Merge pull request #1989 from recommenders-team/miguel/new_test_categ…

…ories

Refactor tests into new categories reviewed by Eric Gamma

.github/actions/get-test-groups/action.yml

@@ -29,6 +29,6 @@ runs:
         if [[ ${{ inputs.TEST_KIND }} == "nightly" ]]; then
           test_groups_str=$(python -c 'from tests.ci.azureml_tests.test_groups import nightly_test_groups; print([t for t in nightly_test_groups.keys() if "${{inputs.TEST_ENV}}" in t])')
         else
-          test_groups_str=$(python -c 'from tests.ci.azureml_tests.test_groups import unit_test_groups; print(list(unit_test_groups.keys()))')
+          test_groups_str=$(python -c 'from tests.ci.azureml_tests.test_groups import pr_gate_test_groups; print(list(pr_gate_test_groups.keys()))')
         fi
         echo "test_groups=$test_groups_str" >> $GITHUB_OUTPUT

README.md

@@ -135,13 +135,13 @@ This project adheres to [Microsoft's Open Source Code of Conduct](CODE_OF_CONDUC
 
 ## Build Status
 
-These tests are the nightly builds, which compute the smoke and integration tests. `main` is our principal branch and `staging` is our development branch. We use [pytest](https://docs.pytest.org/) for testing python utilities in [recommenders](recommenders) and [Papermill](https://github.com/nteract/papermill) and [Scrapbook](https://nteract-scrapbook.readthedocs.io/en/latest/) for the [notebooks](examples). 
+These tests are the nightly builds, which compute the asynchronous tests. `main` is our principal branch and `staging` is our development branch. We use [pytest](https://docs.pytest.org/) for testing python utilities in [recommenders](recommenders) and [Papermill](https://github.com/nteract/papermill) and [Scrapbook](https://nteract-scrapbook.readthedocs.io/en/latest/) for the [notebooks](examples). 
 
 For more information about the testing pipelines, please see the [test documentation](tests/README.md).
 
 ### AzureML Nightly Build Status
 
-Smoke and integration tests are run daily on AzureML.
+The nightly build tests are run daily on AzureML.
 
 | Build Type | Branch | Status |  | Branch | Status |
 | --- | --- | --- | --- | --- | --- |

SETUP.md

@@ -156,9 +156,9 @@ First make sure that the tag that you want to add, e.g. `0.6.0`, is added in [`r
 1. Make sure that the code in main passes all the tests (unit and nightly tests).
 1. Create a tag with the version number: e.g. `git tag -a 0.6.0 -m "Recommenders 0.6.0"`.
 1. Push the tag to the remote server: `git push origin 0.6.0`.
-1. When the new tag is pushed, a release pipeline is executed. This pipeline runs all the tests again (unit, smoke and integration), generates a wheel and a tar.gz which are uploaded to a [GitHub draft release](https://github.com/microsoft/recommenders/releases).
+1. When the new tag is pushed, a release pipeline is executed. This pipeline runs all the tests again (PR gate and nightly builds), generates a wheel and a tar.gz which are uploaded to a [GitHub draft release](https://github.com/microsoft/recommenders/releases).
 1. Fill up the draft release with all the recent changes in the code.
 1. Download the wheel and tar.gz locally, these files shouldn't have any bug, since they passed all the tests.
 1. Install twine: `pip install twine`
-1. Publish the wheel and tar.gz to pypi: `twine upload recommenders*`
+1. Publish the wheel and tar.gz to PyPI: `twine upload recommenders*`
 

pyproject.toml

@@ -14,10 +14,7 @@ build-backend = "setuptools.build_meta"
 [tool.pytest.ini_options]
 markers = [
     "experimental: tests that will not be executed and may need extra dependencies",
-    "flaky: flaky tests that can fail unexpectedly",
     "gpu: tests running on GPU",
-    "integration: integration tests",
     "notebooks: tests for notebooks",
-    "smoke: smoke tests",
     "spark: tests that requires Spark",
 ]

recommenders/utils/gpu_utils.py

@@ -97,7 +97,7 @@ def get_cuda_version():
                 data = f.read().replace("\n", "")
             return data
         else:
-            return "Cannot find CUDA in this machine"
+            return None
 
 
 def get_cudnn_version():
@@ -125,14 +125,14 @@ def find_cudnn_in_headers(candiates):
             if version:
                 return version
             else:
-                return "Cannot find CUDNN version"
+                return None
         else:
-            return "Cannot find CUDNN version"
+            return None
 
     try:
         import torch
 
-        return torch.backends.cudnn.version()
+        return str(torch.backends.cudnn.version())
     except (ImportError, ModuleNotFoundError):
         if sys.platform == "win32":
             candidates = [r"C:\NVIDIA\cuda\include\cudnn.h"]

setup.py

@@ -42,7 +42,7 @@
     "transformers>=2.5.0,<5",
     "category_encoders>=1.3.0,<2",
     "jinja2>=2,<3.1",
-    "requests>=2.0.0,<3",
+    "requests>=2.31.0,<3",
     "cornac>=1.1.2,<1.15.2;python_version<='3.7'",
     "cornac>=1.15.2,<2;python_version>='3.8'",  # After 1.15.2, Cornac requires python 3.8
     "retrying>=1.3.3",
@@ -64,7 +64,7 @@
         "tensorflow~=2.6.1;python_version=='3.6'",
         "tensorflow~=2.7.0;python_version>='3.7'",
         "tf-slim>=1.1.0",
-        "torch>=1.8",  # for CUDA 11 support
+        "torch>=1.13.1",  # for CUDA 11 support
         "fastai>=1.0.46,<2",
     ],
     "spark": [
@@ -89,6 +89,7 @@
     "vowpalwabbit>=8.9.0,<9",
     # nni needs to be upgraded
     "nni==1.5",
+    "pymanopt>=0.2.5",
 ]
 
 # The following dependency can be installed as below, however PyPI does not allow direct URLs.

tests/README.md

@@ -63,7 +63,9 @@ In this section we show how to create tests and add them to the test pipeline. T
 1. Create your code in the library and/or notebooks.
 1. Design the unit tests for the code.
 1. If you have written a notebook, design the notebook tests and check that the metrics they return is what you expect.
-1. Add the tests to the AzureML pipeline in the corresponding [test group](./ci/azureml_tests/test_groups.py). **Please note that if you don't add your tests to the pipeline, they will not be executed.**
+1. Add the tests to the AzureML pipeline in the corresponding [test group](./ci/azureml_tests/test_groups.py). 
+
+**Please note that if you don't add your tests to the pipeline, they will not be executed.**
 
 ### How to create tests for the Recommenders library
 
@@ -74,8 +76,6 @@ You want to make sure that all your code works before you submit it to the repos
 * Use the mark `@pytest.mark.gpu` if you want the test to be executed
   in a GPU environment. Use `@pytest.mark.spark` if you want the test
   to be executed in a Spark environment.
-* Use `@pytest.mark.smoke` and `@pytest.mark.integration` to mark the
-  tests as smoke tests and integration tests.
 * Use `@pytest.mark.notebooks` if you are testing a notebook.
 * Avoid using `is` in the asserts, instead use the operator `==`.
 * Follow the pattern `assert computation == value`, for example:
@@ -113,7 +113,7 @@ For executing this test, first make sure you are in the correct environment as d
 *Notice that the next instruction executes the tests from the root folder.*
 
 ```bash
-pytest tests/unit/test_notebooks_python.py::test_sar_single_node_runs
+pytest tests/unit/examples/test_notebooks_python.py::test_sar_single_node_runs
 ```
 
 #### Developing nightly tests with Papermill and Scrapbook
@@ -124,7 +124,7 @@ The first step is to tag the parameters that we are going to inject. For it we n
 
 The way papermill works to inject parameters is very simple, it generates a copy of the notebook (in our code we call it `OUTPUT_NOTEBOOK`), and creates a new cell with the injected variables.
 
-The second modification that we need to do to the notebook is to record the metrics we want to test using `sb.glue("output_variable", python_variable_name)`. We normally use the last cell of the notebook to record all the metrics. These are the metrics that we are going to control in the smoke and integration tests.
+The second modification that we need to do to the notebook is to record the metrics we want to test using `sb.glue("output_variable", python_variable_name)`. We normally use the last cell of the notebook to record all the metrics. These are the metrics that we are going to control in the smoke and functional tests.
 
 This is an example on how we do a smoke test. The complete code can be found in [smoke/examples/test_notebooks_python.py](./smoke/examples/test_notebooks_python.py):
 
@@ -136,7 +136,6 @@ import scrapbook as sb
 TOL = 0.05
 ABS_TOL = 0.05
 
-@pytest.mark.smoke
 def test_sar_single_node_smoke(notebooks, output_notebook, kernel_name):
     notebook_path = notebooks["sar_single_node"]
     pm.execute_notebook(
@@ -159,14 +158,14 @@ For executing this test, first make sure you are in the correct environment as d
 *Notice that the next instructions execute the tests from the root folder.*
 
 ```
-pytest tests/smoke/test_notebooks_python.py::test_sar_single_node_smoke
+pytest tests/smoke/examples/test_notebooks_python.py::test_sar_single_node_smoke
 ```
 
 More details on how to integrate Papermill with notebooks can be found in their [repo](https://github.com/nteract/papermill). Also, you can check the [Scrapbook repo](https://github.com/nteract/scrapbook).
 
 ### How to add tests to the AzureML pipeline
 
-To add a new test to the AzureML pipeline, add the test path to an appropriate test group listed in [test_groups.py](https://github.com/microsoft/recommenders/blob/main/tests/ci/azureml_tests/test_groups.py). 
+To add a new test to the AzureML pipeline, add the test path to an appropriate test group listed in [test_groups.py](./ci/azureml_tests/test_groups.py). 
 
 Tests in `group_cpu_xxx` groups are executed on a CPU-only AzureML compute cluster node. Tests in `group_gpu_xxx` groups are executed on a GPU-enabled AzureML compute cluster node with GPU related dependencies added to the AzureML run environment. Tests in `group_pyspark_xxx` groups are executed on a CPU-only AzureML compute cluster node, with the PySpark related dependencies added to the AzureML run environment. 
 
@@ -177,15 +176,13 @@ Example of adding a new test:
 1. In the environment that you are running your code, first see if there is a group whose total runtime is less than the threshold.
 ```python
 "group_spark_001": [  # Total group time: 271.13s
-    "tests/smoke/recommenders/dataset/test_movielens.py::test_load_spark_df",  # 4.33s
-    "tests/integration/recommenders/datasets/test_movielens.py::test_load_spark_df",  # 25.58s + 101.99s + 139.23s
+    "tests/data_validation/recommenders/datasets/test_movielens.py::test_load_spark_df",  # 4.33s+ 25.58s + 101.99s + 139.23s
 ],
 ```
 2. Add the test to the group, add the time it takes to compute, and update the total group time.
 ```python
 "group_spark_001": [  # Total group time: 571.13s
-    "tests/smoke/recommenders/dataset/test_movielens.py::test_load_spark_df",  # 4.33s
-    "tests/integration/recommenders/datasets/test_movielens.py::test_load_spark_df",  # 25.58s + 101.99s + 139.23s
+    "tests/data_validation/recommenders/datasets/test_movielens.py::test_load_spark_df",  # 4.33s+ 25.58s + 101.99s + 139.23s
     #
     "tests/path/to/test_new.py::test_new_function", # 300s
 ],
@@ -217,133 +214,50 @@ Then, follow the steps below to create the AzureML infrastructure:
 
 To manually execute the tests in the CPU, GPU or Spark environments, first **make sure you are in the correct environment as described in the [SETUP.md](../SETUP.md)**.
 
-*Click on the following menus* to see more details on how to execute the unit, smoke and integration tests:
-
-<details>
-<summary><strong><em>Unit tests</em></strong></summary>
-
-Unit tests ensure that each class or function behaves as it should. Every time a developer makes a pull request to staging or main branch, a battery of unit tests is executed.
-
-*Note that the next instructions execute the tests from the root folder.*
-
-For executing the Python unit tests for the utilities:
-
-    pytest tests/unit -m "not notebooks and not spark and not gpu" --durations 0
-
-For executing the Python unit tests for the notebooks:
-
-    pytest tests/unit -m "notebooks and not spark and not gpu" --durations 0
-
-For executing the Python GPU unit tests for the utilities:
-
-    pytest tests/unit -m "not notebooks and not spark and gpu" --durations 0
-
-For executing the Python GPU unit tests for the notebooks:
-
-    pytest tests/unit -m "notebooks and not spark and gpu" --durations 0
-
-For executing the PySpark unit tests for the utilities:
-
-    pytest tests/unit -m "not notebooks and spark and not gpu" --durations 0
-
-For executing the PySpark unit tests for the notebooks:
-
-    pytest tests/unit -m "notebooks and spark and not gpu" --durations 0
-
-*NOTE: Adding `--durations 0` shows the computation time of all tests.*
-
-*NOTE: Adding `--disable-warnings` will disable the warning messages.*
-
-</details>
-
-<details>
-<summary><strong><em>Smoke tests</em></strong></summary>
-
-Smoke tests make sure that the system works and are executed just before the integration tests every night.
+### CPU tests
 
 *Note that the next instructions execute the tests from the root folder.*
 
-For executing the Python smoke tests:
+For executing the CPU tests for the utilities:
 
-    pytest tests/smoke -m "smoke and not spark and not gpu" --durations 0
+    pytest tests -m "not notebooks and not spark and not gpu" --durations 0 --disable-warnings
 
-For executing the Python GPU smoke tests:
+For executing the CPU tests for the notebooks:
 
-    pytest tests/smoke -m "smoke and not spark and gpu" --durations 0
+    pytest tests -m "notebooks and not spark and not gpu" --durations 0 --disable-warnings
 
-For executing the PySpark smoke tests:
+If you want to execute a specific test, you can use the following command:
 
-    pytest tests/smoke -m "smoke and spark and not gpu" --durations 0
+    pytest tests/data_validation/recommenders/datasets/test_mind.py::test_mind_url --durations 0 --disable-warnings
 
-*NOTE: Adding `--durations 0` shows the computation time of all tests.*
+If you want to execute any of the tests types (data_validation, unit, smoke, functional, etc.) you can use the following command:
 
-*NOTE: Adding `--disable-warnings` will disable the warning messages.*
+    pytest tests/data_validation -m "not notebooks and not spark and not gpu" --durations 0 --disable-warnings
 
-</details>
+### GPU tests
 
-<details>
-<summary><strong><em>Integration tests</em></strong></summary>
+For executing the GPU tests for the utilities:
 
-Integration tests make sure that the program results are acceptable.
+    pytest tests -m "not notebooks and not spark and gpu" --durations 0 --disable-warnings
 
-*Note that the next instructions execute the tests from the root folder.*
+For executing the GPU tests for the notebooks:
 
-For executing the Python integration tests:
+    pytest tests -m "notebooks and not spark and gpu" --durations 0 --disable-warnings
 
-    pytest tests/integration -m "integration and not spark and not gpu" --durations 0
+### Spark tests
 
-For executing the Python GPU integration tests:
+For executing the PySpark tests for the utilities:
 
-    pytest tests/integration -m "integration and not spark and gpu" --durations 0
+    pytest tests -m "not notebooks and spark and not gpu" --durations 0 --disable-warnings
 
-For executing the PySpark integration tests:
+For executing the PySpark tests for the notebooks:
 
-    pytest tests/integration -m "integration and spark and not gpu" --durations 0
+    pytest tests -m "notebooks and spark and not gpu" --durations 0 --disable-warnings
 
 *NOTE: Adding `--durations 0` shows the computation time of all tests.*
 
 *NOTE: Adding `--disable-warnings` will disable the warning messages.*
 
-</details>
-
-<details>
-<summary><strong><em>Current Skipped Tests</em></strong></summary>
-
-Several of the tests are skipped for various reasons which are noted below.
-
-<table>
-<tr>
-<td>Test Module</td>
-<td>Test</td>
-<td>Test Environment</td>
-<td>Reason</td>
-</tr>
-<tr>
-<td>unit/recommenders/datasets/test_wikidata</td>
-<td>*</td>
-<td>Linux</td>
-<td>Wikidata API is unstable</td>
-</tr>
-<tr>
-<td>integration/recommenders/datasets/test_notebooks_python</td>
-<td>test_wikidata</td>
-<td>Linux</td>
-<td>Wikidata API is unstable</td>
-</tr>
-<tr>
-<td>*/test_notebooks_python</td>
-<td>test_vw*</td>
-<td>Linux</td>
-<td>VW pip package has installation incompatibilities</td>
-</tr>
-<tr>
-<td>*/test_notebooks_python</td>
-<td>test_nni*</td>
-<td>Linux</td>
-<td>NNI pip package has installation incompatibilities</td>
-</tr>
-</table>
-
 In order to skip a test because there is an OS or upstream issue which cannot be resolved you can use pytest [annotations](https://docs.pytest.org/en/latest/skipping.html).
 
 Example:
@@ -353,4 +267,3 @@ Example:
     def test_to_skip():
         assert False
 
-</details>

tests/ci/azureml_tests/run_groupwise_pytest.py

@@ -13,7 +13,7 @@
 import argparse
 import glob
 from azureml.core import Run
-from test_groups import nightly_test_groups, unit_test_groups
+from test_groups import nightly_test_groups, pr_gate_test_groups
 
 if __name__ == "__main__":
 
@@ -46,7 +46,7 @@
     if args.testkind == "nightly":
         test_group = nightly_test_groups[args.testgroup]
     else:
-        test_group = unit_test_groups[args.testgroup]
+        test_group = pr_gate_test_groups[args.testgroup]
 
     logger.info("Tests to be executed")
     logger.info(str(test_group))