tests: ignore::FutureWarning...

.azure/gpu-unittests.yml

@@ -136,14 +136,15 @@ jobs:
         displayName: "Show caches"
 
       - bash: |
-          python -m pytest torchmetrics -s --cov=torchmetrics \
+          python -m pytest torchmetrics --cov=torchmetrics \
             --timeout=240 --durations=50 \
             --reruns 2 --reruns-delay 1
           #  --numprocesses=5 --dist=loadfile
         env:
           DOCTEST_DOWNLOAD_TIMEOUT: "180"
           SKIP_SLOW_DOCTEST: "1"
         workingDirectory: src
+        timeoutInMinutes: "40"
         displayName: "DocTesting"
 
       - bash: |
@@ -154,7 +155,7 @@ jobs:
         displayName: "Pull testing data from S3"
 
       - bash: |
-          python -m pytest $(TEST_DIRS) -v \
+          python -m pytest $(TEST_DIRS) \
             -m "not DDP" --numprocesses=5 --dist=loadfile \
             --cov=torchmetrics --timeout=240 --durations=100 \
             --reruns 3 --reruns-delay 1

.github/workflows/ci-tests.yml

@@ -161,7 +161,7 @@ jobs:
         if: ${{ env.TEST_DIRS != '' }}
         working-directory: ./tests
         run: |
-          python -m pytest -v \
+          python -m pytest \
             $TEST_DIRS \
             --cov=torchmetrics \
             --durations=50 \

pyproject.toml

@@ -110,7 +110,10 @@ addopts = [
 markers = [
     "DDP: mark a test as Distributed Data Parallel",
 ]
-#filterwarnings = ["error::FutureWarning"] # ToDo
+filterwarnings = [
+    "ignore::FutureWarning",
+    "default:::torchmetrics",
+]
 xfail_strict = true
 junit_duration_report = "call"
 

tests/unittests/__init__.py

@@ -1,4 +1,5 @@
 import os.path
+import warnings
 from typing import NamedTuple
 
 import numpy
@@ -28,6 +29,11 @@
 
 _reference_cachier = cachier(cache_dir=_PATH_TEST_CACHE, separate_files=True)
 
+# ignore FutureWarnings while testing (mainly appearing with DDP runs)
+warnings.filterwarnings("ignore", category=FutureWarning, module="sklearn.*")
+warnings.filterwarnings("ignore", category=FutureWarning, module="transformers.*")
+warnings.filterwarnings("ignore", category=UserWarning, module="sklearn.*")
+
 
 if torch.cuda.is_available():
     torch.backends.cuda.matmul.allow_tf32 = False