diff --git a/release/rllib_tests/autoscaler-cluster.yaml b/release/rllib_tests/autoscaler-cluster.yaml
new file mode 100644
index 0000000000000..9bb68449c6f8d
--- /dev/null
+++ b/release/rllib_tests/autoscaler-cluster.yaml
@@ -0,0 +1,59 @@
+cluster_name: ray-rllib-regression-tests
+
+min_workers: 0
+max_workers: 0
+
+docker:
+    image: anyscale/ray-ml:latest-gpu
+    container_name: ray_container
+    pull_before_run: True
+    run_options: ["--ulimit nofile=1045876"]
+
+# Cloud-provider specific configuration.
+provider:
+    type: aws
+    region: us-west-2
+    availability_zone: us-west-2a
+    cache_stopped_nodes: False
+
+# How Ray will authenticate with newly launched nodes.
+auth:
+    ssh_user: ubuntu
+
+head_node:
+    InstanceType: p3.16xlarge
+    ImageId: ami-0a2363a9cff180a64 # Custom ami
+    # Set primary volume to 25 GiB
+    BlockDeviceMappings:
+        - DeviceName: /dev/sda1
+          Ebs:
+              VolumeSize: 100
+
+worker_nodes:
+    InstanceType: p3.16xlarge
+    ImageId: ami-0a2363a9cff180a64 # Custom ami
+    # Set primary volume to 25 GiB
+    BlockDeviceMappings:
+        - DeviceName: /dev/sda1
+          Ebs:
+              VolumeSize: 100
+
+# List of shell commands to run to set up nodes.
+setup_commands:
+    - apt-get install -y libglib2.0-0 libcudnn7=7.6.5.32-1+cuda10.1
+    - pip install terminado
+    - pip install torch==1.6 torchvision
+    - pip install boto3==1.4.8 cython==0.29.0
+    - "pip install https://s3-us-west-2.amazonaws.com/ray-wheels/releases/1.1.0/f591f6c1c8fa14af6df2adfdcf3255c59dff12b1/ray-1.1.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
+    - git clone https://github.com/ray-project/ray.git ray-cp
+    - pip install -r ./ray-cp/release/rllib_tests/regression_tests/requirements.txt
+
+# Command to start ray on the head node. You don't need to change this.
+head_start_ray_commands:
+    - ray stop
+    - OMP_NUM_THREADS=1 ray start --head --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
+    - OMP_NUM_THREADS=1 ray start --address=$RAY_HEAD_IP:6379 --object-manager-port=8076
diff --git a/release/stress_tests/autoscaler-cluster.yaml b/release/stress_tests/autoscaler-cluster.yaml
index a75b04fae5a63..b51253689d515 100644
--- a/release/stress_tests/autoscaler-cluster.yaml
+++ b/release/stress_tests/autoscaler-cluster.yaml
@@ -3,7 +3,7 @@
 ####################################################################
 
 # An unique identifier for the head node and workers of this cluster.
-cluster_name: autoscaler-stress-test-1.1.0-alex
+cluster_name: autoscaler-stress-test
 
 # The minimum number of workers nodes to launch in addition to the head
 # node. This number should be >= 0.
@@ -54,6 +54,15 @@ head_node:
 
     # Additional options in the boto docs.
 
+docker:
+    image: "rayproject/ray:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
+    container_name: "ray_container"
+    # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
+    # if no cached version is present.
+    pull_before_run: True
+    run_options: ["--ulimit nofile=1045876"]  # Extra options to pass into "docker run"
+
+
 # Provider-specific config for worker nodes, e.g. instance type. By default
 # Ray will auto-configure unspecified fields such as SubnetId and KeyName.
 # For more documentation on available fields, see: