From 19542c5eb05da0115b667c9b5bcfe191a642e6d4 Mon Sep 17 00:00:00 2001 From: Ian Rodney Date: Wed, 9 Dec 2020 11:49:16 -0800 Subject: [PATCH] [docker] Default to ray-ml image (#12703) --- python/ray/autoscaler/aws/example-full.yaml | 7 ++++--- python/ray/autoscaler/aws/example-gpu-docker.yaml | 7 ++++--- python/ray/autoscaler/aws/example-ml.yaml | 6 +++--- python/ray/autoscaler/azure/example-full.yaml | 7 ++++--- python/ray/autoscaler/azure/example-gpu-docker.yaml | 9 +++++---- python/ray/autoscaler/azure/example-gpu.yaml | 7 ++++--- python/ray/autoscaler/gcp/example-full.yaml | 7 ++++--- python/ray/autoscaler/gcp/example-gpu-docker.yaml | 9 +++++---- python/ray/autoscaler/local/example-full.yaml | 3 ++- 9 files changed, 35 insertions(+), 27 deletions(-) diff --git a/python/ray/autoscaler/aws/example-full.yaml b/python/ray/autoscaler/aws/example-full.yaml index f47a7523f..844919573 100644 --- a/python/ray/autoscaler/aws/example-full.yaml +++ b/python/ray/autoscaler/aws/example-full.yaml @@ -19,7 +19,8 @@ upscaling_speed: 1.0 # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. 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 + 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 + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull 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. @@ -27,10 +28,10 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" # Allow Ray to automatically detect GPUs - # worker_image: "rayproject/ray:latest-cpu" + # worker_image: "rayproject/ray-ml:latest-cpu" # worker_run_options: [] # If a node is idle for this many minutes, it will be removed. diff --git a/python/ray/autoscaler/aws/example-gpu-docker.yaml b/python/ray/autoscaler/aws/example-gpu-docker.yaml index 6916feb9c..5f8986ec6 100644 --- a/python/ray/autoscaler/aws/example-gpu-docker.yaml +++ b/python/ray/autoscaler/aws/example-gpu-docker.yaml @@ -19,13 +19,14 @@ upscaling_speed: 1.0 # 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" + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull container_name: "ray_nvidia_docker" # e.g. ray_docker # # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" - # worker_image: "rayproject/ray:latest" + # worker_image: "rayproject/ray-ml:latest" # If a node is idle for this many minutes, it will be removed. idle_timeout_minutes: 5 diff --git a/python/ray/autoscaler/aws/example-ml.yaml b/python/ray/autoscaler/aws/example-ml.yaml index 8da0baff6..c2564c954 100644 --- a/python/ray/autoscaler/aws/example-ml.yaml +++ b/python/ray/autoscaler/aws/example-ml.yaml @@ -24,7 +24,7 @@ upscaling_speed: 1.0 # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "" # e.g., rayproject/ray:latest + image: "" # e.g., rayproject/ray-ml:latest container_name: "" # e.g. ray_docker # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image # if no cached version is present. @@ -32,9 +32,9 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" - # worker_image: "rayproject/ray:latest" + # worker_image: "rayproject/ray-ml:latest" # If a node is idle for this many minutes, it will be removed. idle_timeout_minutes: 5 diff --git a/python/ray/autoscaler/azure/example-full.yaml b/python/ray/autoscaler/azure/example-full.yaml index 07fa6e495..d2e2873b1 100644 --- a/python/ray/autoscaler/azure/example-full.yaml +++ b/python/ray/autoscaler/azure/example-full.yaml @@ -19,7 +19,8 @@ upscaling_speed: 1.0 # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. 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 + 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 + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull 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. @@ -27,10 +28,10 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" # Allow Ray to automatically detect GPUs - # worker_image: "rayproject/ray:latest-cpu" + # worker_image: "rayproject/ray-ml:latest-cpu" # worker_run_options: [] # If a node is idle for this many minutes, it will be removed. diff --git a/python/ray/autoscaler/azure/example-gpu-docker.yaml b/python/ray/autoscaler/azure/example-gpu-docker.yaml index 11dcece40..1980e6f2e 100644 --- a/python/ray/autoscaler/azure/example-gpu-docker.yaml +++ b/python/ray/autoscaler/azure/example-gpu-docker.yaml @@ -19,13 +19,14 @@ upscaling_speed: 1.0 # 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" + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull container_name: "ray_nvidia_docker" # e.g. ray_docker # # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" - # worker_image: "rayproject/ray:latest" + # worker_image: "rayproject/ray-ml:latest" # If a node is idle for this many minutes, it will be removed. idle_timeout_minutes: 5 @@ -65,7 +66,7 @@ file_mounts: { } # List of shell commands to run to set up nodes. -# NOTE: rayproject/ray:latest has ray latest bundled +# 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-1.1.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl diff --git a/python/ray/autoscaler/azure/example-gpu.yaml b/python/ray/autoscaler/azure/example-gpu.yaml index 52d533b59..2a2554c90 100644 --- a/python/ray/autoscaler/azure/example-gpu.yaml +++ b/python/ray/autoscaler/azure/example-gpu.yaml @@ -19,7 +19,8 @@ upscaling_speed: 1.0 # 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" + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull container_name: "ray_docker" # If true, pulls latest version of image. Otherwise, `docker run` will only pull the image # if no cached version is present. @@ -27,9 +28,9 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" - # worker_image: "rayproject/ray:latest" + # worker_image: "rayproject/ray-ml:latest" # If a node is idle for this many minutes, it will be removed. idle_timeout_minutes: 5 diff --git a/python/ray/autoscaler/gcp/example-full.yaml b/python/ray/autoscaler/gcp/example-full.yaml index 2f66d1dd3..01ccccbe3 100644 --- a/python/ray/autoscaler/gcp/example-full.yaml +++ b/python/ray/autoscaler/gcp/example-full.yaml @@ -19,7 +19,8 @@ upscaling_speed: 1.0 # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. 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 + 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 + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull 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. @@ -27,10 +28,10 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" # Allow Ray to automatically detect GPUs - # worker_image: "rayproject/ray:latest-cpu" + # worker_image: "rayproject/ray-ml:latest-cpu" # worker_run_options: [] # If a node is idle for this many minutes, it will be removed. diff --git a/python/ray/autoscaler/gcp/example-gpu-docker.yaml b/python/ray/autoscaler/gcp/example-gpu-docker.yaml index 6552a8c89..a9c89bc42 100644 --- a/python/ray/autoscaler/gcp/example-gpu-docker.yaml +++ b/python/ray/autoscaler/gcp/example-gpu-docker.yaml @@ -19,14 +19,15 @@ upscaling_speed: 1.0 # 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" + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull container_name: "ray_nvidia_docker" # e.g. ray_docker # # Example of running a GPU head with CPU workers - # head_image: "rayproject/ray:latest-gpu" + # head_image: "rayproject/ray-ml:latest-gpu" - # worker_image: "rayproject/ray:latest" + # worker_image: "rayproject/ray-ml:latest" # If a node is idle for this many minutes, it will be removed. idle_timeout_minutes: 5 @@ -117,7 +118,7 @@ initialization_commands: done" # List of shell commands to run to set up nodes. -# NOTE: rayproject/ray:latest has ray latest bundled +# 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-1.1.0.dev0-cp36-cp36m-manylinux2014_x86_64.whl # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-1.1.0.dev0-cp37-cp37m-manylinux2014_x86_64.whl diff --git a/python/ray/autoscaler/local/example-full.yaml b/python/ray/autoscaler/local/example-full.yaml index ff0d651d0..e9a506773 100644 --- a/python/ray/autoscaler/local/example-full.yaml +++ b/python/ray/autoscaler/local/example-full.yaml @@ -25,7 +25,8 @@ idle_timeout_minutes: 5 # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. Assumes Docker is installed. 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 + 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 + # image: rayproject/ray:latest-gpu # use this one if you don't need ML dependencies, it's faster to pull 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.