This line:
```
pip3 install -U --force-reinstall xgboost xgboost_ray lightgbm_ray petastorm
```
also re-installs the dependencies of these packages, and the `--force-reinstall` means we overwrite existing ones. This leads us to re-install the latest ray release, overwriting the wheels to be tested:
```
[INFO] 5/31/2022, 12:12:16 AM: Successfully installed ... ray-1.12.1 ...
[INFO] 5/31/2022, 12:12:17 AM: * Executed RUN pip3 install -U --force-reinstall xgboost xgboost_ray petastorm (ff6ae9f9)
```
Instead, we should use `--no-deps` to avoid re-installing dependencies. Also, the wheels sanity check is moved to after installing additional packages in order to catch these errors earlier.
It fixes the mysterious error when all cluster env build is failing when pip uninstall / pip install is written in 2 lines. The root cause will be fixed later
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.
Xgboost released a new version a few days ago. Due to caching of the Anyscale cluster env, this resulted in the server having an outdated xgboost version while the client has the most recent version causing the test to fail.
Instead, we reinstall xgboost-ray and xgboost in the post build commands so that these dependencies are not being cached in the cluster env.
Quotation marks were needed in Anyscale app configs to avoid install errors when # were used e.g. in URLs.
Since this has been fixed on the Anyscale side, we can get rid of these.
* 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
