Use us-west-2 for application stress tests (#6782)

ci/stress_tests/application_cluster_template.yaml

@@ -33,8 +33,9 @@ idle_timeout_minutes: 5
 # Cloud-provider specific configuration.
 provider:
     type: aws
-    region: us-east-1
-    availability_zone: us-east-1a
+    region: us-west-2
+    availability_zone: us-west-2a
+    cache_stopped_nodes: false
 
 # How Ray will authenticate with newly launched nodes.
 auth:
@@ -50,7 +51,7 @@ auth:
 # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
 head_node:
     InstanceType: <<<HEAD_TYPE>>>
-    ImageId: ami-0757fc5a639fe7666
+    ImageId: ami-07728e9e2742b0662 # Deep Learning AMI (Ubuntu 16.04)
 
     # You can provision additional disk space with a conf as follows
     BlockDeviceMappings:
@@ -66,7 +67,7 @@ head_node:
 # http://boto3.readthedocs.io/en/latest/reference/services/ec2.html#EC2.ServiceResource.create_instances
 worker_nodes:
     InstanceType: <<<WORKER_TYPE>>>
-    ImageId: ami-0757fc5a639fe7666
+    ImageId: ami-07728e9e2742b0662 # Deep Learning AMI (Ubuntu 16.04)
 
     # Run workers on spot by default. Comment this out to use on-demand.
 #    InstanceMarketOptions:
@@ -87,7 +88,7 @@ file_mounts: {
 # List of shell commands to run to set up nodes.
 setup_commands:
     - wget --quiet https://s3-us-west-2.amazonaws.com/ray-wheels/releases/<<<RAY_VERSION>>>/<<<RAY_COMMIT>>>/ray-<<<RAY_VERSION>>>-<<<WHEEL_STR>>>-manylinux1_x86_64.whl
-    - source activate tensorflow_p36 && pip install -U ray-<<<RAY_VERSION>>>-<<<WHEEL_STR>>>-manylinux1_x86_64.whl[rllib]
+    - source activate tensorflow_p36 && pip install -U ray-<<<RAY_VERSION>>>-<<<WHEEL_STR>>>-manylinux1_x86_64.whl
     - source activate tensorflow_p36 && pip install ray[rllib] ray[debug]
     # Consider uncommenting these if you also want to run apt-get commands during setup
     # - sudo pkill -9 apt-get || true
@@ -103,10 +104,10 @@ worker_setup_commands: []
 
 # Command to start ray on the head node. You don't need to change this.
 head_start_ray_commands:
-    - ray stop
+    - source activate tensorflow_p36 && ray stop
     - ulimit -n 65536; source activate tensorflow_p36 && OMP_NUM_THREADS=1 ray start --head --redis-port=6379 --object-manager-port=8076 --autoscaling-config=~/ray_bootstrap_config.yaml
 
 # Command to start ray on worker nodes. You don't need to change this.
 worker_start_ray_commands:
-    - ray stop
+    - source activate tensorflow_p36 && ray stop
     - ulimit -n 65536; source activate tensorflow_p36 && OMP_NUM_THREADS=1 ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076