES']}")
+
+
+# Two actors can share the same GPU.
+io_actor1 = IOActor.remote()
+io_actor2 = IOActor.remote()
+ray.get(io_actor1.ping.remote())
+ray.get(io_actor2.ping.remote())
+# Output:
+# (IOActor pid=96328) CUDA_VISIBLE_DEVICES: 1
+# (IOActor pid=96329) CUDA_VISIBLE_DEVICES: 1
+# __specifying_fractional_resource_requirements_end__