diff --git a/.github/workflows/test-python.yaml b/.github/workflows/test-python.yaml index 84651751..90b68f95 100644 --- a/.github/workflows/test-python.yaml +++ b/.github/workflows/test-python.yaml @@ -16,6 +16,14 @@ jobs: with: python-version: '3.9' - - name: Verify run: make verify + + - name: Run Tests + run: | + make test-python report=xml + + - name: Upload coverage to Coveralls + uses: coverallsapp/github-action@v2 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} # Pass the GITHUB_TOKEN diff --git a/.gitignore b/.gitignore index 3acf5463..ae9c006e 100644 --- a/.gitignore +++ b/.gitignore @@ -9,4 +9,9 @@ __debug_bin # Python cache files __pycache__/ *.egg-info/ + +.coverage bin + +coverage.xml +htmlcov/ diff --git a/Makefile b/Makefile index ea3ff1d9..75e195af 100644 --- a/Makefile +++ b/Makefile @@ -21,6 +21,7 @@ SHELL = /usr/bin/env bash -o pipefail PROJECT_DIR := $(shell dirname $(abspath $(lastword $(MAKEFILE_LIST)))) PY_DIR := $(PROJECT_DIR)/python +VENV_DIR := $(PROJECT_DIR)/.venv ##@ General @@ -38,7 +39,6 @@ PY_DIR := $(PROJECT_DIR)/python help: ## Display this help. @awk 'BEGIN {FS = ":.*##"; printf "\nUsage:\n make \033[36m\033[0m\n"} /^[a-zA-Z_0-9-]+:.*?##/ { printf " \033[36m%-15s\033[0m %s\n", $$1, $$2 } /^##@/ { printf "\n\033[1m%s\033[0m\n", substr($$0, 5) } ' $(MAKEFILE_LIST) - #UV := $(shell which uv) .PHONY: uv @@ -52,14 +52,28 @@ uv: ## Install UV ruff: ## Install Ruff @uvx ruff --help &> /dev/null || uv tool install ruff - .PHONY: verify -verify: uv ruff ## install all required tools +verify: uv uv-venv ruff ## install all required tools @cd $(PY_DIR) && uv lock --check @cd $(PY_DIR) && uvx ruff check --show-fixes -.PHONY: test-python -test-python: ## Run Python unit test. - uv install --dev - PYTHONPATH=$(PROJECT_DIR) pytest ./python/kubeflow +.PHONY: uv-venv +uv-venv: + @if [ ! -d "$(VENV_DIR)" ]; then \ + echo "Creating uv virtual environment in $(VENV_DIR)..."; \ + uv venv; \ + else \ + echo "uv virtual environment already exists in $(VENV_DIR)."; \ + fi + # make test-unit will produce html coverage by default. Run with `make test-unit report=xml` to produce xml report. +.PHONY: test-python +test-python: uv-venv + @uv pip install "./python[test]" + @uv run coverage run --source=kubeflow.trainer.api.trainer_client,kubeflow.trainer.utils.utils -m pytest ./python/kubeflow/trainer/api/trainer_client_test.py + @uv run coverage report -m kubeflow/trainer/api/trainer_client.py kubeflow/trainer/utils/utils.py +ifeq ($(report),xml) + @uv run coverage xml +else + @uv run coverage html +endif diff --git a/python/kubeflow/trainer/api/trainer_client_test.py b/python/kubeflow/trainer/api/trainer_client_test.py new file mode 100644 index 00000000..e3f56c69 --- /dev/null +++ b/python/kubeflow/trainer/api/trainer_client_test.py @@ -0,0 +1,898 @@ +# Copyright 2025 The Kubeflow Authors. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Unit tests for the TrainerClient class in the Kubeflow Trainer SDK. + +This module uses pytest and unittest.mock to simulate Kubernetes API interactions. +It tests TrainerClient's behavior across job listing, resource creation etc +""" +import datetime +import multiprocessing +import random +import string +import uuid +from dataclasses import asdict, dataclass, field +from typing import Any, Dict, Optional, Type +from unittest.mock import Mock, patch + +import pytest +from kubeflow.trainer import TrainerClient +from kubeflow.trainer.constants import constants +from kubeflow.trainer.types import types +from kubeflow.trainer.utils import utils +from kubeflow_trainer_api import models + + +@dataclass +class TestCase: + name: str + expected_status: str + config: Dict[str, Any] = field(default_factory=dict) + expected_output: Optional[Any] = None + expected_error: Optional[Type[Exception]] = None + __test__ = False + + +# -------------------------- +# Constants for test scenarios +# -------------------------- +TIMEOUT = "timeout" +RUNTIME = "runtime" +INVALID_RUNTIME = "invalid_runtime" +SUCCESS = "success" +FAILED = "Failed" +CREATED = "Created" +RUNNING = "Running" +RESTARTING = "Restarting" +NO_PODS = "no_pods" +SUCCEEDED = "Succeeded" +INVALID = "invalid" +DEFAULT_NAMESPACE = "default" +PYTORCH = "pytorch" +MOCK_POD_OBJ = "mock_pod_obj" +FAIL_LOGS = "fail_logs" +TORCH_DISTRIBUTED = "torch-distributed" +LIST_RUNTIMES = "list_runtimes" +BASIC_TRAIN_JOB_NAME = "basic-job" +TRAIN_JOBS = "trainjobs" +TRAIN_JOB_WITH_BUILT_IN_TRAINER = "train-job-with-built-in-trainer" +TRAIN_JOB_WITH_CUSTOM_TRAINER = "train-job-with-custom-trainer" + + +# -------------------------- +# Fixtures +# -------------------------- + + +@pytest.fixture +def training_client(request): + """Provide a TrainerClient with mocked Kubernetes APIs.""" + with patch("kubernetes.config.load_kube_config", return_value=None), patch( + "kubernetes.client.CustomObjectsApi", + return_value=Mock( + create_namespaced_custom_object=Mock(side_effect=conditional_error_handler), + patch_namespaced_custom_object=Mock(side_effect=conditional_error_handler), + delete_namespaced_custom_object=Mock(side_effect=conditional_error_handler), + get_namespaced_custom_object=Mock( + side_effect=get_namespaced_custom_object_response + ), + get_cluster_custom_object=Mock( + side_effect=get_cluster_custom_object_response + ), + list_namespaced_custom_object=Mock( + side_effect=list_namespaced_custom_object_response + ), + list_cluster_custom_object=Mock(side_effect=list_cluster_custom_object), + ), + ), patch( + "kubernetes.client.CoreV1Api", + return_value=Mock( + list_namespaced_pod=Mock(side_effect=list_namespaced_pod_response), + read_namespaced_pod_log=Mock(side_effect=mock_read_namespaced_pod_log), + ), + ): + yield TrainerClient() + + +# -------------------------- +# Mock Handlers +# -------------------------- + + +def conditional_error_handler(*args, **kwargs): + """Raise simulated errors based on resource name.""" + if args[2] == TIMEOUT: + raise multiprocessing.TimeoutError() + elif args[2] == RUNTIME: + raise RuntimeError() + + +def list_namespaced_pod_response(*args, **kwargs): + """Return mock pod list response.""" + pod_list = get_mock_pod_list() + mock_thread = Mock() + mock_thread.get.return_value = pod_list + return mock_thread + + +def get_mock_pod_list(): + """Create a mocked Kubernetes PodList object with pods for different training steps.""" + return models.IoK8sApiCoreV1PodList( + items=[ + # Dataset initializer pod + models.IoK8sApiCoreV1Pod( + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + name="dataset-initializer-pod", + namespace=DEFAULT_NAMESPACE, + labels={ + constants.JOBSET_NAME_LABEL: BASIC_TRAIN_JOB_NAME, + constants.JOBSET_RJOB_NAME_LABEL: constants.DATASET_INITIALIZER, + constants.JOB_INDEX_LABEL: "0", + }, + ), + spec=models.IoK8sApiCoreV1PodSpec( + containers=[ + models.IoK8sApiCoreV1Container( + name=constants.DATASET_INITIALIZER, + image="dataset-initializer:latest", + command=["python", "-m", "dataset_initializer"], + ) + ] + ), + status=models.IoK8sApiCoreV1PodStatus(phase="Running"), + ), + # Model initializer pod + models.IoK8sApiCoreV1Pod( + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + name="model-initializer-pod", + namespace=DEFAULT_NAMESPACE, + labels={ + constants.JOBSET_NAME_LABEL: BASIC_TRAIN_JOB_NAME, + constants.JOBSET_RJOB_NAME_LABEL: constants.MODEL_INITIALIZER, + constants.JOB_INDEX_LABEL: "0", + }, + ), + spec=models.IoK8sApiCoreV1PodSpec( + containers=[ + models.IoK8sApiCoreV1Container( + name=constants.MODEL_INITIALIZER, + image="model-initializer:latest", + command=["python", "-m", "model_initializer"], + ) + ] + ), + status=models.IoK8sApiCoreV1PodStatus(phase="Running"), + ), + # Training node pod + models.IoK8sApiCoreV1Pod( + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + name="node-0-pod", + namespace=DEFAULT_NAMESPACE, + labels={ + constants.JOBSET_NAME_LABEL: BASIC_TRAIN_JOB_NAME, + constants.JOBSET_RJOB_NAME_LABEL: constants.NODE, + constants.JOB_INDEX_LABEL: "0", + }, + ), + spec=models.IoK8sApiCoreV1PodSpec( + containers=[ + models.IoK8sApiCoreV1Container( + name=constants.NODE, + image="trainer:latest", + command=["python", "-m", "trainer"], + resources=get_resource_requirements(), + ) + ] + ), + status=models.IoK8sApiCoreV1PodStatus(phase="Running"), + ), + ] + ) + + +def get_resource_requirements() -> models.IoK8sApiCoreV1ResourceRequirements: + """Create a mock ResourceRequirements object for testing.""" + return models.IoK8sApiCoreV1ResourceRequirements( + requests={ + "nvidia.com/gpu": models.IoK8sApimachineryPkgApiResourceQuantity("1"), + "memory": models.IoK8sApimachineryPkgApiResourceQuantity("2Gi"), + }, + limits={ + "nvidia.com/gpu": models.IoK8sApimachineryPkgApiResourceQuantity("1"), + "memory": models.IoK8sApimachineryPkgApiResourceQuantity("4Gi"), + }, + ) + + +def add_custom_trainer_to_job( + train_job: models.TrainerV1alpha1TrainJob, +) -> models.TrainerV1alpha1TrainJob: + """ + Add a custom trainer configuration to the train job. + """ + trainer_crd = models.TrainerV1alpha1Trainer( + command=["bash", "-c"], + args=[ + '\nif ! [ -x "$(command -v pip)" ]; then\n python -m ensurepip ' + "|| python -m ensurepip --user || apt-get install python-pip" + "\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1 python -m pip install --quiet" + " --no-warn-script-location --index-url https://pypi.org/simple " + "torch numpy \n\nread -r -d '' SCRIPT << EOM\n\nfunc=lambda: " + 'print("Hello World"),\n\n(' + "{'learning_rate': 0.001, 'batch_size': 32})\n\nEOM\nprintf \"%s\" " + '"$SCRIPT" > "trainer_client_test.py"\ntorchrun "trainer_client_test.py"' + ], + num_nodes=2, + ) + + train_job.spec.trainer = trainer_crd + return train_job + + +def add_built_in_trainer_to_job( + train_job: models.TrainerV1alpha1TrainJob, +) -> models.TrainerV1alpha1TrainJob: + """ + Add a built-in trainer configuration to the train job. + """ + trainer_crd = models.TrainerV1alpha1Trainer( + args=["batch_size=2", "epochs=2", "loss=Loss.CEWithChunkedOutputLoss"], + command=["tune", "run"], + numNodes=2, + ) + train_job.spec.trainer = trainer_crd + return train_job + + +def get_train_job( + train_job_name: str = BASIC_TRAIN_JOB_NAME, + runtime_name: str = TORCH_DISTRIBUTED, + add_built_in_trainer: bool = False, + add_custom_trainer: bool = False, +) -> models.TrainerV1alpha1TrainJob: + """ + Create a mock TrainJob object with optional trainer configurations. + """ + train_job = models.TrainerV1alpha1TrainJob( + apiVersion=constants.API_VERSION, + kind=constants.TRAINJOB_KIND, + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta(name=train_job_name), + spec=models.TrainerV1alpha1TrainJobSpec( + runtimeRef=models.TrainerV1alpha1RuntimeRef(name=runtime_name), + ), + ) + + if add_built_in_trainer: + train_job = add_built_in_trainer_to_job(train_job) + if add_custom_trainer: + train_job = add_custom_trainer_to_job(train_job) + + return train_job + + +def get_cluster_custom_object_response(*args, **kwargs): + """Return a mocked ClusterTrainingRuntime object.""" + if args[3] == TIMEOUT: + raise multiprocessing.TimeoutError() + if args[3] == RUNTIME: + raise RuntimeError() + if args[2] == constants.CLUSTER_TRAINING_RUNTIME_PLURAL: + result = create_cluster_training_runtime() + + result = normalize_model(result, models.TrainerV1alpha1ClusterTrainingRuntime) + mock_thread = Mock() + mock_thread.get.return_value = result + return mock_thread + + +def get_namespaced_custom_object_response(*args, **kwargs): + """Return a mocked TrainJob object.""" + if args[2] == TIMEOUT or args[4] == TIMEOUT: + raise multiprocessing.TimeoutError() + if args[2] == RUNTIME or args[4] == RUNTIME: + raise RuntimeError() + if args[3] == TRAIN_JOBS: # TODO: review this. + job = add_status(create_train_job(train_job_name=args[4])) + + mock_thread = Mock() + mock_thread.get.return_value = job + return mock_thread + + +def add_status( + train_job: models.TrainerV1alpha1TrainJob, +) -> models.TrainerV1alpha1TrainJob: + """ + Add status information to the train job. + """ + # Set initial status to Created + status = models.TrainerV1alpha1TrainJobStatus( + conditions=[ + models.IoK8sApimachineryPkgApisMetaV1Condition( + type="Complete", + status="True", + lastTransitionTime=datetime.datetime.now(), + reason="JobCompleted", + message="Job completed successfully", + ) + ] + ) + train_job.status = status + return train_job + + +def list_namespaced_custom_object_response(*args, **kwargs): + """Return a list of mocked TrainJob objects.""" + mock_thread = Mock() + + if args[2] == TIMEOUT: + raise multiprocessing.TimeoutError() + if args[2] == RUNTIME: + raise RuntimeError() + if args[2] == BASIC_TRAIN_JOB_NAME: + return_value = { + "items": [create_train_job(train_job_name=BASIC_TRAIN_JOB_NAME)] + } + if args[3] == TRAIN_JOBS: + return_value = build_train_job_list() + + mock_thread.get.return_value = return_value + return mock_thread + + +def build_train_job_list() -> models.TrainerV1alpha1TrainJobList: + """Build a mock TrainJobList object with multiple TrainJob items.""" + train_job_list = models.TrainerV1alpha1TrainJobList( + apiVersion=constants.API_VERSION, + kind=constants.TRAINJOB_PLURAL, + items=[ + add_status(create_train_job(train_job_name="basic-job-1")), + add_status(create_train_job(train_job_name="basic-job-2")), + ], + ) + return normalize_model(train_job_list, models.TrainerV1alpha1TrainJobList) + + +def list_cluster_custom_object(*args, **kwargs): + """Return a generic mocked response for cluster object listing.""" + if args[2] == TIMEOUT: + raise multiprocessing.TimeoutError() + if args[2] == RUNTIME: + raise RuntimeError() + if args[2] == constants.CLUSTER_TRAINING_RUNTIME_PLURAL: + items = [ + create_cluster_training_runtime(name="runtime-1"), + create_cluster_training_runtime(name="runtime-2"), + ] + + runtime_list_obj = models.TrainerV1alpha1ClusterTrainingRuntimeList(items=items) + runtimes = normalize_model( + runtime_list_obj, models.TrainerV1alpha1ClusterTrainingRuntimeList + ) + + mock_thread = Mock() + mock_thread.get.return_value = runtimes + return mock_thread + + +def mock_read_namespaced_pod_log(*args, **kwargs): + """Simulate log retrieval from a pod.""" + if kwargs.get("namespace") == FAIL_LOGS: + raise Exception("Failed to read logs") + return "test log content" + + +def normalize_model(model_obj, model_class): + # Simulate real api behavior + # Converts model to raw dictionary, like a real API response + # Parses dict and ensures correct model instantiation and type validation + return model_class.from_dict(model_obj.to_dict()) + + +# -------------------------- +# Object Creators +# -------------------------- + + +def create_train_job( + train_job_name: str = random.choice(string.ascii_lowercase) + uuid.uuid4().hex[:11], + namespace: str = "default", + runtime: str = PYTORCH, + image: str = "pytorch/pytorch:latest", + initializer: Optional[types.Initializer] = None, + command: list = None, + args: list = None, +) -> models.TrainerV1alpha1TrainJob: + """Create a mock TrainJob object.""" + return models.TrainerV1alpha1TrainJob( + apiVersion=constants.API_VERSION, + kind=constants.TRAINJOB_KIND, + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + name=train_job_name, + namespace=namespace, + creationTimestamp=datetime.datetime(2025, 6, 1, 10, 30, 0), + ), + spec=models.TrainerV1alpha1TrainJobSpec( + runtimeRef=models.TrainerV1alpha1RuntimeRef(name=runtime), + trainer=None, + initializer=( + models.TrainerV1alpha1Initializer( + dataset=utils.get_dataset_initializer(initializer.dataset), + model=utils.get_model_initializer(initializer.model), + ) + if initializer + else None + ), + ), + ) + + +def create_cluster_training_runtime( + namespace: str = "default", + name: str = TORCH_DISTRIBUTED, +) -> models.TrainerV1alpha1ClusterTrainingRuntime: + """Create a mock ClusterTrainingRuntime object.""" + runtime = models.TrainerV1alpha1ClusterTrainingRuntime( + apiVersion=constants.API_VERSION, + kind="ClusterTrainingRuntime", + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + name=name, + namespace=namespace, + labels={ + "trainer.kubeflow.org/accelerator": "gpu-tesla-v100-16gb", + }, + ), + spec=models.TrainerV1alpha1TrainingRuntimeSpec( + mlPolicy=models.TrainerV1alpha1MLPolicy( + torch=models.TrainerV1alpha1TorchMLPolicySource( + num_proc_per_node=models.IoK8sApimachineryPkgUtilIntstrIntOrString( + 2 + ) + ), + numNodes=2, + ), + template=models.TrainerV1alpha1JobSetTemplateSpec( + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + name=name, + namespace=namespace, + ), + spec=models.JobsetV1alpha2JobSetSpec( + replicated_jobs=[get_replicated_job()] + ), + ), + ), + ) + return runtime + + +def get_replicated_job() -> models.TrainerV1alpha1ClusterTrainingRuntime: + return models.JobsetV1alpha2ReplicatedJob( + name="node", + replicas=1, + template=models.IoK8sApiBatchV1JobTemplateSpec( + metadata=models.IoK8sApimachineryPkgApisMetaV1ObjectMeta( + labels={"trainer.kubeflow.org/trainjob-ancestor-step": "trainer"} + ), + spec=models.IoK8sApiBatchV1JobSpec( + template=models.IoK8sApiCoreV1PodTemplateSpec( + spec=models.IoK8sApiCoreV1PodSpec(containers=[get_container()]) + ) + ), + ), + ) + + +def get_container() -> models.IoK8sApiCoreV1Container: + return models.IoK8sApiCoreV1Container( + name="node", + image="image", + command=["echo", "Hello World"], + resources=get_resource_requirements(), + ) + + +def create_runtime_type( + name: str = TORCH_DISTRIBUTED, +) -> types.Runtime: + """Create a mock Runtime object for testing.""" + return types.Runtime( + name=name, + pretrained_model=None, + trainer=types.Trainer( + trainer_type=types.TrainerType.CUSTOM_TRAINER, + framework=types.Framework.TORCH, + entrypoint=[constants.TORCH_ENTRYPOINT], + accelerator="gpu-tesla-v100-16gb", + accelerator_count=4, + ), + ) + + +def get_train_job_data_type( + train_job_name: str = BASIC_TRAIN_JOB_NAME, +) -> types.TrainJob: + """Create a mock TrainJob object with the expected structure for testing. + + Args: + train_job_name: Name of the training job + + Returns: + A TrainJob object with predefined structure for testing + """ + return types.TrainJob( + name=train_job_name, + creation_timestamp=datetime.datetime(2025, 6, 1, 10, 30, 0), + runtime=types.Runtime( + name=TORCH_DISTRIBUTED, + pretrained_model=None, + trainer=types.Trainer( + trainer_type=types.TrainerType.CUSTOM_TRAINER, + framework=types.Framework.TORCH, + entrypoint=["torchrun"], + accelerator="gpu-tesla-v100-16gb", + accelerator_count=4, + ), + ), + steps=[ + types.Step( + name="dataset-initializer", + status="Running", + pod_name="dataset-initializer-pod", + device="Unknown", + device_count="Unknown", + ), + types.Step( + name="model-initializer", + status="Running", + pod_name="model-initializer-pod", + device="Unknown", + device_count="Unknown", + ), + types.Step( + name="node-0", + status="Running", + pod_name="node-0-pod", + device="gpu", + device_count="1", + ), + ], + status="Succeeded", + ) + + +# -------------------------- +# Tests +# -------------------------- + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={}, + expected_output=create_runtime_type(), + ), + TestCase( + name="timeout error when getting runtime", + expected_status=FAILED, + config={"name": TIMEOUT}, + expected_error=TimeoutError, + ), + TestCase( + name="runtime error when getting runtime", + expected_status=FAILED, + config={"name": RUNTIME}, + expected_error=RuntimeError, + ), + ], +) +def test_get_runtime(training_client, test_case): + """Test TrainerClient.get_runtime with basic success path.""" + print("Executing test:", test_case.name) + try: + runtime = training_client.get_runtime( + test_case.config.get("name", TORCH_DISTRIBUTED) + ) + + assert test_case.expected_status == SUCCESS + assert isinstance(runtime, types.Runtime) + assert asdict(runtime) == asdict(test_case.expected_output) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={"name": LIST_RUNTIMES}, + expected_output=[ + create_runtime_type(name="runtime-1"), + create_runtime_type(name="runtime-2"), + ], + ), + ], +) +def test_list_runtimes(training_client, test_case): + """Test TrainerClient.list_runtimes with basic success path.""" + print("Executing test:", test_case.name) + try: + training_client.namespace = test_case.config.get("namespace", DEFAULT_NAMESPACE) + runtimes = training_client.list_runtimes() + + assert test_case.expected_status == SUCCESS + assert isinstance(runtimes, list) + assert all(isinstance(r, types.Runtime) for r in runtimes) + assert [asdict(r) for r in runtimes] == [ + asdict(r) for r in test_case.expected_output + ] + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={}, + expected_output=get_train_job(train_job_name=BASIC_TRAIN_JOB_NAME), + ), + TestCase( + name="valid flow with built in trainer", + expected_status=SUCCESS, + config={ + "trainer": types.BuiltinTrainer( + config=types.TorchTuneConfig( + num_nodes=2, + batch_size=2, + epochs=2, + loss=types.Loss.CEWithChunkedOutputLoss, + ) + ) + }, + expected_output=get_train_job( + train_job_name=TRAIN_JOB_WITH_BUILT_IN_TRAINER, + add_built_in_trainer=True, + ), + ), + TestCase( + name="valid flow with custom trainer", + expected_status=SUCCESS, + config={ + "trainer": types.CustomTrainer( + func=lambda: print("Hello World"), + func_args={"learning_rate": 0.001, "batch_size": 32}, + packages_to_install=["torch", "numpy"], + pip_index_url=constants.DEFAULT_PIP_INDEX_URL, + num_nodes=2, + ) + }, + expected_output=get_train_job( + train_job_name=TRAIN_JOB_WITH_CUSTOM_TRAINER, add_custom_trainer=True + ), + ), + TestCase( + name="timeout error when deleting job", + expected_status=FAILED, + config={ + "namespace": TIMEOUT, + }, + expected_error=TimeoutError, + ), + TestCase( + name="runtime error when deleting job", + expected_status=FAILED, + config={ + "namespace": RUNTIME, + }, + expected_error=RuntimeError, + ), + ], +) +def test_train(training_client, test_case): + """Test TrainerClient.train with basic success path.""" + print("Executing test:", test_case.name) + try: + training_client.namespace = test_case.config.get("namespace", DEFAULT_NAMESPACE) + test_case.config.pop( + "namespace", None + ) # None is the default value if key doesn't exist + train_job_name = training_client.train(**test_case.config) + + assert test_case.expected_status == SUCCESS + + # This is to get around the fact that the train job name is dynamically generated + # In the future name generation may be more deterministic, and we can revisit this approach + expected_output = test_case.expected_output + expected_output.metadata.name = train_job_name + + training_client.custom_api.create_namespaced_custom_object.assert_called_with( + constants.GROUP, + constants.VERSION, + DEFAULT_NAMESPACE, + constants.TRAINJOB_PLURAL, + expected_output.to_dict(), + ) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={"name": BASIC_TRAIN_JOB_NAME}, + expected_output=get_train_job_data_type( + train_job_name=BASIC_TRAIN_JOB_NAME + ), + ), + TestCase( + name="timeout error when getting job", + expected_status=FAILED, + config={"name": TIMEOUT}, + expected_error=TimeoutError, + ), + TestCase( + name="runtime error when getting job", + expected_status=FAILED, + config={"name": RUNTIME}, + expected_error=RuntimeError, + ), + ], +) +def test_get_job(training_client, test_case): + """Test TrainerClient.get_job with basic success path.""" + print("Executing test:", test_case.name) + try: + job = training_client.get_job(**test_case.config) + + assert test_case.expected_status == SUCCESS + assert asdict(job) == asdict(test_case.expected_output) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={}, + expected_output=[ + get_train_job_data_type(train_job_name="basic-job-1"), + get_train_job_data_type(train_job_name="basic-job-2"), + ], + ), + TestCase( + name="timeout error when listing jobs", + expected_status=FAILED, + config={"namespace": TIMEOUT}, + expected_error=TimeoutError, + ), + TestCase( + name="runtime error when listing jobs", + expected_status=FAILED, + config={"namespace": RUNTIME}, + expected_error=RuntimeError, + ), + ], +) +def test_list_jobs(training_client, test_case): + """Test TrainerClient.list_jobs with basic success path.""" + print("Executing test:", test_case.name) + try: + training_client.namespace = test_case.config.get("namespace", DEFAULT_NAMESPACE) + jobs = training_client.list_jobs() + + assert test_case.expected_status == SUCCESS + assert isinstance(jobs, list) + assert len(jobs) == 2 + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={"name": BASIC_TRAIN_JOB_NAME}, + expected_output=None, + ), + TestCase( + name="timeout error when deleting job", + expected_status=FAILED, + config={"namespace": TIMEOUT}, + expected_error=TimeoutError, + ), + TestCase( + name="runtime error when deleting job", + expected_status=FAILED, + config={"namespace": RUNTIME}, + expected_error=RuntimeError, + ), + ], +) +def test_delete_job(training_client, test_case): + """Test TrainerClient.delete_job with basic success path.""" + print("Executing test:", test_case.name) + try: + training_client.namespace = test_case.config.get("namespace", DEFAULT_NAMESPACE) + training_client.delete_job(test_case.config.get("name")) + assert test_case.expected_status == SUCCESS + + training_client.custom_api.delete_namespaced_custom_object.assert_called_with( + constants.GROUP, + constants.VERSION, + test_case.config.get("namespace", DEFAULT_NAMESPACE), + constants.TRAINJOB_PLURAL, + name=test_case.config.get("name"), + ) + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") + + +@pytest.mark.parametrize( + "test_case", + [ + TestCase( + name="valid flow with all defaults", + expected_status=SUCCESS, + config={"name": BASIC_TRAIN_JOB_NAME}, + expected_output={ + "node-0": "test log content", + }, + ), + TestCase( + name="runtime error when getting logs", + expected_status=FAILED, + config={"name": RUNTIME}, + expected_error=RuntimeError, + ), + ], +) +def test_get_job_logs(training_client, test_case): + """Test TrainerClient.get_job_logs with basic success path.""" + print("Executing test:", test_case.name) + try: + logs = training_client.get_job_logs(test_case.config.get("name")) + assert test_case.expected_status == SUCCESS + assert logs == test_case.expected_output + + except Exception as e: + assert type(e) is test_case.expected_error + print("test execution complete") diff --git a/python/kubeflow/trainer/utils/utils.py b/python/kubeflow/trainer/utils/utils.py index ecdbcbb9..4225bb8a 100644 --- a/python/kubeflow/trainer/utils/utils.py +++ b/python/kubeflow/trainer/utils/utils.py @@ -498,12 +498,16 @@ def get_dataset_initializer( if dataset.storage_uri.startswith("hf://") else "hf://" + dataset.storage_uri ), - env=[ - models.IoK8sApiCoreV1EnvVar( - name=constants.INITIALIZER_ENV_ACCESS_TOKEN, - value=dataset.access_token, - ), - ] if dataset.access_token else None + env=( + [ + models.IoK8sApiCoreV1EnvVar( + name=constants.INITIALIZER_ENV_ACCESS_TOKEN, + value=dataset.access_token, + ), + ] + if dataset.access_token + else None + ), ) return dataset_initializer @@ -525,12 +529,16 @@ def get_model_initializer( if model.storage_uri.startswith("hf://") else "hf://" + model.storage_uri ), - env=[ - models.IoK8sApiCoreV1EnvVar( - name=constants.INITIALIZER_ENV_ACCESS_TOKEN, - value=model.access_token, - ), - ] if model.access_token else None + env=( + [ + models.IoK8sApiCoreV1EnvVar( + name=constants.INITIALIZER_ENV_ACCESS_TOKEN, + value=model.access_token, + ), + ] + if model.access_token + else None + ), ) return model_initializer diff --git a/python/pyproject.toml b/python/pyproject.toml index aecd770e..2ecb710b 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -30,6 +30,12 @@ dependencies = [ "pydantic>=2.10.0", 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