Pin dask and distributed for 23.10 release (#5592)

This PR pins `dask` and `distributed` to `2023.9.2` for `23.10` release.


xref: rapidsai/cudf#14225

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Ray Douglass (https://github.com/raydouglass)
  - Dante Gama Dessavre (https://github.com/dantegd)
  - Peter Andreas Entschev (https://github.com/pentschev)

URL: #5592

ci/test_wheel.sh

@@ -13,7 +13,7 @@ if [[ "$(arch)" == "aarch64" ]]; then
 fi
 
 # Always install latest dask for testing
-python -m pip install git+https://github.com/dask/dask.git@main git+https://github.com/dask/distributed.git@main git+https://github.com/rapidsai/[email protected]
+python -m pip install git+https://github.com/dask/dask.git@2023.9.2 git+https://github.com/dask/distributed.git@2023.9.2 git+https://github.com/rapidsai/[email protected]
 
 # echo to expand wildcard before adding `[extra]` requires for pip
 python -m pip install $(echo ./dist/cuml*.whl)[test]

conda/environments/all_cuda-118_arch-x86_64.yaml

@@ -16,12 +16,12 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
-- dask-core>=2023.7.1
+- dask-core==2023.9.2
 - dask-cuda==23.10.*
 - dask-cudf==23.10.*
 - dask-ml
-- dask>=2023.7.1
-- distributed>=2023.7.1
+- dask==2023.9.2
+- distributed==2023.9.2
 - doxygen=1.9.1
 - gcc_linux-64=11.*
 - gmock>=1.13.0

conda/environments/all_cuda-120_arch-x86_64.yaml

@@ -18,12 +18,12 @@ dependencies:
 - cupy>=12.0.0
 - cxx-compiler
 - cython>=3.0.0
-- dask-core>=2023.7.1
+- dask-core==2023.9.2
 - dask-cuda==23.10.*
 - dask-cudf==23.10.*
 - dask-ml
-- dask>=2023.7.1
-- distributed>=2023.7.1
+- dask==2023.9.2
+- distributed==2023.9.2
 - doxygen=1.9.1
 - gcc_linux-64=11.*
 - gmock>=1.13.0

conda/recipes/cuml/meta.yaml

@@ -76,9 +76,9 @@ requirements:
     - cudf ={{ minor_version }}
     - cupy >=12.0.0
     - dask-cudf ={{ minor_version }}
-    - dask >=2023.7.1
-    - dask-core>=2023.7.1
-    - distributed >=2023.7.1
+    - dask ==2023.9.2
+    - dask-core==2023.9.2
+    - distributed ==2023.9.2
     - joblib >=0.11
     - libcuml ={{ version }}
     - libcumlprims ={{ minor_version }}

dependencies.yaml

@@ -175,10 +175,10 @@ dependencies:
       - output_types: [conda, requirements, pyproject]
         packages:
           - cudf==23.10.*
-          - dask>=2023.7.1
+          - dask==2023.9.2
           - dask-cuda==23.10.*
           - dask-cudf==23.10.*
-          - distributed>=2023.7.1
+          - distributed==2023.9.2
           - joblib>=0.11
           - numba>=0.57
             # TODO: Is scipy really a hard dependency, or should
@@ -192,7 +192,7 @@ dependencies:
           - cupy>=12.0.0
       - output_types: conda
         packages:
-          - dask-core>=2023.7.1
+          - dask-core==2023.9.2
       - output_types: pyproject
         packages:
           - *treelite_runtime

python/README.md

@@ -70,8 +70,8 @@ Packages required for multigpu algorithms*:
 - ucx-py version matching the cuML version
 - dask-cudf version matching the cuML version
 - nccl>=2.5
-- dask>=2023.7.1
-- distributed>=2023.7.1
+- dask==2023.9.2
+- distributed==2023.9.2
 
 * this can be avoided with `--singlegpu` argument flag.
 

python/pyproject.toml

@@ -61,8 +61,8 @@ dependencies = [
     "cupy-cuda11x>=12.0.0",
     "dask-cuda==23.10.*",
     "dask-cudf==23.10.*",
-    "dask>=2023.7.1",
-    "distributed>=2023.7.1",
+    "dask==2023.9.2",
+    "distributed==2023.9.2",
     "joblib>=0.11",
     "numba>=0.57",
     "raft-dask==23.10.*",