Add e2e failure debugging guidance (#1469)

* Add e2e failure debugging guidance

* Update docs/testing/e2e_debugging.md

Co-authored-by: Yuan Tang <[email protected]>

* Address review feedbacks

Co-authored-by: Yuan Tang <[email protected]>

docs/testing/e2e_debugging.md

@@ -0,0 +1,120 @@
+# How to debug an E2E test for Kubeflow Training Operator
+
+[E2E Testing](./e2e_testing.md) gives an overview of writing e2e tests. This guidance concentrates more on the e2e failure debugging.
+
+
+## Prerequsite
+
+1. Install python 3.7
+
+2. Clone `kubeflow/testing` repo under `$GOPATH/src/kubeflow/`
+
+3. Install [ksonnet](https://ksonnet.io/)
+
+```
+wget https://github.com/ksonnet/ksonnet/releases/download/v0.13.1/ks_0.13.1_linux_amd64.tar.gz
+tar -xvzf ks_0.13.1_linux_amd64.tar.gz
+sudo cp ks_0.13.1_linux_amd64/ks /usr/local/bin/ks-13
+```
+> We would like to deprecate `ksonnet` but may takes some time. Feel free to pick up [the issue](https://github.com/kubeflow/training-operator/issues/1468) if you are interested in it. 
+> If your platform is darwin or windows, feel free to download binaries in [ksonnet v0.13.1](https://github.com/ksonnet/ksonnet/releases/tag/v0.13.1)
+
+4. Deploy HEAD training operator version in your environment
+
+```
+IMG=kubeflow/training-operator:e2e-debug-prid make docker-build
+
+# Optional - load image into kind cluster if you are using kind
+kind load docker-image kubeflow/training-operator:e2e-debug-1462
+
+kubectl set image deployment.v1.apps/training-operator training-operator=kubeflow/training-operator:e2e-debug-1462
+```
+
+## Run E2E Tests locally
+
+1. Set environments
+```
+export KUBEFLOW_PATH=$GOPATH/src/github.com/kubeflow
+export KUBEFLOW_TRAINING_REPO=$KUBEFLOW_PATH/training-operator
+export KUBEFLOW_TESTING_REPO=$KUBEFLOW_PATH/testing
+export PYTHONPATH=$KUBEFLOW_TRAINING_REPO:$KUBEFLOW_TRAINING_REPO/py:$KUBEFLOW_TESTING_REPO/py:$KUBEFLOW_TRAINING_REPO/sdk/python
+```
+
+
+2. Install python dependencies
+```
+pip3 install -r $KUBEFLOW_TESTING_REPO/py/kubeflow/testing/requirements.txt
+```
+
+> Note: if you have meet problem install requirement, you may need to `sudo apt-get install libffi-dev`. Feel free to share error logs if you don't know how to handle it.
+
+
+3. Run Tests
+```
+# enter the ksonnet app to run tests
+cd $KUBEFLOW_TRAINING_REPO/test/workflows
+
+# run individual test that failed in the presubmit job.
+python3 -m kubeflow.tf_operator.pod_names_validation_tests --app_dir=$KUBEFLOW_TRAINING_REPO/test/workflows --params=name=pod-names-validation-tests-v1,namespace=kubeflow --tfjob_version=v1 --num_trials=1 --artifacts_path=/tmp/output/artifacts
+python3 -m kubeflow.tf_operator.cleanpod_policy_tests --app_dir=$KUBEFLOW_TRAINING_REPO/test/workflows --params=name=cleanpod-policy-tests-v1,namespace=kubeflow --tfjob_version=v1 --num_trials=1 --artifacts_path=/tmp/output/artifacts
+python3 -m kubeflow.tf_operator.simple_tfjob_tests  --app_dir=$KUBEFLOW_TRAINING_REPO/test/workflows --params=name=simple-tfjob-tests-v1,namespace=kubeflow --tfjob_version=v1 --num_trials=2 --artifacts_path=/tmp/output/artifact
+```
+
+
+## Check results
+
+You can either check logs or check results in `/tmp/output/artifact`. 
+
+```
+$ ls -al /tmp/output/artifact
+junit_test_simple_tfjob_cpu.xml
+
+$ cat /tmp/output/artifact/junit_test_simple_tfjob_cpu.xml
+<testsuite failures="0" tests="1" time="659.5505294799805"><testcase classname="SimpleTfJobTests" name="simple-tfjob-tests-v1" time="659.5505294799805" /></testsuite>
+```
+
+## Common issues
+
+1. ksonnet is not installed 
+
+```
+ERROR|2021-11-16T03:06:06|/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/test_runner.py|57| There was a problem running the job; Exception [Errno 2] No such file or directory: 'ks-13': 'ks-13'
+Traceback (most recent call last):
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/test_runner.py", line 38, in run_test
+    test_func()
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/pod_names_validation_tests.py", line 53, in test_pod_names
+    self.params)
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/util.py", line 579, in setup_ks_app
+    cwd=app_dir)
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/testing/py/kubeflow/testing/util.py", line 59, in run
+    command, cwd=cwd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
+  File "/usr/local/lib/python3.7/subprocess.py", line 775, in __init__
+    restore_signals, start_new_session)
+  File "/usr/local/lib/python3.7/subprocess.py", line 1522, in _execute_child
+    raise child_exception_type(errno_num, err_msg, err_filename)
+FileNotFoundError: [Errno 2] No such file or directory: 'ks-13': 'ks-13'
+```
+
+Please check `Prerequsite` section to install ksonnet.
+
+
+2. TypeError: load() missing 1 required positional argument: 'Loader'
+
+```
+ERROR|2021-11-16T03:04:12|/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/test_runner.py|57| There was a problem running the job; Exception load() missing 1 required positional argument: 'Loader'
+Traceback (most recent call last):
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/test_runner.py", line 38, in run_test
+    test_func()
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/training-operator/py/kubeflow/tf_operator/pod_names_validation_tests.py", line 51, in test_pod_names
+    ks_cmd = ks_util.get_ksonnet_cmd(self.app_dir)
+  File "/home/jiaxin.shan/go/src/github.com/kubeflow/testing/py/kubeflow/testing/ks_util.py", line 47, in get_ksonnet_cmd
+    results = yaml.load(app_yaml)
+TypeError: load() missing 1 required positional argument: 'Loader'
+```
+
+This is the pyyaml compatibility issue. Please check if you are using pyyaml==6.0.0. If so, downgrade to `5.4.1` instead.
+
+```
+pip3 uninstall pyyaml
+pip3 install pyyaml==5.4.1 --user
+```

docs/testing/e2e_testing.md

@@ -4,7 +4,7 @@ The E2E tests for Kubeflow Training operator are implemented as Argo workflows.
 about Argo (not required for understanding the rest of this document), please take a look at
 [this link](https://github.com/kubeflow/testing/blob/master/README.md).
 
-Test results can be monitored at the [Prow dashboard](https://prow.k8s.io/?repo=kubeflow%2Ftraining-operator).
+Test results can be monitored at the [Prow dashboard](http://prow.kubeflow-testing.com/?repo=kubeflow%2Ftraining-operator).
 
 At a high level, the E2E test suites are structured as Python test classes. Each test class contains
 one or more tests. A test typically runs the following: