[docker] Detect CPUs in container correctly (#10507)

Co-authored-by: simon-mo <simon.mo@hey.com>
Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
Co-authored-by: Alex Wu <itswu.alex@gmail.com>

python/ray/serve/examples/doc/quickstart_class.py

@@ -1,6 +1,8 @@
+import ray
 from ray import serve
 import requests
 
+ray.init(num_cpus=8)
 client = serve.start()
 
 

python/ray/serve/examples/doc/quickstart_function.py

@@ -1,6 +1,8 @@
+import ray
 from ray import serve
 import requests
 
+ray.init(num_cpus=8)
 client = serve.start()
 
 

python/ray/serve/examples/doc/snippet_model_composition.py

@@ -3,7 +3,7 @@ import requests
 import ray
 from ray import serve
 
-ray.init(num_cpus=10)
+ray.init(num_cpus=8)
 client = serve.start()
 
 # Our pipeline will be structured as follows:

python/ray/serve/examples/doc/tutorial_batch.py

@@ -28,8 +28,8 @@ def batch_adder_v0(flask_requests: List):
 
 # __doc_define_servable_v0_end__
 
+ray.init(num_cpus=8)
 # __doc_deploy_begin__
-ray.init(num_cpus=10)
 client = serve.start()
 client.create_backend("adder:v0", batch_adder_v0, config={"max_batch_size": 4})
 client.create_endpoint(

python/ray/serve/examples/doc/tutorial_pytorch.py

@@ -1,4 +1,5 @@
 # yapf: disable
+import ray
 # __doc_import_begin__
 from ray import serve
 
@@ -45,6 +46,7 @@ class ImageModel:
 
 # __doc_define_servable_end__
 
+ray.init(num_cpus=8)
 # __doc_deploy_begin__
 client = serve.start()
 client.create_backend("resnet18:v0", ImageModel)

python/ray/serve/examples/doc/tutorial_sklearn.py

@@ -1,4 +1,5 @@
 # yapf: disable
+import ray
 # __doc_import_begin__
 from ray import serve
 
@@ -70,6 +71,7 @@ class BoostingModel:
 
 # __doc_define_servable_end__
 
+ray.init(num_cpus=8)
 # __doc_deploy_begin__
 client = serve.start()
 client.create_backend("lr:v1", BoostingModel)

python/ray/serve/examples/doc/tutorial_tensorflow.py

@@ -1,4 +1,5 @@
 # yapf: disable
+import ray
 # __doc_import_begin__
 from ray import serve
 
@@ -68,6 +69,7 @@ class TFMnistModel:
 
 # __doc_define_servable_end__
 
+ray.init(num_cpus=8)
 # __doc_deploy_begin__
 client = serve.start()
 client.create_backend("tf:v1", TFMnistModel, TRAINED_MODEL_PATH)