ray/release/ml_user_tests/ray-lightning/app_config.yaml

base_image: {{ env["RAY_IMAGE_ML_NIGHTLY_GPU"] | default("anyscale/ray-ml:nightly-py37-gpu") }}

debian_packages:
  - curl

python:
  pip_packages:
    - ray-lightning
    - tblib
    - torch==1.9.0
  conda_packages: []

post_build_cmds:
  # Upgrade the Ray Lightning version, otherwise it will be cached in the Anyscale Docker image.
  - echo {{ env["TIMESTAMP"] }}
  - pip3 install -U --force-reinstall ray-lightning pytorch-lightning lightning-bolts
  - pip3 install --force-reinstall torch==1.9.0
  - pip3 install --force-reinstall torchvision==0.10.0
  - 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") }}
[ci/release] Support running tests with different python versions (#24843) OSS release tests currently run with hardcoded Python 3.7 base. In the future we will want to run tests on different python versions. This PR adds support for a new `python` field in the test configuration. The python field will determine both the base image used in the Buildkite runner docker container (for Ray client compatibility) and the base image for the Anyscale cluster environments. Note that in Buildkite, we will still only wait for the python 3.7 base image before kicking off tests. That is acceptable, as we can assume that most wheels finish in a similar time, so even if we wait for the 3.7 image and kick off a 3.8 test, that runner will wait maybe for 5-10 more minutes. 2022-05-17 17:03:12 +01:00			`base_image: {{ env["RAY_IMAGE_ML_NIGHTLY_GPU"] \| default("anyscale/ray-ml:nightly-py37-gpu") }}`
[Release/Lightning] Add Ray lightning user test (#19812) * wip * wip * add ray lightning test * fix * update * merge and add * fix * fix * rename * autoscale * add tblib * gloo backend * typo * upgrade torch * latest and master 2021-11-01 18:29:48 -07:00
			`debian_packages:`
			`- curl`

			`python:`
			`pip_packages:`
			`- ray-lightning`
[Release] Use nightly Docker images (#20001) * use nightly * switch ml cpu to ray cpu * fix * add pytest * add more pytest * add constraint * add tensorflow * fix merge conflict * add tblib * fix * add back uninstall 2021-11-10 18:00:16 -08:00			`- tblib`
[ci/release/ml] Run ML release tests on staging (#26168) This moves all ML release tests to staging. Signed-off-by: Kai Fricke <kai@anyscale.com> 2022-06-30 13:24:28 -07:00			`- torch==1.9.0`
[Release/Lightning] Add Ray lightning user test (#19812) * wip * wip * add ray lightning test * fix * update * merge and add * fix * fix * rename * autoscale * add tblib * gloo backend * typo * upgrade torch * latest and master 2021-11-01 18:29:48 -07:00			`conda_packages: []`

			`post_build_cmds:`
[Release] Disable caching for `ray_lightning` (#21886) Passing tests: https://buildkite.com/ray-project/periodic-ci/builds/2560#_ Add an echo timestamp to the post build commands of the ray lightning release tests to trigger a cluster env rebuild and get the latest versions of ray lightning. Without this, the cluster env gets cached so an outdated version is installed on the cluster that is different than the one on the driver, resulting in the below failures. Closes #21871 Closes #21863 Also reinstalls the dependencies in the post build commands so old versions are not cached in the Docker images 2022-01-27 17:56:32 -08:00			`# Upgrade the Ray Lightning version, otherwise it will be cached in the Anyscale Docker image.`
			`- echo {{ env["TIMESTAMP"] }}`
[ci/release/ml] Run ML release tests on staging (#26168) This moves all ML release tests to staging. Signed-off-by: Kai Fricke <kai@anyscale.com> 2022-06-30 13:24:28 -07:00			`- pip3 install -U --force-reinstall ray-lightning pytorch-lightning lightning-bolts`
			`- pip3 install --force-reinstall torch==1.9.0`
[ml/release] fix ptl ml user test. (#26365) Between version1 and 2 of [this](https://console.anyscale-staging.com/o/anyscale-internal/configurations/app-config-versions/apt_TsCpJCRjMJDpNFhNgJmyCniS) cluster_env, 1 fails and 2 succeeds. btw, we really should start to think about a systematic approach towards our python dependency story. - between client and server - but more importantly server side, and any conflicts among requirements - how are pip freeze result evolving over time 2022-07-07 11:45:46 -07:00			`- pip3 install --force-reinstall torchvision==0.10.0`
[ci/release/ml] Run ML release tests on staging (#26168) This moves all ML release tests to staging. Signed-off-by: Kai Fricke <kai@anyscale.com> 2022-06-30 13:24:28 -07:00			`- 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") }}`