Add test to create PyTorchJob from func

.github/workflows/integration-tests.yaml

@@ -67,7 +67,7 @@ jobs:
           sudo rm -rf /usr/local/lib/android
           sudo rm -rf /usr/local/share/powershell
           sudo rm -rf /usr/share/swift
-          
+
           echo "Disk usage after cleanup:"
           df -h
 
@@ -109,7 +109,7 @@ jobs:
       - name: Run tests
         run: |
           pip install pytest
-          python3 -m pip install -e sdk/python; pytest sdk/python/test --log-cli-level=info --namespace=default
+          python3 -m pip install -e sdk/python; pytest -s sdk/python/test --log-cli-level=debug --namespace=default
         env:
           GANG_SCHEDULER_NAME: ${{ matrix.gang-scheduler-name }}
 

sdk/python/kubeflow/training/api/training_client_test.py

@@ -161,6 +161,19 @@ def __init__(self, kind) -> None:
         {"job": create_job(), "namespace": "test"},
         "success",
     ),
+    (
+        "valid flow to create job from func",
+        {
+            "name": "test-job",
+            "namespace": "test",
+            "train_func": lambda: print("Test Training Function"),
+            "base_image": "docker.io/test-training",
+            "num_worker_replicas": "3",
+            "packages_to_install": ["boto3==1.34.14"],
+            "pip_index_url": "https://pypi.custom.com/simple",
+        },
+        "success",
+    ),
 ]
 
 

sdk/python/test/e2e/test_e2e_mpijob.py

@@ -36,7 +36,7 @@
 from test.e2e.constants import GANG_SCHEDULERS, NONE_GANG_SCHEDULERS
 
 logging.basicConfig(format="%(message)s")
-logging.getLogger().setLevel(logging.INFO)
+logging.getLogger("kubeflow.training.api.training_client").setLevel(logging.DEBUG)
 
 TRAINING_CLIENT = TrainingClient(job_kind=constants.MPIJOB_KIND)
 JOB_NAME = "mpijob-mxnet-ci-test"

sdk/python/test/e2e/test_e2e_mxjob.py

@@ -37,7 +37,7 @@
 from test.e2e.constants import GANG_SCHEDULERS, NONE_GANG_SCHEDULERS
 
 logging.basicConfig(format="%(message)s")
-logging.getLogger().setLevel(logging.INFO)
+logging.getLogger("kubeflow.training.api.training_client").setLevel(logging.DEBUG)
 
 TRAINING_CLIENT = TrainingClient(job_kind=constants.MXJOB_KIND)
 JOB_NAME = "mxjob-mnist-ci-test"

sdk/python/test/e2e/test_e2e_paddlejob.py

@@ -36,7 +36,7 @@
 from test.e2e.constants import GANG_SCHEDULERS, NONE_GANG_SCHEDULERS
 
 logging.basicConfig(format="%(message)s")
-logging.getLogger().setLevel(logging.INFO)
+logging.getLogger("kubeflow.training.api.training_client").setLevel(logging.DEBUG)
 
 TRAINING_CLIENT = TrainingClient(job_kind=constants.PADDLEJOB_KIND)
 JOB_NAME = "paddlejob-cpu-ci-test"

sdk/python/test/e2e/test_e2e_pytorchjob.py

@@ -36,10 +36,9 @@
 from test.e2e.constants import GANG_SCHEDULERS, NONE_GANG_SCHEDULERS
 
 logging.basicConfig(format="%(message)s")
-logging.getLogger().setLevel(logging.INFO)
+logging.getLogger("kubeflow.training.api.training_client").setLevel(logging.DEBUG)
 
 TRAINING_CLIENT = TrainingClient(job_kind=constants.PYTORCHJOB_KIND)
-JOB_NAME = "pytorchjob-mnist-ci-test"
 CONTAINER_NAME = "pytorch"
 GANG_SCHEDULER_NAME = os.getenv(TEST_GANG_SCHEDULER_NAME_ENV_KEY, "")
 
@@ -49,6 +48,7 @@
     reason="For gang-scheduling",
 )
 def test_sdk_e2e_with_gang_scheduling(job_namespace):
+    JOB_NAME = "pytorchjob-gang-scheduling"
     container = generate_container()
 
     master = KubeflowOrgV1ReplicaSpec(
@@ -81,12 +81,17 @@ def test_sdk_e2e_with_gang_scheduling(job_namespace):
 
     unschedulable_pytorchjob = generate_pytorchjob(
         job_namespace,
+        JOB_NAME,
         master,
         worker,
         KubeflowOrgV1SchedulingPolicy(min_available=10),
     )
     schedulable_pytorchjob = generate_pytorchjob(
-        job_namespace, master, worker, KubeflowOrgV1SchedulingPolicy(min_available=2)
+        job_namespace,
+        JOB_NAME,
+        master,
+        worker,
+        KubeflowOrgV1SchedulingPolicy(min_available=2),
     )
 
     TRAINING_CLIENT.create_job(job=unschedulable_pytorchjob, namespace=job_namespace)
@@ -120,6 +125,7 @@ def test_sdk_e2e_with_gang_scheduling(job_namespace):
     reason="For plain scheduling",
 )
 def test_sdk_e2e(job_namespace):
+    JOB_NAME = "pytorchjob-e2e"
     container = generate_container()
 
     master = KubeflowOrgV1ReplicaSpec(
@@ -144,7 +150,7 @@ def test_sdk_e2e(job_namespace):
         ),
     )
 
-    pytorchjob = generate_pytorchjob(job_namespace, master, worker)
+    pytorchjob = generate_pytorchjob(job_namespace, JOB_NAME, master, worker)
 
     TRAINING_CLIENT.create_job(job=pytorchjob, namespace=job_namespace)
     logging.info(f"List of created {TRAINING_CLIENT.job_kind}s")
@@ -161,16 +167,62 @@ def test_sdk_e2e(job_namespace):
     TRAINING_CLIENT.delete_job(JOB_NAME, job_namespace)
 
 
+@pytest.mark.skipif(
+    GANG_SCHEDULER_NAME in GANG_SCHEDULERS,
+    reason="For plain scheduling",
+)
+def test_sdk_e2e_create_from_func(job_namespace):
+    JOB_NAME = "pytorchjob-from-func"
+
+    def train_func():
+        import time
+
+        for i in range(10):
+            print(f"Start training for Epoch {i}")
+            time.sleep(1)
+
+    num_workers = 1
+
+    TRAINING_CLIENT.create_job(
+        name=JOB_NAME,
+        namespace=job_namespace,
+        train_func=train_func,
+        num_worker_replicas=num_workers,
+    )
+
+    logging.info(f"List of created {TRAINING_CLIENT.job_kind}s")
+    logging.info(TRAINING_CLIENT.list_jobs(job_namespace))
+
+    try:
+        utils.verify_job_e2e(TRAINING_CLIENT, JOB_NAME, job_namespace, wait_timeout=900)
+    except Exception as e:
+        utils.print_job_results(TRAINING_CLIENT, JOB_NAME, job_namespace)
+        TRAINING_CLIENT.delete_job(JOB_NAME, job_namespace)
+        raise Exception(f"PyTorchJob create from function E2E fails. Exception: {e}")
+
+    # Verify that PyTorchJob has correct pods.
+    pod_names = TRAINING_CLIENT.get_job_pod_names(
+        name=JOB_NAME, namespace=job_namespace
+    )
+
+    if len(pod_names) != num_workers or f"{JOB_NAME}-worker-0" not in pod_names:
+        raise Exception(f"PyTorchJob has incorrect pods: {pod_names}")
+
+    utils.print_job_results(TRAINING_CLIENT, JOB_NAME, job_namespace)
+    TRAINING_CLIENT.delete_job(JOB_NAME, job_namespace)
+
+
 def generate_pytorchjob(
     job_namespace: str,
+    job_name: str,
     master: KubeflowOrgV1ReplicaSpec,
     worker: KubeflowOrgV1ReplicaSpec,
     scheduling_policy: Optional[KubeflowOrgV1SchedulingPolicy] = None,
 ) -> KubeflowOrgV1PyTorchJob:
     return KubeflowOrgV1PyTorchJob(
         api_version=constants.API_VERSION,
         kind=constants.PYTORCHJOB_KIND,
-        metadata=V1ObjectMeta(name=JOB_NAME, namespace=job_namespace),
+        metadata=V1ObjectMeta(name=job_name, namespace=job_namespace),
         spec=KubeflowOrgV1PyTorchJobSpec(
             run_policy=KubeflowOrgV1RunPolicy(
                 clean_pod_policy="None",

sdk/python/test/e2e/test_e2e_tfjob.py

@@ -36,7 +36,8 @@
 from test.e2e.constants import GANG_SCHEDULERS, NONE_GANG_SCHEDULERS
 
 logging.basicConfig(format="%(message)s")
-logging.getLogger().setLevel(logging.INFO)
+logging.getLogger("kubeflow.training.api.training_client").setLevel(logging.DEBUG)
+
 
 TRAINING_CLIENT = TrainingClient(job_kind=constants.TFJOB_KIND)
 JOB_NAME = "tfjob-mnist-ci-test"

sdk/python/test/e2e/test_e2e_xgboostjob.py

@@ -36,7 +36,7 @@
 from test.e2e.constants import GANG_SCHEDULERS, NONE_GANG_SCHEDULERS
 
 logging.basicConfig(format="%(message)s")
-logging.getLogger().setLevel(logging.INFO)
+logging.getLogger("kubeflow.training.api.training_client").setLevel(logging.DEBUG)
 
 TRAINING_CLIENT = TrainingClient(job_kind=constants.XGBOOSTJOB_KIND)
 JOB_NAME = "xgboostjob-iris-ci-test"