Fix CI failing tests (#416)

* Use auto mode for asyncio tests

* Drop testing python 3.8

* Set build.os

* Set build options for readthedocs

* Switch everywhere to 3.8

.github/workflows/ci.yml

@@ -9,7 +9,7 @@ jobs:
       fail-fast: true
       matrix:
         os: ["ubuntu-latest"]
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
 
     steps:
       - name: Checkout source
@@ -50,7 +50,7 @@ jobs:
         uses: conda-incubator/setup-miniconda@v2
         with:
           miniconda-version: "latest"
-          python-version: "3.8"
+          python-version: "3.9"
 
       - name: Run import tests
         shell: bash -l {0}

.github/workflows/release.yml

@@ -10,10 +10,10 @@ jobs:
       - name: Checkout source
         uses: actions/checkout@v2
 
-      - name: Set up Python 3.8
+      - name: Set up Python 3.9
         uses: actions/setup-python@v1
         with:
-          python-version: 3.8
+          python-version: 3.9
 
       - name: Install pypa/build
         run: python -m pip install build wheel

.readthedocs.yml

@@ -6,7 +6,6 @@ sphinx:
 formats: all
 
 python:
-  version: "3.8"
   install:
     - method: pip
       path: .
@@ -16,3 +15,8 @@ python:
 
 submodules:
   include: all
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3"

ci/scripts/test_imports.sh

@@ -3,9 +3,9 @@ set -o errexit
 
 
 test_import () {
-    echo "Create environment: python=3.8 $1"
+    echo "Create environment: python=3.9 $1"
     # Create an empty environment
-    conda create -q -y -n test-imports -c conda-forge python=3.8
+    conda create -q -y -n test-imports -c conda-forge python=3.9
     conda activate test-imports
     pip install -e .[$1]
     echo "python -c '$2'"

dask_cloudprovider/aws/tests/test_ec2.py

@@ -48,7 +48,7 @@ async def cluster_rapids():
         # Deep Learning AMI (Ubuntu 18.04)
         ami="ami-0c7c7d78f752f8f17",
         # Python version must match local version and CUDA version must match AMI CUDA version
-        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.9",
         instance_type="p3.2xlarge",
         bootstrap=False,
         filesystem_size=120,
@@ -65,7 +65,7 @@ async def cluster_rapids_packer():
         # Packer AMI
         ami="ami-04e5539cb82859e69",
         # Python version must match local version and CUDA version must match AMI CUDA version
-        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.9",
         instance_type="p3.2xlarge",
         bootstrap=False,
         filesystem_size=120,
@@ -202,7 +202,7 @@ async def test_get_cloud_init_rapids():
         # Deep Learning AMI (Ubuntu 18.04)
         ami="ami-0c7c7d78f752f8f17",
         # Python version must match local version and CUDA version must match AMI CUDA version
-        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.9",
         instance_type="p3.2xlarge",
         bootstrap=False,
         filesystem_size=120,

dask_cloudprovider/azure/azurevm.py

@@ -433,7 +433,7 @@ class AzureVMCluster(VMCluster):
     ...                          security_group="<security group>",
     ...                          n_workers=1,
     ...                          vm_size="Standard_NC12s_v3",  # Or any NVIDIA GPU enabled size
-    ...                          docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.8",
+    ...                          docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.9",
     ...                          worker_class="dask_cuda.CUDAWorker")
     >>> from dask.distributed import Client
     >>> client = Client(cluster)

dask_cloudprovider/azure/tests/test_azurevm.py

@@ -87,7 +87,7 @@ async def test_create_rapids_cluster_sync():
 
     with AzureVMCluster(
         vm_size="Standard_NC12s_v3",
-        docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.9",
         worker_class="dask_cuda.CUDAWorker",
         worker_options={"rmm_pool_size": "15GB"},
     ) as cluster:

dask_cloudprovider/gcp/tests/test_gcp.py

@@ -125,7 +125,7 @@ async def test_create_rapids_cluster():
         filesystem_size=50,
         ngpus=2,
         gpu_type="nvidia-tesla-t4",
-        docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.9",
         worker_class="dask_cuda.CUDAWorker",
         worker_options={"rmm_pool_size": "15GB"},
         asynchronous=True,
@@ -168,7 +168,7 @@ def test_create_rapids_cluster_sync():
         filesystem_size=50,
         ngpus=2,
         gpu_type="nvidia-tesla-t4",
-        docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda11.0-runtime-ubuntu18.04-py3.9",
         worker_class="dask_cuda.CUDAWorker",
         worker_options={"rmm_pool_size": "15GB"},
         asynchronous=False,

doc/source/packer.rst

@@ -218,7 +218,7 @@ To launch `RAPIDS <https://rapids.ai/>`_ on AWS EC2 we can select a GPU instance
 
     cluster = EC2Cluster(
         ami="ami-0c7c7d78f752f8f17",  # Deep Learning AMI (this ID varies by region so find yours in the AWS Console)
-        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.9",
         instance_type="p3.2xlarge",
         bootstrap=False,  # Docker is already installed on the Deep Learning AMI
         filesystem_size=120,
@@ -263,7 +263,7 @@ pull the RAPIDS Docker image. That way when a scheduler or worker VM is created
             {
                 "type": "shell",
                 "inline": [
-                    "docker pull rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.8"
+                    "docker pull rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.9"
                 ]
             }
         ]
@@ -315,12 +315,12 @@ We can then run our code snippet again but this time it will take less than 5 mi
 
     cluster = EC2Cluster(
         ami="ami-04e5539cb82859e69",  # AMI ID provided by Packer
-        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.8",
+        docker_image="rapidsai/rapidsai:cuda10.1-runtime-ubuntu18.04-py3.9",
         instance_type="p3.2xlarge",
         bootstrap=False,
         filesystem_size=120,
     )
     cluster.scale(2)
 
     client = Client(cluster)
-    # Your cluster is ready to use
+    # Your cluster is ready to use

pytest.ini

@@ -0,0 +1,2 @@
+[pytest]
+asyncio_mode = auto

setup.py

@@ -37,5 +37,5 @@
     [console_scripts]
     dask-ecs=dask_cloudprovider.cli.ecs:go
     """,
-    python_requires=">=3.8",
+    python_requires=">=3.9",
 )