dev: autofind available gpu in tests

tests/myria3d/test_train_and_predict.py

@@ -3,6 +3,8 @@
 
 import numpy as np
 import pytest
+from lightning.pytorch.accelerators import find_usable_cuda_devices
+
 
 from myria3d.pctl.dataset.toy_dataset import TOY_LAS_DATA
 from myria3d.pctl.dataset.utils import pdal_read_las_array
@@ -59,9 +61,7 @@ def test_FrenchLidar_RandLaNetDebug_with_gpu(toy_dataset_hdf5_path, tmpdir_facto
     tmp_paths_overrides = _make_list_of_necesary_hydra_overrides_with_tmp_paths(
         toy_dataset_hdf5_path, tmpdir
     )
-    # We will always use the first GPU id for tests, because it always exists if there are some GPUs.
-    # Attention to concurrency with other processes using the GPU when running tests.
-    gpu_id = 0
+    gpu_id = find_usable_cuda_devices(1)
     cfg_one_epoch = make_default_hydra_cfg(
         overrides=[
             "experiment=RandLaNetDebug",
@@ -216,7 +216,7 @@ def _run_test_right_after_training(
     tmp_paths_overrides = _make_list_of_necesary_hydra_overrides_with_tmp_paths(
         toy_dataset_hdf5_path, tmpdir
     )
-    devices = "[0]" if accelerator == "gpu" else 1
+    devices = find_usable_cuda_devices(1) if accelerator == "gpu" else 1
     cfg_test_using_trained_model = make_default_hydra_cfg(
         overrides=[
             "experiment=test",  # sets task.task_name to "test"

tests/runif.py

@@ -1,5 +1,6 @@
 import pytest
 import torch
+from lightning.pytorch.accelerators import find_usable_cuda_devices
 
 """
 Simplified from:
@@ -35,8 +36,12 @@ def __new__(
         reasons = []
 
         if min_gpus:
-            conditions.append(torch.cuda.device_count() < min_gpus)
-            reasons.append(f"GPUs>={min_gpus}")
+            try:
+                find_usable_cuda_devices(min_gpus)
+                conditions.append(False)
+            except (ValueError, RuntimeError) as _:
+                conditions.append(True)
+                reasons.append(f"GPUs>={min_gpus}")
 
         reasons = [rs for cond, rs in zip(conditions, reasons) if cond]
         return pytest.mark.skipif(