README.md

Tests

In this document we show our test infrastructure and how to contribute tests to the repository.

Table of Contents

• Test workflows
• Categories of tests
• Scalable test infrastructure with AzureML
• How to contribute tests to the repository
  • How to create tests for the Recommenders library
  • How to create tests for the notebooks
  • How to add tests to the AzureML pipeline
  • Setup GitHub Actions with AzureML compute clusters
• How to execute tests in your local environment

Test workflows

All the tests in this repository are part of the following two workflows: the PR gate and the nightly builds.

PR gates are the set of tests executed after doing a pull request and they should be quick. The objective is to validate that the code is not breaking anything before merging it. The PR gate should not surpass 20-30 minutes.

The nightly builds are tests executed asynchronously and can take hours. Some tests take so long that they cannot be executed in a PR gate, therefore they are executed asynchronously in the nightly builds.

Notice that the errors in the nightly builds are detected after the code has been merged. This is the reason why, with nightly builds, it is interesting to have a two-level branching strategy. In the standard one-level branching strategy, all pull requests go to the main branch. If a nightly build fails, then the main branch has broken code. In the two-level branching strategy, a pre-production or staging branch is where developers send pull requests to. The main branch is only updated from the staging branch after the nightly builds are successful. This way, the main branch always has working code.

Categories of tests

The tests in this repository are divided into the following categories:

• Data validation tests: In the data validation tests, we ensure that the schema for input and output data for each function in the pipeline matches the desired prespecified schema, that the data is available and has the correct size. They should be fast and can be added to the PR gate.
• Unit tests: In the unit tests we just make sure the python utilities and notebooks run correctly. Unit tests are fast, ideally less than 5min and are run in every pull request. They belong to the PR gate. For this type of tests, synthetic data can be used.
• Functional tests: These tests make sure that the components of the project not just run but their function is correct. For example, we want to test that an ML model evaluation of RMSE gives a positive number. These tests can be run asynchronously in the nightly builds and can take hours. In these tests, we want to use real data.
• Integration tests: We want to make sure that the interaction between different components is correct. For example, the interaction between data ingestion pipelines and the compute where the model is trained, or between the compute and a database. These tests can be of variable length, if they are fast, we could add them to the PR gate, otherwise, we will add them to the nightly builds. For this type of tests, synthetic and real data can be used.
• Smoke tests: The smoke tests are gates to the slow tests in the nightly builds to detect quick errors. If we are running a test with a large dataset that takes 4h, we want to create a faster version of the large test (maybe with a small percentage of the dataset or with 1 epoch) to ensure that it runs end-to-end without obvious failures. Smoke tests can run sequentially with functional or integration tests in the nightly builds, and should be fast, ideally less than 20min. They use the same type of data as their longer counterparts.
• Performance test: The performance tests are tests that measure the computation time or memory footprint of a piece of code and make sure that this is bounded between some limits. Another kind of performance testing can be a load test to measure an API response time, this can be specially useful when working with large deep learning models. For this type of tests, synthetic data can be used.
• Responsible AI tests: Responsible AI tests are test that enforce fairness, transparency, explainability, human-centeredness, and privacy.
• Security tests: Security tests are tests that make sure that the code is not vulnerable to attacks. These can detect potential security issues either in python packages or the underlying OS, in addition to scheduled scans in the production pipelines.
• Regression tests: In some situations, we are migrating from a deprecated version to a new version of the code, or maybe we are maintaining two versions of the same library (i.e. Tensorflow v1 and v2). Regression tests make sure that the code works in both versions of the code. These types of tests sometimes are done locally, before upgrading to the new version, or they can be included in the tests pipelines if we want to execute them recurrently.

For more information, see a quick introduction testing.

Scalable test infrastructure with AzureML

AzureML is used to run the existing unit, smoke and integration tests. AzureML benefits include being able to run the tests in parallel, managing the compute environment by automatically turning it on/off, automatic logging of artifacts from test runs and more. GitHub is used as a control plane to configure and run the tests on AzureML.

In the following figure we show a workflow on how the tests are executed via AzureML:

GitHub workflows azureml-unit-tests.yml, azureml-cpu-nightly.yml, azureml-gpu-nightly.yml and azureml-spark-nightly located in .github/workflows/ are used to run the tests on AzureML. The parameters to configure AzureML are defined in the workflow yml files. The tests are divided into groups and each workflow triggers these test groups in parallel, which significantly reduces end-to-end execution time.

There are three scripts used with each workflow, all of them are located in ci/azureml_tests:

• submit_groupwise_azureml_pytest.py: this script uses parameters in the workflow yml to set up the AzureML environment for testing using the AzureML SDK.
• run_groupwise_pytest.py: this script uses pytest to run the tests of the libraries and notebooks. This script runs in an AzureML workspace with the environment created by the script above.
• test_groups.py: this script defines the groups of tests. If the tests are part of the unit tests, the total compute time of each group should be less than 15min. If the tests are part of the nightly builds, the total time of each group should be less than 35min.

How to contribute tests to the repository

In this section we show how to create tests and add them to the test pipeline. The steps you need to follow are:

1. Create your code in the library and/or notebooks.
2. Design the unit tests for the code.
3. If you have written a notebook, design the notebook tests and check that the metrics they return is what you expect.
4. Add the tests to the AzureML pipeline in the corresponding test group. 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

You want to make sure that all your code works before you submit it to the repository. Here are some guidelines for creating the unit tests:

• It is better to create multiple small tests than one large test that checks all the code.
• Use @pytest.fixture to create data in your tests.
• 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:

assert results["precision"] == pytest.approx(0.330753)

• Check always the limits of your computations, for example, you want to check that the RMSE between two equal vectors is 0:

assert rmse(rating_true, rating_true) == 0
assert rmse(rating_true, rating_pred) == pytest.approx(7.254309)

How to create tests for the notebooks

In the notebooks of this repo, we use Papermill and Scrapbook in unit, smoke and integration tests. Papermill is a tool that enables you to parametrize and execute notebooks. Scrapbook is a library for recording a notebook’s data values and generate visual content as “scraps”. These recorded scraps can be read at a future time. We use Scrapbook to collect the metrics in the notebooks.

Developing PR gate tests with Papermill and Scrapbook

Executing a notebook with Papermill is easy, this is what we mostly do in the unit tests. Next, we show just one of the tests that we have in tests/unit/examples/test_notebooks_python.py.

import pytest
import papermill as pm

@pytest.mark.notebooks
def test_sar_single_node_runs(notebooks, output_notebook, kernel_name):
    notebook_path = notebooks["sar_single_node"]
    pm.execute_notebook(notebook_path, output_notebook, kernel_name=kernel_name)

Notice that the input of the function is a fixture defined in conftest.py. For more information, please see the definition of fixtures in PyTest.

For executing this test, first make sure you are in the correct environment as described in the SETUP.md:

Notice that the next instruction executes the tests from the root folder.

pytest tests/unit/test_notebooks_python.py::test_sar_single_node_runs

Developing nightly tests with Papermill and Scrapbook

A more advanced option is used in the nightly tests, where we not only execute the notebook, but inject parameters and recover the computed metrics.

The first step is to tag the parameters that we are going to inject. For it we need to modify the notebook. We will add a tag with the name parameters. To add a tag, go the notebook menu, View, Cell Toolbar and Tags. A tag field will appear on every cell. The variables in the cell tagged with parameters can be injected. The typical variables that we inject are MOVIELENS_DATA_SIZE, EPOCHS and other configuration variables for our algorithms.

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.

This is an example on how we do a smoke test. The complete code can be found in smoke/examples/test_notebooks_python.py:

import pytest
import papermill as pm
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(
        notebook_path,
        output_notebook,
        kernel_name=kernel_name,
        parameters=dict(TOP_K=10, MOVIELENS_DATA_SIZE="100k"),
    )
    results = sb.read_notebook(output_notebook).scraps.dataframe.set_index("name")[
        "data"
    ]
    assert results["precision"] == pytest.approx(0.330753, rel=TOL, abs=ABS_TOL)
    assert results["recall"] == pytest.approx(0.176385, rel=TOL, abs=ABS_TOL)

As it can be seen in the code, we are injecting the dataset size and the top k and we are recovering the precision and recall at k.

For executing this test, first make sure you are in the correct environment as described in the SETUP.md:

Notice that the next instructions execute the tests from the root folder.

pytest tests/smoke/test_notebooks_python.py::test_sar_single_node_smoke

More details on how to integrate Papermill with notebooks can be found in their repo. Also, you can check the Scrapbook repo.

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.

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.

It's important to keep in mind while adding a new test that the runtime of the test group should not exceed the specified threshold in test_groups.py.

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.

"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
],

2. Add the test to the group, add the time it takes to compute, and update the total group time.

"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/path/to/test_new.py::test_new_function", # 300s
],

3. If all the groups of your environment are above the threshold, add a new group.

Setup GitHub Actions with AzureML compute clusters

In this section we explain how to create the AzureML infrastructure to run the tests in GitHub Actions.

In order to execute the tests in Recommenders, we need two types of virtual machines: ones without GPU, to execute the CPU and Spark tests, and ones with GPU, to execute the GPU tests. Therefore, the first step is to request enough quota for your subscription.

Then, follow the steps below to create the AzureML infrastructure:

1. Create a new AzureML workspace.
   • Name: azureml-test-workspace
   • Resource group: recommenders_project_resources
   • Location: Make sure you have enough quota in the location you choose
2. Create two new clusters: cpu-cluster and gpu-cluster. Go to compute, then compute cluster, then new.
   • Select the CPU VM base. Anything above 64GB of RAM, and 8 cores should be fine.
   • Select the GPU VM base. Anything above 56GB of RAM, and 6 cores, and an NVIDIA K80 should be fine.
3. Add the subscription ID to GitHub action secrets here. Create a new repository secret called AZUREML_TEST_SUBID and add the subscription ID as the value.
4. Make sure you have installed Azure CLI, and that you are logged in: az login.
5. Select your subscription: az account set -s $AZURE_SUBSCRIPTION_ID.
6. Create a Service Principal: az ad sp create-for-rbac --name "recommenders-cicd" --role contributor --scopes /subscriptions/$AZURE_SUBSCRIPTION_ID --sdk-auth.
7. Add the output from the Service Principal (should be a JSON blob) as an action secret AZUREML_TEST_CREDENTIALS.

How to execute tests in your local environment

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.

Click on the following menus to see more details on how to execute the unit, smoke and integration tests:

Unit tests

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.

Smoke tests

Smoke tests make sure that the system works and are executed just before the integration tests every night.

Note that the next instructions execute the tests from the root folder.

For executing the Python smoke tests:

pytest tests/smoke -m "smoke and not spark and not gpu" --durations 0

For executing the Python GPU smoke tests:

pytest tests/smoke -m "smoke and not spark and gpu" --durations 0

For executing the PySpark smoke tests:

pytest tests/smoke -m "smoke 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.

Integration tests

Integration tests make sure that the program results are acceptable.

Note that the next instructions execute the tests from the root folder.

For executing the Python integration tests:

pytest tests/integration -m "integration and not spark and not gpu" --durations 0

For executing the Python GPU integration tests:

pytest tests/integration -m "integration and not spark and gpu" --durations 0

For executing the PySpark integration tests:

pytest tests/integration -m "integration 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.

Current Skipped Tests

Several of the tests are skipped for various reasons which are noted below.

Test Module	Test	Test Environment	Reason
unit/recommenders/datasets/test_wikidata	*	Linux	Wikidata API is unstable
integration/recommenders/datasets/test_notebooks_python	test_wikidata	Linux	Wikidata API is unstable
*/test_notebooks_python	test_vw*	Linux	VW pip package has installation incompatibilities
*/test_notebooks_python	test_nni*	Linux	NNI pip package has installation incompatibilities

In order to skip a test because there is an OS or upstream issue which cannot be resolved you can use pytest annotations.

Example:

@pytest.mark.skip(reason="<INSERT VALID REASON>")
@pytest.mark.skipif(sys.platform == 'win32', reason="Not implemented on Windows")
def test_to_skip():
    assert False