[ci] script to validate tutorials were built in > 0 sec

.jenkins/build.sh

@@ -105,6 +105,7 @@ if [[ "${JOB_BASE_NAME}" == *worker_* ]]; then
 
   # Step 5: Remove INVISIBLE_CODE_BLOCK from .html/.rst.txt/.ipynb/.py files
   bash $DIR/remove_invisible_code_block_batch.sh docs
+  python .jenkins/validate_tutorials_built.py
 
   # Step 6: Copy generated files to S3, tag with commit ID
   7z a worker_${WORKER_ID}.7z docs
@@ -138,6 +139,7 @@ elif [[ "${JOB_BASE_NAME}" == *manager ]]; then
 
   # Step 5: Remove INVISIBLE_CODE_BLOCK from .html/.rst.txt/.ipynb/.py files
   bash $DIR/remove_invisible_code_block_batch.sh docs
+  python .jenkins/validate_tutorials_built.py
 
   # Step 6: Copy generated HTML files and static files to S3
   7z a manager.7z docs

.jenkins/validate_tutorials_built.py

@@ -0,0 +1,102 @@
+from pathlib import Path
+from typing import List
+
+from bs4 import BeautifulSoup
+
+REPO_ROOT = Path(__file__).parent.parent
+
+# files that are ok to have 0 min 0 sec time, probably because they don't have any python code
+OK_TO_NOT_RUN = [
+    "beginner/basics/intro.html",  # no code
+]
+
+# when the tutorial is fixed, remove it from this list
+KNOWN_BAD = [
+    "beginner/translation_transformer.html",
+    "beginner/profiler.html",
+    "beginner/saving_loading_models.html",
+    "beginner/introyt/captumyt.html",
+    "beginner/introyt/trainingyt.html",
+    "beginner/examples_nn/polynomial_module.html",
+    "beginner/examples_nn/dynamic_net.html",
+    "beginner/examples_nn/polynomial_optim.html",
+    "beginner/examples_nn/polynomial_nn.html",
+    "beginner/examples_tensor/polynomial_numpy.html",
+    "beginner/examples_tensor/polynomial_tensor.html",
+    "beginner/former_torchies/autograd_tutorial_old.html",
+    "beginner/former_torchies/tensor_tutorial_old.html",
+    "beginner/examples_autograd/polynomial_autograd.html",
+    "beginner/examples_autograd/polynomial_custom_function.html",
+    "intermediate/forward_ad_usage.html",
+    "intermediate/parametrizations.html",
+    "intermediate/reinforcement_q_learning.html",
+    "intermediate/text_to_speech_with_torchaudio.html",
+    "intermediate/mnist_train_nas.html",
+    "intermediate/fx_conv_bn_fuser.html",
+    "advanced/super_resolution_with_onnxruntime.html",
+    "advanced/ddp_pipeline.html",
+    "prototype/fx_graph_mode_ptq_dynamic.html",
+    "prototype/vmap_recipe.html",
+    "prototype/torchscript_freezing.html",
+    "prototype/nestedtensor.html",
+    "recipes/recipes/saving_and_loading_models_for_inference.html",
+    "recipes/recipes/saving_multiple_models_in_one_file.html",
+    "recipes/recipes/loading_data_recipe.html",
+    "recipes/recipes/tensorboard_with_pytorch.html",
+    "recipes/recipes/what_is_state_dict.html",
+    "recipes/recipes/profiler_recipe.html",
+    "recipes/recipes/save_load_across_devices.html",
+    "recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html",
+    "recipes/recipes/dynamic_quantization.html",
+    "recipes/recipes/saving_and_loading_a_general_checkpoint.html",
+    "recipes/recipes/benchmark.html",
+    "recipes/recipes/tuning_guide.html",
+    "recipes/recipes/zeroing_out_gradients.html",
+    "recipes/recipes/defining_a_neural_network.html",
+    "recipes/recipes/timer_quick_start.html",
+    "recipes/recipes/amp_recipe.html",
+    "recipes/recipes/Captum_Recipe.html",
+]
+
+
+def tutorial_source_dirs() -> List[Path]:
+    return [
+        p.relative_to(REPO_ROOT).with_name(p.stem[:-7])
+        for p in REPO_ROOT.glob("*_source")
+    ]
+
+
+def main() -> None:
+    docs_dir = REPO_ROOT / "docs"
+    html_file_paths = []
+    for tutorial_source_dir in tutorial_source_dirs():
+        glob_path = f"{tutorial_source_dir}/**/*.html"
+        html_file_paths += docs_dir.glob(glob_path)
+
+    did_not_run = []
+    for html_file_path in html_file_paths:
+        with open(html_file_path, "r", encoding="utf-8") as html_file:
+            html = html_file.read()
+        html_soup = BeautifulSoup(html, "html.parser")
+        elems = html_soup.find_all("p", {"class": "sphx-glr-timing"})
+        for elem in elems:
+            if (
+                "Total running time of the script: ( 0 minutes  0.000 seconds)"
+                in elem.text
+                and not any(
+                    html_file_path.match(file) for file in KNOWN_BAD + OK_TO_NOT_RUN
+                )
+            ):
+                did_not_run.append(html_file_path.as_posix())
+
+    if len(did_not_run) != 0:
+        raise RuntimeError(
+            "The following file(s) are not known bad but ran in 0.000 sec, meaning that any "
+            + "python code in this tutorial probably didn't run:\n{}".format(
+                "\n".join(did_not_run)
+            )
+        )
+
+
+if __name__ == "__main__":
+    main()