chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# syntax=docker/dockerfile:1.3-labs
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ARG BASE_IMAGE
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ARG FULL_BASE_IMAGE=rayproject/ray:nightly"$BASE_IMAGE"
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FROM "$FULL_BASE_IMAGE"
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COPY python/*requirements.txt \
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python/requirements/ml/*requirements.txt \
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python/requirements/docker/*requirements.txt ./
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COPY docker/ray-ml/install-ml-docker-requirements.sh ./
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RUN sudo chmod +x install-ml-docker-requirements.sh \
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&& ./install-ml-docker-requirements.sh
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# Export installed packages
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RUN $HOME/anaconda3/bin/pip freeze > /home/ray/pip-freeze.txt
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# Make sure tfp is installed correctly and matches tf version.
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RUN python -c "import tensorflow_probability"
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## About
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This image is an extension of the [`rayproject/ray`](https://hub.docker.com/repository/docker/rayproject/ray) image. It includes all extended requirements of `RLlib`, `Serve` and `Tune`. It is a well-provisioned starting point for trying out the Ray ecosystem. [Find the Dockerfile here.](https://github.com/ray-project/ray/blob/master/docker/ray-ml/Dockerfile)
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## Tags
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Images are `tagged` with the format `{Ray version}[-{Python version}][-{Platform}]`. `Ray version` tag can be one of the following:
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| Ray version tag | Description |
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| --------------- | ----------- |
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| `latest` | The most recent Ray release. |
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| `x.y.z` | A specific Ray release, e.g. 2.9.3 |
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| `nightly` | The most recent Ray development build (a recent commit from GitHub `master`) |
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The optional `Python version` tag specifies the Python version in the image. All Python versions supported by Ray are available, e.g. `py39`, `py310` and `py311`. If unspecified, the tag points to an image using `Python 3.9`.
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The optional `Platform` tag specifies the platform where the image is intended for:
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| Platform tag | Description |
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| --------------- | ----------- |
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| `-cpu` | These are based off of an Ubuntu image. |
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| `-cuXX` | These are based off of an NVIDIA CUDA image with the specified CUDA version `xx`. They require the NVIDIA Docker Runtime. |
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| `-gpu` | Aliases to a specific `-cuXX` tagged image. |
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| no tag | Aliases to `-cpu` tagged images for `ray`, and aliases to ``-gpu`` tagged images for `ray-ml`. |
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Examples tags:
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- none: equivalent to `latest`
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- `latest`: equivalent to `latest-py39-gpu`, i.e. image for the most recent Ray release
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- `nightly-py39-cpu`
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- `806c18-py39-cu112`
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The `ray-ml` images are not built for the `arm64` (`aarch64`) architecture.
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## Other Images
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* [`rayproject/ray`](https://hub.docker.com/repository/docker/rayproject/ray) - Ray and all of its dependencies.
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name: "ray-ml-py$PYTHON_VERSION-cpu-base"
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froms: ["cr.ray.io/rayproject/ray-py$PYTHON_VERSION-cpu-base"]
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dockerfile: docker/ray-ml/Dockerfile
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srcs:
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- python/requirements.txt
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- python/requirements/ml/dl-cpu-requirements.txt
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- python/requirements/ml/dl-gpu-requirements.txt
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- python/requirements/ml/core-requirements.txt
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- python/requirements/ml/data-requirements.txt
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- python/requirements/ml/rllib-requirements.txt
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- python/requirements/ml/rllib-test-requirements.txt
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- python/requirements/ml/train-requirements.txt
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- python/requirements/ml/train-test-requirements.txt
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- python/requirements/ml/tune-requirements.txt
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- python/requirements/ml/tune-test-requirements.txt
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- python/requirements/docker/ray-docker-requirements.txt
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- docker/ray-ml/install-ml-docker-requirements.sh
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build_args:
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- FULL_BASE_IMAGE=cr.ray.io/rayproject/ray-py$PYTHON_VERSION-cpu-base
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tags:
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- cr.ray.io/rayproject/ray-ml-py$PYTHON_VERSION-cpu-base
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name: "ray-ml-py$PYTHON_VERSION-cu$CUDA_VERSION-base"
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froms: ["cr.ray.io/rayproject/ray-py$PYTHON_VERSION-cu$CUDA_VERSION-base"]
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dockerfile: docker/ray-ml/Dockerfile
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srcs:
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- python/requirements.txt
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- python/requirements/ml/dl-cpu-requirements.txt
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- python/requirements/ml/dl-gpu-requirements.txt
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- python/requirements/ml/core-requirements.txt
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- python/requirements/ml/data-requirements.txt
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- python/requirements/ml/rllib-requirements.txt
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- python/requirements/ml/rllib-test-requirements.txt
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- python/requirements/ml/train-requirements.txt
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- python/requirements/ml/train-test-requirements.txt
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- python/requirements/ml/tune-requirements.txt
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- python/requirements/ml/tune-test-requirements.txt
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- python/requirements/docker/ray-docker-requirements.txt
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- docker/ray-ml/install-ml-docker-requirements.sh
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build_args:
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- FULL_BASE_IMAGE=cr.ray.io/rayproject/ray-py$PYTHON_VERSION-cu$CUDA_VERSION-base
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tags:
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- cr.ray.io/rayproject/ray-ml-py$PYTHON_VERSION-cu$CUDA_VERSION-base
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+65
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#!/bin/bash
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set -e
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# shellcheck disable=SC2139
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alias pip="$HOME/anaconda3/bin/pip"
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sudo apt-get update \
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&& sudo apt-get install -y gcc \
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cmake \
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libgtk2.0-dev \
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libgl1-mesa-dev \
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libgl1-mesa-glx \
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libosmesa6 \
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libosmesa6-dev \
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libglfw3 \
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patchelf \
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unzip \
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unrar \
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zlib1g-dev
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# Install requirements
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pip --no-cache-dir install -r requirements.txt -c requirements_compiled.txt
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# Install other requirements. Keep pinned requirements bounds as constraints
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pip --no-cache-dir install \
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-c requirements.txt \
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-c requirements_compiled.txt \
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-r dl-cpu-requirements.txt \
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-r core-requirements.txt \
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-r data-requirements.txt \
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-r rllib-requirements.txt \
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-r rllib-test-requirements.txt \
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-r train-requirements.txt \
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-r train-test-requirements.txt \
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-r tune-requirements.txt \
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-r tune-test-requirements.txt \
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-r ray-docker-requirements.txt
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# Remove any device-specific constraints from requirements_compiled.txt.
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# E.g.: torch-scatter==2.1.1+pt20cpu or torchvision==0.15.2+cpu
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# These are replaced with gpu-specific requirements in dl-gpu-requirements.txt.
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# Also remove pandas and cupy-cuda12x pins so cudf-cu12 dependencies can resolve.
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sed "/[0-9]\+cpu/d;/[0-9]\+pt/d;/^pandas==/d;/^cupy-cuda12x==/d" "requirements_compiled.txt" > requirements_compiled_gpu.txt
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# explicitly install (overwrite) pytorch with CUDA support
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pip --no-cache-dir install \
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-c requirements.txt \
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-c requirements_compiled_gpu.txt \
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-r dl-gpu-requirements.txt
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sudo apt-get clean
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# requirements_compiled.txt will be kept.
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sudo rm ./*requirements.txt requirements_compiled_gpu.txt
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# MuJoCo Installation.
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export MUJOCO_GL=osmesa
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wget https://github.com/google-deepmind/mujoco/releases/download/2.1.1/mujoco-2.1.1-linux-x86_64.tar.gz
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mkdir -p ~/.mujoco
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mv mujoco-2.1.1-linux-x86_64.tar.gz ~/.mujoco/.
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cd ~/.mujoco || exit
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tar -xf ~/.mujoco/mujoco-2.1.1-linux-x86_64.tar.gz
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export LD_LIBRARY_PATH=${LD_LIBRARY_PATH:-}:/root/.mujoco/mujoco-2.1.1/bin
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