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https://github.com/vale981/ray
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[docker] Default to ray-ml image (#12703)
This commit is contained in:
Ian Rodney
GitHub
parent
6f3aacd087
commit
19542c5eb0
9 changed files with 35 additions and 27 deletions
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@ -19,7 +19,8 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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image: "rayproject/ray-ml:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_container"
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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@ -27,10 +28,10 @@ docker:
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run_options: [] # Extra options to pass into "docker run"
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# Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# Allow Ray to automatically detect GPUs
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# worker_image: "rayproject/ray:latest-cpu"
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# worker_image: "rayproject/ray-ml:latest-cpu"
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# worker_run_options: []
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# If a node is idle for this many minutes, it will be removed.
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@ -19,13 +19,14 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu"
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image: "rayproject/ray-ml:latest-gpu"
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_nvidia_docker" # e.g. ray_docker
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# # Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# worker_image: "rayproject/ray:latest"
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# worker_image: "rayproject/ray-ml:latest"
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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@ -24,7 +24,7 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "" # e.g., rayproject/ray:latest
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image: "" # e.g., rayproject/ray-ml:latest
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container_name: "" # e.g. ray_docker
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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@ -32,9 +32,9 @@ docker:
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run_options: [] # Extra options to pass into "docker run"
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# Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# worker_image: "rayproject/ray:latest"
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# worker_image: "rayproject/ray-ml:latest"
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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@ -19,7 +19,8 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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image: "rayproject/ray-ml:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_container"
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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@ -27,10 +28,10 @@ docker:
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run_options: [] # Extra options to pass into "docker run"
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# Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# Allow Ray to automatically detect GPUs
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# worker_image: "rayproject/ray:latest-cpu"
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# worker_image: "rayproject/ray-ml:latest-cpu"
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# worker_run_options: []
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# If a node is idle for this many minutes, it will be removed.
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@ -19,13 +19,14 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu"
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image: "rayproject/ray-ml:latest-gpu"
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_nvidia_docker" # e.g. ray_docker
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# # Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# worker_image: "rayproject/ray:latest"
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# worker_image: "rayproject/ray-ml:latest"
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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@ -65,7 +66,7 @@ file_mounts: {
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}
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# List of shell commands to run to set up nodes.
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# NOTE: rayproject/ray:latest has ray latest bundled
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# NOTE: rayproject/ray-ml:latest has ray latest bundled
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setup_commands: []
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# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.1.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
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@ -19,7 +19,8 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu"
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image: "rayproject/ray-ml:latest-gpu"
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_docker"
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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@ -27,9 +28,9 @@ docker:
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run_options: [] # Extra options to pass into "docker run"
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# Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# worker_image: "rayproject/ray:latest"
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# worker_image: "rayproject/ray-ml:latest"
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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@ -19,7 +19,8 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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image: "rayproject/ray-ml:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_container"
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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@ -27,10 +28,10 @@ docker:
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run_options: [] # Extra options to pass into "docker run"
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# Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# Allow Ray to automatically detect GPUs
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# worker_image: "rayproject/ray:latest-cpu"
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# worker_image: "rayproject/ray-ml:latest-cpu"
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# worker_run_options: []
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# If a node is idle for this many minutes, it will be removed.
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@ -19,14 +19,15 @@ upscaling_speed: 1.0
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled.
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docker:
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image: "rayproject/ray:latest-gpu"
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image: "rayproject/ray-ml:latest-gpu"
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_nvidia_docker" # e.g. ray_docker
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# # Example of running a GPU head with CPU workers
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# head_image: "rayproject/ray:latest-gpu"
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# head_image: "rayproject/ray-ml:latest-gpu"
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# worker_image: "rayproject/ray:latest"
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# worker_image: "rayproject/ray-ml:latest"
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# If a node is idle for this many minutes, it will be removed.
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idle_timeout_minutes: 5
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@ -117,7 +118,7 @@ initialization_commands:
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done"
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# List of shell commands to run to set up nodes.
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# NOTE: rayproject/ray:latest has ray latest bundled
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# NOTE: rayproject/ray-ml:latest has ray latest bundled
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setup_commands: []
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# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.1.0.dev0-cp36-cp36m-manylinux2014_x86_64.whl
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# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.1.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl
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@ -25,7 +25,8 @@ idle_timeout_minutes: 5
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# and opens all the necessary ports to support the Ray cluster.
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# Empty string means disabled. Assumes Docker is installed.
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docker:
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image: "rayproject/ray:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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image: "rayproject/ray-ml:latest-gpu" # You can change this to latest-cpu if you don't need GPU support and want a faster startup
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# image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull
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container_name: "ray_container"
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# If true, pulls latest version of image. Otherwise, `docker run` will only pull the image
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# if no cached version is present.
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