chore: import upstream snapshot with attribution

python/ray/autoscaler/gcp/example-gpu-docker.yaml

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+# An unique identifier for the head node and workers of this cluster.
+cluster_name: gpu-docker
+
+# The maximum number of workers nodes to launch in addition to the head
+# node.
+max_workers: 2
+
+# The autoscaler will scale up the cluster faster with higher upscaling speed.
+# E.g., if the task requires adding more nodes then autoscaler will gradually
+# scale up the cluster in chunks of upscaling_speed*currently_running_nodes.
+# This number should be > 0.
+upscaling_speed: 1.0
+
+# This executes all commands on all nodes in the docker container,
+# and opens all the necessary ports to support the Ray cluster.
+# Empty string means disabled.
+docker:
+    image: "rayproject/ray:latest-gpu"
+    # image: rayproject/ray-ml:latest-gpu   # use this one if you need ML dependencies, but it's slower to pull
+    container_name: "ray_nvidia_docker" # e.g. ray_docker
+
+    # # Example of running a GPU head with CPU workers
+    # head_image: "rayproject/ray-ml:latest-gpu"
+
+
+    # worker_image: "rayproject/ray-ml:latest"
+
+# If a node is idle for this many minutes, it will be removed.
+idle_timeout_minutes: 5
+
+# Cloud-provider specific configuration.
+provider:
+    type: gcp
+    region: us-west1
+    availability_zone: us-west1-b
+    project_id: null # Replace this with your globally unique project id
+
+# How Ray will authenticate with newly launched nodes.
+auth:
+    ssh_user: ubuntu
+# By default Ray creates a new private keypair, but you can also use your own.
+# If you do so, make sure to also set "KeyName" in the head and worker node
+# configurations below. This requires that you have added the key into the
+# project wide meta-data.
+#    ssh_private_key: /path/to/your/key.pem
+
+# Tell the autoscaler the allowed node types and the resources they provide.
+# The key is the name of the node type, which is just for debugging purposes.
+# The node config specifies the launch config and physical instance type.
+available_node_types:
+    ray_head_gpu:
+        # The resources provided by this node type.
+        resources: {"CPU": 6, "GPU": 1}
+        # Provider-specific config for the head node, e.g. instance type. By default
+        # Ray will auto-configure unspecified fields such as subnets and ssh-keys.
+        # For more documentation on available fields, see:
+        # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
+        node_config:
+            machineType: n1-standard-2
+            disks:
+              - boot: true
+                autoDelete: true
+                type: PERSISTENT
+                initializeParams:
+                  diskSizeGb: 50
+                  # See https://cloud.google.com/compute/docs/images for more images
+                  sourceImage: projects/ml-images/global/images/c0-deeplearning-common-cu121-v20231209-debian-11
+            # Make sure to set scheduling->onHostMaintenance to TERMINATE when GPUs are present
+            guestAccelerators:
+              - acceleratorType: nvidia-tesla-t4
+                acceleratorCount: 1
+            metadata:
+              items:
+                - key: install-nvidia-driver
+                  value: "True"
+            scheduling:
+              - onHostMaintenance: TERMINATE
+
+    ray_worker_gpu:
+        # The minimum number of nodes of this type to launch.
+        # This number should be >= 0.
+        min_workers: 0
+        # The maximum number of workers nodes of this type to launch.
+        # This takes precedence over min_workers.
+        max_workers: 2
+        # The resources provided by this node type.
+        resources: {"CPU": 2, "GPU": 1}
+        # Provider-specific config for the head node, e.g. instance type. By default
+        # Ray will auto-configure unspecified fields such as subnets and ssh-keys.
+        # For more documentation on available fields, see:
+        # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert
+        node_config:
+            machineType: n1-standard-2
+            disks:
+              - boot: true
+                autoDelete: true
+                type: PERSISTENT
+                initializeParams:
+                  diskSizeGb: 50
+                  # See https://cloud.google.com/compute/docs/images for more images
+                  sourceImage: projects/ml-images/global/images/c0-deeplearning-common-cu121-v20231209-debian-11
+            # Make sure to set scheduling->onHostMaintenance to TERMINATE when GPUs are present
+            guestAccelerators:
+              - acceleratorType: nvidia-tesla-t4
+                acceleratorCount: 1
+            metadata:
+              items:
+                - key: install-nvidia-driver
+                  value: "True"
+            # Run workers on preemtible instance by default.
+            # Comment this out to use on-demand.
+            scheduling:
+              - preemptible: true
+              - onHostMaintenance: TERMINATE
+
+# Specify the node type of the head node (as configured above).
+head_node_type: ray_head_gpu
+
+# Files or directories to copy to the head and worker nodes. The format is a
+# dictionary from REMOTE_PATH: LOCAL_PATH, e.g.
+file_mounts: {
+#    "/path1/on/remote/machine": "/path1/on/local/machine",
+#    "/path2/on/remote/machine": "/path2/on/local/machine",
+}
+
+initialization_commands:
+    # Wait until nvidia drivers are installed
+    - >-
+      timeout 300 bash -c "
+          command -v nvidia-smi && nvidia-smi
+          until [ \$? -eq 0 ]; do
+              command -v nvidia-smi && nvidia-smi
+          done"
+
+# List of shell commands to run to set up nodes.
+# NOTE: rayproject/ray-ml:latest has ray latest bundled
+setup_commands: []
+    # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
+    # - pip install -U "ray[default] @ https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-3.0.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl"
+
+# Custom commands that will be run on the head node after common setup.
+head_setup_commands:
+  - pip install google-api-python-client==1.7.8
+
+# Custom commands that will be run on worker nodes after common setup.
+worker_setup_commands: []
+
+# Command to start ray on the head node. You don't need to change this.
+head_start_ray_commands:
+    - ray stop
+    - >-
+      ulimit -n 65536;
+      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
+    - >-
+      ulimit -n 65536;
+      ray start
+      --address=$RAY_HEAD_IP:6379
+      --object-manager-port=8076