From a079f46c255a4557ed847fb6b70237db03b90100 Mon Sep 17 00:00:00 2001 From: Ian Rodney Date: Mon, 17 Aug 2020 09:49:28 -0700 Subject: [PATCH] [autoscaler]/[docker] Cleanup YAMLs & Use RAY docker images (#10108) --- doc/source/cluster/cloud.rst | 2 +- python/ray/autoscaler/aws/example-full.yaml | 8 +++----- .../ray/autoscaler/aws/example-gpu-docker.yaml | 12 ++++++------ python/ray/autoscaler/azure/example-full.yaml | 6 +++--- .../autoscaler/azure/example-gpu-docker.yaml | 11 ++++++----- python/ray/autoscaler/azure/example-gpu.yaml | 6 +++--- python/ray/autoscaler/gcp/example-full.yaml | 4 +--- .../ray/autoscaler/gcp/example-gpu-docker.yaml | 18 ++++++------------ python/ray/autoscaler/local/example-full.yaml | 2 +- python/ray/autoscaler/ray-schema.json | 2 +- python/ray/tests/additional_property.yaml | 1 - 11 files changed, 31 insertions(+), 41 deletions(-) diff --git a/doc/source/cluster/cloud.rst b/doc/source/cluster/cloud.rst index 5ff96df54..8fccec6a8 100644 --- a/doc/source/cluster/cloud.rst +++ b/doc/source/cluster/cloud.rst @@ -352,7 +352,7 @@ and opens all the necessary ports to support the Ray cluster. .. code-block:: yaml docker: - image: tensorflow/tensorflow:1.5.0-py3 + image: rayproject/ray:0.8.7 container_name: ray_docker If Docker is not installed, add the following commands to ``initialization_commands`` to install it. diff --git a/python/ray/autoscaler/aws/example-full.yaml b/python/ray/autoscaler/aws/example-full.yaml index 8cb4665c7..345a9ccb6 100644 --- a/python/ray/autoscaler/aws/example-full.yaml +++ b/python/ray/autoscaler/aws/example-full.yaml @@ -23,7 +23,7 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "" # e.g., tensorflow/tensorflow:1.5.0-py3 + image: "" # e.g., rayproject/ray:0.8.7 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. @@ -31,11 +31,11 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "tensorflow/tensorflow:1.13.1-py3" + # head_image: "rayproject/ray:0.8.7-gpu" # head_run_options: # - --runtime=nvidia - # worker_image: "ubuntu:18.04" + # worker_image: "rayproject/ray:0.8.7" # worker_run_options: [] # The autoscaler will scale up the cluster to this target fraction of resource @@ -129,8 +129,6 @@ setup_commands: # has your Ray repo pre-cloned. Then, you can replace the pip installs # below with a git checkout (and possibly a recompile). - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc - # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp27-cp27mu-manylinux1_x86_64.whl - # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp35-cp35m-manylinux1_x86_64.whl - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl # Consider uncommenting these if you also want to run apt-get commands during setup # - sudo pkill -9 apt-get || true diff --git a/python/ray/autoscaler/aws/example-gpu-docker.yaml b/python/ray/autoscaler/aws/example-gpu-docker.yaml index 2634c504a..07653e1ef 100644 --- a/python/ray/autoscaler/aws/example-gpu-docker.yaml +++ b/python/ray/autoscaler/aws/example-gpu-docker.yaml @@ -23,17 +23,17 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "tensorflow/tensorflow:1.12.0-gpu-py3" + image: "rayproject/ray:0.8.7-gpu" container_name: "ray-nvidia-docker-test" # e.g. ray_docker run_options: - --runtime=nvidia # # Example of running a GPU head with CPU workers - # head_image: "tensorflow/tensorflow:1.13.1-gpu-py3" + # head_image: "rayproject/ray:0.8.7-gpu" # head_run_options: # - --runtime=nvidia - # worker_image: "tensorflow/tensorflow:1.13.1-py3" + # worker_image: "rayproject/ray:0.8.7" # worker_run_options: [] # The autoscaler will scale up the cluster to this target fraction of resource @@ -104,10 +104,10 @@ file_mounts: { } # List of shell commands to run to set up nodes. -setup_commands: - # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp27-cp27mu-manylinux1_x86_64.whl - - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp35-cp35m-manylinux1_x86_64.whl +# NOTE: rayproject/ray:0.8.7 has ray 0.8.7 bundled +# setup_commands: # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl + # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl # Custom commands that will be run on the head node after common setup. head_setup_commands: diff --git a/python/ray/autoscaler/azure/example-full.yaml b/python/ray/autoscaler/azure/example-full.yaml index c188d4184..c42e4b476 100644 --- a/python/ray/autoscaler/azure/example-full.yaml +++ b/python/ray/autoscaler/azure/example-full.yaml @@ -23,7 +23,7 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "" # e.g., tensorflow/tensorflow:1.5.0-py3 + image: "" # e.g., rayproject/ray:0.8.7 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. @@ -31,11 +31,11 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "tensorflow/tensorflow:1.13.1-py3" + # head_image: "rayproject/ray:0.8.7-gpu" # head_run_options: # - --runtime=nvidia - # worker_image: "ubuntu:18.04" + # worker_image: "rayproject/ray:0.8.7" # worker_run_options: [] # The autoscaler will scale up the cluster to this target fraction of resource diff --git a/python/ray/autoscaler/azure/example-gpu-docker.yaml b/python/ray/autoscaler/azure/example-gpu-docker.yaml index 7a72d61cf..6ed526b21 100644 --- a/python/ray/autoscaler/azure/example-gpu-docker.yaml +++ b/python/ray/autoscaler/azure/example-gpu-docker.yaml @@ -23,17 +23,17 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "tensorflow/tensorflow:1.13.1-gpu-py3" + image: "rayproject/ray:0.8.7-gpu" container_name: "ray-nvidia-docker-test" # e.g. ray_docker run_options: - --runtime=nvidia # # Example of running a GPU head with CPU workers - # head_image: "tensorflow/tensorflow:1.13.1-gpu-py3" + # head_image: "rayproject/ray:0.8.7-gpu" # head_run_options: # - --runtime=nvidia - # worker_image: "ubuntu:18.04" + # worker_image: "rayproject/ray:0.8.7" # worker_run_options: [] # The autoscaler will scale up the cluster to this target fraction of resource @@ -79,8 +79,9 @@ file_mounts: { } # List of shell commands to run to set up nodes. -setup_commands: - - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl +# NOTE: rayproject/ray:0.8.7 has ray 0.8.7 bundled +# setup_commands: +# - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl # Custom commands that will be run on the head node after common setup. head_setup_commands: diff --git a/python/ray/autoscaler/azure/example-gpu.yaml b/python/ray/autoscaler/azure/example-gpu.yaml index 0c6f85673..2a9d3016f 100644 --- a/python/ray/autoscaler/azure/example-gpu.yaml +++ b/python/ray/autoscaler/azure/example-gpu.yaml @@ -23,7 +23,7 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "" # e.g., tensorflow/tensorflow:1.5.0-py3 + image: "" # e.g., rayproject/ray:0.8.7-gpu 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. @@ -31,11 +31,11 @@ docker: run_options: [] # Extra options to pass into "docker run" # Example of running a GPU head with CPU workers - # head_image: "tensorflow/tensorflow:1.13.1-py3" + # head_image: "rayproject/ray:0.8.7-gpu" # head_run_options: # - --runtime=nvidia - # worker_image: "ubuntu:18.04" + # worker_image: "rayproject/ray:0.8.7" # worker_run_options: [] # The autoscaler will scale up the cluster to this target fraction of resource diff --git a/python/ray/autoscaler/gcp/example-full.yaml b/python/ray/autoscaler/gcp/example-full.yaml index b5da92226..76c1454d1 100644 --- a/python/ray/autoscaler/gcp/example-full.yaml +++ b/python/ray/autoscaler/gcp/example-full.yaml @@ -23,7 +23,7 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "" # e.g., tensorflow/tensorflow:1.5.0-py3 + image: "" # e.g., rayproject/ray:0.8.7 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. @@ -140,8 +140,6 @@ setup_commands: && echo 'export PATH="$HOME/anaconda3/bin:$PATH"' >> ~/.profile # Install ray - # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp27-cp27mu-manylinux1_x86_64.whl - # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp35-cp35m-manylinux1_x86_64.whl - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl diff --git a/python/ray/autoscaler/gcp/example-gpu-docker.yaml b/python/ray/autoscaler/gcp/example-gpu-docker.yaml index 3b63e9745..27227470f 100644 --- a/python/ray/autoscaler/gcp/example-gpu-docker.yaml +++ b/python/ray/autoscaler/gcp/example-gpu-docker.yaml @@ -23,17 +23,17 @@ autoscaling_mode: default # and opens all the necessary ports to support the Ray cluster. # Empty string means disabled. docker: - image: "tensorflow/tensorflow:1.13.1-gpu-py3" + image: "rayproject/ray:0.8.7-gpu" container_name: "ray-nvidia-docker-test" # e.g. ray_docker run_options: - --runtime=nvidia # # Example of running a GPU head with CPU workers - # head_image: "tensorflow/tensorflow:1.13.1-gpu-py3" + # head_image: "rayproject/ray:0.8.7-gpu" # head_run_options: # - --runtime=nvidia - # worker_image: "ubuntu:18.04" + # worker_image: "rayproject/ray:0.8.7" # worker_run_options: [] @@ -133,16 +133,10 @@ initialization_commands: done" # List of shell commands to run to set up nodes. -setup_commands: - # Note: if you're developing Ray, you probably want to create an AMI that - # has your Ray repo pre-cloned. Then, you can replace the pip installs - # below with a git checkout (and possibly a recompile). - # - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc - - # Install ray - # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp27-cp27mu-manylinux1_x86_64.whl - - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp35-cp35m-manylinux1_x86_64.whl +# NOTE: rayproject/ray:0.8.7 has ray 0.8.7 bundled +# setup_commands: # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp36-cp36m-manylinux1_x86_64.whl + # - pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.9.0.dev0-cp37-cp37m-manylinux1_x86_64.whl # Custom commands that will be run on the head node after common setup. head_setup_commands: diff --git a/python/ray/autoscaler/local/example-full.yaml b/python/ray/autoscaler/local/example-full.yaml index fadfd004e..037426e08 100644 --- a/python/ray/autoscaler/local/example-full.yaml +++ b/python/ray/autoscaler/local/example-full.yaml @@ -26,7 +26,7 @@ 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: "" # e.g., tensorflow/tensorflow:1.5.0-py3 + image: "" # e.g., rayproject/ray:0.8.7 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. diff --git a/python/ray/autoscaler/ray-schema.json b/python/ray/autoscaler/ray-schema.json index bb2f10f3b..fe7341031 100644 --- a/python/ray/autoscaler/ray-schema.json +++ b/python/ray/autoscaler/ray-schema.json @@ -188,7 +188,7 @@ "image": { "type": "string", "description": "the docker image name", - "default": "tensorflow/tensorflow:1.5.0-py3" + "default": "rayproject/ray:0.8.7" }, "container_name": { "type": "string", diff --git a/python/ray/tests/additional_property.yaml b/python/ray/tests/additional_property.yaml index c76fb7277..640d534ff 100644 --- a/python/ray/tests/additional_property.yaml +++ b/python/ray/tests/additional_property.yaml @@ -22,7 +22,6 @@ head_node: setup_commands: - error me # - echo 'export PATH="$HOME/anaconda3/envs/tensorflow_p36/bin:$PATH"' >> ~/.bashrc - # - source activate tensorflow_p27 && pip install -U https://s3-us-west-2.amazonaws.com/ray-wheels/latest/ray-0.4.0-cp27-cp27mu-manylinux1_x86_64.whl # # Command to start ray on the head node. You don't need to change this. head_start_ray_commands: