317 reenable testing

pathology/tumor_detection/ignite/profiling_camelyon_pipeline.ipynb

@@ -46,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!python -c \"import monai\" || pip install -q git+https://github.com/Project-MONAI/MONAI#egg=MONAI[gdown,ignite,torchvision,cucim]\n",
+    "!python -c \"import monai\" || pip install -q \"monai-weekly[gdown,ignite,torchvision,cucim]\"\n",
     "!python -c \"import ignite\" || pip install -q pytorch-ignite\n",
     "!python -c \"import torchvision\" || pip install -q torchvision\n",
     "!python -c \"import cucim\" || pip install -q cucim\n",
@@ -568,10 +568,10 @@
     "\n",
     "# Nsys output column names are different in different versions, \n",
     "# so we need to find the corresponding columns\n",
-    "avg_col = [c for c in summary.columns if c.startswith('Av')] \n",
+    "avg_col = [c for c in summary.columns if c.startswith('Av')]\n",
     "std_col = [c for c in summary.columns if c.startswith('Std')]\n",
-    "min_col = [c for c in summary.columns if c.startswith('Min')] \n",
-    "max_col = [c for c in summary.columns if c.startswith('Max')] \n",
+    "min_col = [c for c in summary.columns if c.startswith('Min')]\n",
+    "max_col = [c for c in summary.columns if c.startswith('Max')]\n",
     "\n",
     "# Normalize each transform range with total average time (percentage of transform time)\n",
     "summary[[\"avg%\", \"std%\", 'min%', 'max%']] = summary[avg_col+std_col+min_col+max_col] / summary[avg_col].sum()[0] * 100\n",
@@ -662,21 +662,15 @@
     "axes[-1].set_xticks(range(0, 101, 10))\n",
     "axes[-1].set_xlabel(\"% of preprocessing time\", fontsize=20)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "interpreter": {
    "hash": "98b0a9b7b4eaaa670588a142fd0a9b87eaafe866f1db4228be72b4211d12040f"
   },
   "kernelspec": {
-   "display_name": "Python 3.8.10 64-bit ('base': conda)",
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -689,7 +683,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

runner.sh

@@ -48,6 +48,7 @@ doesnt_contain_max_epochs=("${doesnt_contain_max_epochs[@]}" 2d_slices_from_3d_t
 doesnt_contain_max_epochs=("${doesnt_contain_max_epochs[@]}" preprocess_to_build_detection_dataset.ipynb)
 doesnt_contain_max_epochs=("${doesnt_contain_max_epochs[@]}" preprocess_detect_scene_and_split_fold.ipynb)
 doesnt_contain_max_epochs=("${doesnt_contain_max_epochs[@]}" preprocess_extract_images_from_video.ipynb)
+doesnt_contain_max_epochs=("${doesnt_contain_max_epochs[@]}" profiling_camelyon_pipeline.ipynb)
 
 # output formatting
 separator=""
@@ -72,7 +73,6 @@ failfast=false
 pattern="-and -name '*' -and ! -wholename '*federated_learning*'\
  -and ! -wholename '*transchex_openi*'\
  -and ! -wholename '*unetr_*'\
- -and ! -wholename '*profiling_camelyon*'\
  -and ! -wholename '*profiling_train_base_nvtx*'\
  -and ! -wholename '*benchmark_global_mutual_information*'\
  -and ! -wholename '*spleen_segmentation_3d_visualization_basic*'\