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There are two test infrastructures exist in the Kubeflow community:
If you are interested in oss-test-infra, please find useful resources here.
If you are interested in optional-test-infra, please find useful resources here
We use Prow, K8s' continuous integration tool.
- Prow is a set of binaries that run on Kubernetes and respond to GitHub events.
We use Prow to run:
- Presubmit jobs
- Postsubmit jobs
- Periodic tests
Here's high-level idea about how it works
- Prow is used to trigger E2E tests
- The E2E test will launch an Argo workflow that describes the tests to run
- Each step in the Argo workflow will be a binary invoked inside a container
- The Argo workflow will use an NFS volume to attach a shared POSIX compliant filesystem to each step in the workflow.
- Each step in the pipeline can write outputs and junit.xml files to a test directory in the volume
- A final step in the Argo pipeline will upload the outputs to GCS so they are available in spyglass
Quick Links
- Our prow jobs are defined here
- Each prow job defines a K8s PodSpec indicating a command to run
- Our prow jobs use run_e2e_workflow.py to trigger an Argo workflow that checks out our code and runs our tests.
- Our tests are structured as Argo workflows so that we can easily perform steps in parallel.
- The Argo workflow is defined in the repository being tested
- We always use the worfklow at the commit being tested
- checkout.sh is used to checkout the code being tested
- This also checks out kubeflow/testing so that all repositories can rely on it for shared tools.
This section provides guidelines for writing Argo workflows to use as E2E tests
This guide is complementary to the E2E testing guide for TFJob operator which describes how to author tests to performed as individual steps in the workflow.
Some examples to look at
- gis.jsonnet in kubeflow/examples
Follow these steps to add a new test to a repository.
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Create a Python function in that repository and return an Argo workflow if one doesn't already exist
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We use Python functions defined in each repository to define the Argo workflows corresponding to E2E tests
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You can look at
prow_config.yaml(see below) to see which Python functions are already defined in a repository.
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Modify the
prow_config.yamlat the root of the repo to trigger your new test.-
If
prow_config.yamldoesn't exist (e.g. the repository is new) copy one from an existing repository (example). -
prow_config.yamlcontains an array of workflows where each workflow defines an E2E test to run; exampleworkflows: - name: workflow-test py_func: my_test_package.my_test_module.my_test_workflow kwargs: arg1: argument- py_func: Is the Python method to create a python object representing the Argo workflow resource
- kwargs: This is an array of arguments passed to the Python method
- name: This is the base name to use for the submitted Argo workflow.
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You can use the e2e_tool.py to print out the Argo workflow and potentially submit it
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Examples
- kf_unittests.py creates the E2E workflow for kubeflow/testing
** Using ksonnet is deprecated. New pipelines should use python. **
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Create a ksonnet App in that repository and define an Argo workflow if one doesn't already exist
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We use ksonnet apps defined in each repository to define the Argo workflows corresponding to E2E tests
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If a ksonnet app already exists you can just define a new component in that app
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Create a .jsonnet file (e.g by copying an existing .jsonnet file)
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Change the import for the params to use the newly defined component
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Update the
params.libsonnetto add a stanza to define params for the new component
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You can look at
prow_config.yaml(see below) to see which ksonnet apps are already defined in a repository.
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Modify the
prow_config.yamlat the root of the repo to trigger your new test.-
If
prow_config.yamldoesn't exist (e.g. the repository is new) copy one from an existing repository (example). -
prow_config.yamlcontains an array of workflows where each workflow defines an E2E test to run; exampleworkflows: - app_dir: kubeflow/testing/workflows component: workflows name: unittests job_types: - presubmit include_dirs: - foo/* - bar/* params: params: platform: gke gkeApiVersion: v1beta1-
app_dir: Is the path to the ksonnet directory within the repository. This should be of the form
${GITHUB_ORG}/${GITHUB_REPO_NAME}/${PATH_WITHIN_REPO_TO_KS_APP} -
component: This is the name of the ksonnet component to use for the Argo workflow
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name: This is the base name to use for the submitted Argo workflow.
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The test infrastructure appends a suffix of 22 characters (see here)
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The result is passed to your ksonnet component via the name parameter
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Your ksonnet component should truncate the name if necessary to satisfy K8s naming constraints.
- e.g. Argo workflow names should be less than 63 characters because they are used as pod labels
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job_types: This is an array specifying for which types of prow jobs this workflow should be triggered on.
- Currently allowed values are presubmit, postsubmit, and periodic.
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include_dirs: If specified, the pre and postsubmit jobs will only trigger this test if the PR changed at least one file matching at least one of the listed directories.
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Python's fnmatch function is used to compare the listed patterns against the full path of modified files (see here)
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This functionality should be used to ensure that expensive tests are only run when test impacting changes are made; particularly if its an expensive or flaky presubmit
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periodic runs ignore include_dirs; a periodic run will trigger all workflows that include job_type periodic
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A given ksonnet component can have multiple workflow entries to allow different triggering conditions on pre/postsubmit
- For example, on presubmit we might run a test on a single platform (GKE) but on postsubmit that same test might run on GKE and minikube
- this can be accomplished with different entries pointing at the same ksonnet
component but with different
job_typesandparams.
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params: A dictionary of parameters to set on the ksonnet component e.g. by running
ks param set ${COMPONENT} ${PARAM_NAME} ${PARAM_VALUE}
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pytest is really useful for writing tests
- Results can be emitted as junit files which is what prow needs to report test results
- It provides annotations to skip tests or mark flaky tests as expected to fail
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Use pytest to easily script various checks
- For example kf_is_ready_test.py uses some simple scripting to test that various K8s objects are deployed and healthy
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Pytest provides fixtures for setting additional attributes in the junit files (docs)
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In particular record_xml_attribute allows us to set attributes that control how's the results are grouped in test grid
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name - This is the name shown in test grid
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Testgrid supports grouping by spliting the tests into a hierarchy based on the name
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recommendation Leverage this feature to name tests to support grouping; e.g. use the pattern
{WORKFLOW_NAME}/{PY_FUNC_NAME}-
workflow_name Workflow name as set in prow_config.yaml
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PY_FUNC_NAME the name of the python test function
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util.py provides the helper method
set_pytest_junitto set the required attributes -
run_e2e_workflow.py will pass the argument
test_target_nameto your py function to create the Argo workflow- Use this argument to set the environment variable TEST_TARGET_NAME on all Argo pods.
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classname - testgrid uses classname as the test target and allows results to be grouped by name
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recommendation - Set the classname to the workflow name as defined in prow_config.yaml
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This allows easy grouping of tests by the entries defined in prow_config.yaml
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Each entry in prow_config.yaml usually corresponds to a different configuration e.g. "GCP with IAP" vs. "GCP with basic auth"
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So worflow name is a natural grouping
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For each test run PROW defines several variables that pass useful information to your job.
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The list of variables is defined in the prow docs.
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These variables are often used to assign unique names to each test run to ensure isolation (e.g. by appending the BUILD_NUMBER)
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The prow variables are passed via ksonnet parameter
prow_envto your workflows-
You can copy the macros defined in util.libsonnet to parse the ksonnet parameter into a jsonnet map that can be used in your workflow.
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Important Always define defaults for the prow variables in the dict e.g. like
local prowDict = { BUILD_ID: "notset", BUILD_NUMBER: "notset", REPO_OWNER: "notset", REPO_NAME: "notset", JOB_NAME: "notset", JOB_TYPE: "notset", PULL_NUMBER: "notset", } + util.listOfDictToMap(prowEnv);- This prevents jsonnet from failing in a hard to debug way in the event that you try to access a key which is not in the map.
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Guard against long names by truncating the name and using the BUILD_ID to ensure the name remains unique e.g
local name = std.substr(params.name, 0, std.min(58, std.lenght(params.name))) + "-" + prowDict["BUILD_ID"];-
Argo workflow names need to be less than 63 characters because they are used as pod labels
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BUILD_ID are unique for each run per repo; we suggest reserving 5 characters for the BUILD_ID.
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Argo workflows should have standard labels corresponding to prow variables; for example
labels: prowDict + { workflow_template: "code_search", },-
This makes it easy to query for Argo workflows based on prow job info.
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In addition the convention is to use the following labels
- workflow_template: The name of the ksonnet component from which the workflow is created.
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The templates for the individual steps in the argo workflow should also have standard labels
labels: prowDict + { step_name: stepName, workflow_template: "code_search", workflow: workflowName, },- step_name: Name of the step (e.g. what shows up in the Argo graph)
- workflow_template: The name of the ksonnet component from which the workflow is created.
- workflow: The name of the Argo workflow that owns this pod.
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Following the above conventions make it very easy to get logs for specific steps
kubectl logs -l step_name=checkout,REPO_OWNER=kubeflow,REPO_NAME=examples,BUILD_ID=0104-064201 -c main
Tests often need a K8s/Kubeflow deployment on which to create resources and run various tests.
Depending on the change being tested
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The test might need exclusive access to a Kubeflow/Kubernetes cluster
- e.g. Testing a change to a custom resource usually requires exclusive access to a K8s cluster because only one CRD and controller can be installed per cluster. So trying to test two different changes to an operator (e.g. tf-operator) on the same cluster is not good.
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The test might need a Kubeflow/K8s deployment but doesn't need exclusive access
- e.g. When running tests for Kubeflow examples we can isolate each test using namespaces or other mechanisms.
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If the test needs exclusive access to the Kubernetes cluster then there should be a step in the workflow that creates a KubeConfig file to talk to the cluster.
- e.g. E2E tests for most operators should probably spin up a new Kubeflow cluster
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If the test just needs a known version of Kubeflow (e.g. master or v0.4) then it should use one of the test clusters in project kubeflow-ci for this
- The infrasture to support this is not fully implemented see kubeflow/testing#95 and kubeflow/testing#273
To connect to the cluster:
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The Argo workflow should have a step that configures the
KUBE_CONFIGfile to talk to the cluster- e.g. by running
gcloud container clusters get-credentials
- e.g. by running
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The Kubeconfig file should be stored in the NFS test directory so it can be used in subsequent steps
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Set the environment variable
KUBE_CONFIGon your steps to use the KubeConfig file
An NFS volume is used to create a shared filesystem between steps in the workflow.
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Your Argo workflows should use a PVC claim to mount the NFS filesystem into each step
- The current PVC name is
nfs-external - This should be a parameter to allow different PVC names in different environments.
- The current PVC name is
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Use the following directory structure
${MOUNT_POINT}/${WORKFLOW_NAME} /src /${REPO_ORG}/${REPO_NAME} /outputs /outputs/artifacts- MOUNT_PATH: Location inside the pod where the NFS volume is mounted
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WORKFLOW_NAME: The name of the Argo workflow
- Each Argo workflow job has a unique name (enforced by APIServer)
- So using WORKFLOW_NAME as root for all results associated with a particular job ensures there are no conflicts
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/src: Any repositories that are checked out should be checked out here
- Each repo should be checked out to the sub-directory ${REPO_ORG}/${REPO_NAME}
- /outputs: Any files that should be sync'd to GCS for Gubernator should be written here
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The Docker image used by the Argo steps should be a ksonnet parameter
stepImage -
The Docker image should use an immutable image tag e.g
gcr.io/kubeflow-ci/test-worker:v20181017-bfeaaf5-dirty-4adcd0- This ensures tests don't break if someone pushes a new test image
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The ksonnet parameter
stepImageshould be set in theprow_config.yamlfile defining the E2E tests- This makes it easy to update all the workflows to use some new image.
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A common runtime is defined here and published to gcr.io/kubeflow-ci/test-worker
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The first step in the Argo workflow should checkout out the source repos to the NFS directory
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Use checkout.sh to checkout the repos
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checkout.sh environment variable
EXTRA_REPOSallows checking out additional repositories in addition to the repository that triggered the pre/post submit test- This allows your test to use source code located in a different repository
- You can specify whether to checkout the repository at HEAD or pin to a specific commit
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Most E2E tests will want to checkout kubeflow/testing in order to use various test utilities
There are lots of different ways to build Docker images (e.g. GCB, Docker in Docker). Current recommendation is
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Define a Makefile to provide a convenient way to invoke Docker builds
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Using Google Container Builder (GCB) to run builds in Kubeflow's CI system generally works better than alternatives (e.g. Docker in Docker, Kaniko)
- Your Makefile can have alternative rules to support building locally via Docker for developers
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Use jsonnet if needed to define GCB workflows
- Example jsonnet file and associated Makefile
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Makefile should expose variables for the following
- Registry where image is pushed
- TAG used for the images
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Argo workflow should define the image paths and tag so that subsequent steps can use the newly built images
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