ray/release/rllib_tests/app_config.yaml

base_image: "anyscale/ray-ml:pinned-nightly-py37-gpu"
env_vars: {}
debian_packages:
  - unzip
  - zip

python:
  # These dependencies should be handled by requirements_rllib.txt and
  # requirements_ml_docker.txt
  pip_packages:
    - torch==1.9.0 # TODO(amogkam): Remove after nightly images are available.
  conda_packages: []

post_build_cmds:
  # Create a couple of soft links so tf 2.4.3 works with cuda 11.2.
  # TODO(jungong): remove these once product ray-ml docker gets upgraded to use tf 2.5.0.
  - sudo ln -s /usr/local/cuda /usr/local/nvidia
  - sudo ln -s /usr/local/cuda/lib64/libcusolver.so.11 /usr/local/cuda/lib64/libcusolver.so.10
  - pip install tensorflow==2.5.0
  # END: TODO

  - pip uninstall -y ray || true
  - pip3 install -U {{ env["RAY_WHEELS"] | default("ray") }}
  - {{ env["RAY_WHEELS_SANITY_CHECK"] | default("echo No Ray wheels sanity check") }}
  # Clone the rl-experiments repo for offline-RL files.
  - git clone https://github.com/ray-project/rl-experiments.git
  - cp rl-experiments/halfcheetah-sac/2021-09-06/halfcheetah_expert_sac.zip ~/.