[Discussion] How to manage Conda environments

[chyunsu3]

Currently, there are three places where Conda environments are specified:

• Environment files in https://github.com/dmlc/xgboost-devops/tree/main/containers/conda_env. Environments are built as part of the container build and get cached as part of the CI container.
• Dockerfiles, e.g.

  xgboost-devops/containers/dockerfile/Dockerfile.gpu

  Lines 32 to 40 in 6a0c47d

  	mamba create -y -n gpu_test -c ${RAPIDSAI_CONDA_CHANNEL} -c conda-forge -c nvidia \
  	python=$PYTHON_VERSION "cudf=$RAPIDS_VERSION.*" "rmm=$RAPIDS_VERSION.*" cuda-version=$CUDA_SHORT_VER \
  	"nccl>=${NCCL_SHORT_VER}" \
  	dask \
  	distributed \
  	"dask-cuda=$RAPIDS_VERSION.*" "dask-cudf=$RAPIDS_VERSION.*" cupy \
  	numpy pytest pytest-timeout scipy scikit-learn pandas matplotlib wheel \
  	python-kubernetes urllib3 graphviz hypothesis "loky>=3.5.1" \
  	"pyspark>=3.4.0" cloudpickle cuda-python && \

  Environments are built as part of the container build and get cached as part of the CI container.
• Environment files in https://github.com/dmlc/xgboost/tree/master/ops/conda_env. Environments are re-built in every CI run.

So changing a test dependency involves changing code in three places. It would be great if we can gather Conda env specs in a single place.

cc @trivialfis

[trivialfis]

Gathering them to XGBoost main repo seems good. The dependencies are tied to XGB development. For instance, we can't reliably use deps for 3.0 with XGB 2.1, tests are likely to fail. But they are not tied to devop infrastructure.

We can create template of conda files to enable version specifications.