def test_fail_pipelines_are_equal(good_pipeline):
test_operations_dict = {"123123123": good_operation, "234234234": good_operation}
compare_pipeline = Pipeline(
id="Random-UUID-123123123123123", name="test-pipeline", runtime="kfp", runtime_config="default_kfp"
)
for key, operation in test_operations_dict.items():
compare_pipeline.operations[key] = operation
with pytest.raises(AssertionError):
assert compare_pipeline == good_pipeline
def construct_pipeline(name: str,
nodes: List[NodeBase],
location,
runtime_type: Optional[str] = 'local',
runtime_config: Optional[str] = 'local') -> Pipeline:
"""Returns an instance of a local Pipeline consisting of each node and populates the
specified location with the necessary files to run the pipeline from that location.
"""
pipeline = Pipeline(str(uuid.uuid4()), name, runtime_type, runtime_config)
for node in nodes:
pipeline.operations[node.id] = node.get_operation()
# copy the node file into the "working directory"
if isinstance(node, NotebookNode):
src_file = os.path.join(os.path.dirname(__file__),
'resources/node_util/node.ipynb')
elif isinstance(node, PythonNode):
src_file = os.path.join(os.path.dirname(__file__),
'resources/node_util/node.py')
else:
assert False, f"Invalid node type detected: {node.__class__.__name__}"
shutil.copy(src_file, os.path.join(location, node.filename))
# copy the node_util directory into the "working directory"
shutil.copytree(
os.path.join(os.path.dirname(__file__), 'resources/node_util'),
os.path.join(location, 'node_util'))
return pipeline
def test_pipelines_are_equal(good_pipeline):
compare_pipeline = Pipeline(id="Random-UUID-123123123123123",
name="test-pipeline",
runtime="kfp",
runtime_config="default_kfp")
assert compare_pipeline == good_pipeline
def parse(self, pipeline_json: Dict) -> Pipeline:
"""
The pipeline definitions allow for defining multiple pipelines in one json file.
When super_nodes are used, their node actually references another pipeline in the
set of pipeline definitions - which is "flattened" into the overall pipeline object's
list of operations.
"""
try:
pipeline_definition = PipelineDefinition(
pipeline_definition=pipeline_json)
primary_pipeline = pipeline_definition.primary_pipeline
except Exception as e:
raise ValueError(f"Invalid Pipeline: {e}")
# runtime info is only present on primary pipeline...
runtime = primary_pipeline.runtime
if not runtime:
raise ValueError("Invalid pipeline: Missing runtime.")
runtime_config = primary_pipeline.runtime_config
if not runtime_config:
raise ValueError(
"Invalid pipeline: Missing runtime configuration.")
source = primary_pipeline.source
description = primary_pipeline.get_property("description")
pipeline_object = Pipeline(
id=primary_pipeline.id,
name=primary_pipeline.name,
runtime=runtime,
runtime_config=runtime_config,
source=source,
description=description,
pipeline_parameters=primary_pipeline.pipeline_parameters,
)
self._nodes_to_operations(pipeline_definition, pipeline_object,
primary_pipeline.nodes)
return pipeline_object
def test_processing_filename_runtime_specific_component(
monkeypatch, processor, sample_metadata, tmpdir):
# Define the appropriate reader for a filesystem-type component definition
kfp_supported_file_types = [".yaml"]
reader = FilesystemComponentCatalogConnector(kfp_supported_file_types)
# Assign test resource location
absolute_path = os.path.abspath(
os.path.join(os.path.dirname(__file__), "..", "resources",
"components", "download_data.yaml"))
# Read contents of given path -- read_component_definition() returns a
# a dictionary of component definition content indexed by path
entry_data = reader.get_entry_data({"path": absolute_path}, {})
component_definition = entry_data.definition
# Instantiate a file-based component
component_id = "test-component"
component = Component(
id=component_id,
name="Download data",
description="",
op="download-data",
catalog_type="elyra-kfp-examples-catalog",
component_reference={"path": absolute_path},
definition=component_definition,
properties=[],
categories=[],
)
# Fabricate the component cache to include single filename-based component for testing
ComponentCache.instance()._component_cache[processor._type.name] = {
"spoofed_catalog": {
"components": {
component_id: component
}
}
}
# Construct hypothetical operation for component
operation_name = "Download data test"
operation_params = {
"url":
"https://raw.githubusercontent.com/elyra-ai/elyra/master/tests/assets/helloworld.ipynb",
"curl_options": "--location",
}
operation = Operation(
id="download-data-id",
type="execution_node",
classifier=component_id,
name=operation_name,
parent_operation_ids=[],
component_params=operation_params,
)
# Build a mock runtime config for use in _cc_pipeline
mocked_runtime = Metadata(name="test-metadata",
display_name="test",
schema_name="kfp",
metadata=sample_metadata)
mocked_func = mock.Mock(return_value="default",
side_effect=[mocked_runtime, sample_metadata])
monkeypatch.setattr(processor, "_get_metadata_configuration", mocked_func)
# Construct single-operation pipeline
pipeline = Pipeline(id="pipeline-id",
name="kfp_test",
runtime="kfp",
runtime_config="test",
source="download_data.pipeline")
pipeline.operations[operation.id] = operation
# Establish path and function to construct pipeline
pipeline_path = os.path.join(tmpdir, "kfp_test.yaml")
constructed_pipeline_function = lambda: processor._cc_pipeline(
pipeline=pipeline, pipeline_name="test_pipeline")
# TODO Check against both argo and tekton compilations
# Compile pipeline and save into pipeline_path
kfp_argo_compiler.Compiler().compile(constructed_pipeline_function,
pipeline_path)
# Read contents of pipeline YAML
with open(pipeline_path) as f:
pipeline_yaml = yaml.safe_load(f.read())
# Check the pipeline file contents for correctness
pipeline_template = pipeline_yaml["spec"]["templates"][0]
assert pipeline_template["metadata"]["annotations"][
"pipelines.kubeflow.org/task_display_name"] == operation_name
assert pipeline_template["container"]["command"][3] == operation_params[
"url"]
def good_pipeline():
test_pipeline = Pipeline(id="Random-UUID-123123123123123",
name="test-pipeline",
runtime="kfp",
runtime_config="default_kfp")
return test_pipeline
def test_fail_create_pipeline_missing_runtime_config():
with pytest.raises(TypeError):
Pipeline(id="Random-UUID-123123123123123",
name="test-pipeline",
runtime="kfp")
def test_fail_create_pipeline_missing_name():
with pytest.raises(TypeError):
Pipeline(id="Random-UUID-123123123123123",
runtime="kfp",
runtime_config="default_kfp")
def test_fail_create_pipeline_missing_id():
with pytest.raises(TypeError):
Pipeline(name="test-pipeline",
runtime="kfp",
runtime_config="default_kfp")
def test_cc_pipeline_component_no_input(monkeypatch, processor,
component_cache, sample_metadata,
tmpdir):
"""
Verifies that cc_pipeline can handle KFP component definitions that don't
include any inputs
"""
# Define the appropriate reader for a filesystem-type component definition
kfp_supported_file_types = [".yaml"]
reader = FilesystemComponentCatalogConnector(kfp_supported_file_types)
# Assign test resource location
cpath = (Path(__file__).parent / ".." / "resources" / "components" /
"kfp_test_operator_no_inputs.yaml").resolve()
assert cpath.is_file()
cpath = str(cpath)
# Read contents of given path -- read_component_definition() returns a
# a dictionary of component definition content indexed by path
entry_data = reader.get_entry_data({"path": cpath}, {})
component_definition = entry_data.definition
# Instantiate a file-based component
component_id = "test-component"
component = Component(
id=component_id,
name="No input data",
description="",
op="no-input-data",
catalog_type="elyra-kfp-examples-catalog",
component_reference={"path": cpath},
definition=component_definition,
properties=[],
categories=[],
)
# Fabricate the component cache to include single filename-based component for testing
component_cache._component_cache[processor._type.name] = {
"spoofed_catalog": {
"components": {
component_id: component
}
}
}
# Construct hypothetical operation for component
operation_name = "no-input-test"
operation_params = {}
operation = Operation(
id="no-input-id",
type="execution_node",
classifier=component_id,
name=operation_name,
parent_operation_ids=[],
component_params=operation_params,
)
# Build a mock runtime config for use in _cc_pipeline
mocked_runtime = Metadata(name="test-metadata",
display_name="test",
schema_name="kfp",
metadata=sample_metadata)
mocked_func = mock.Mock(return_value="default",
side_effect=[mocked_runtime, sample_metadata])
monkeypatch.setattr(processor, "_get_metadata_configuration", mocked_func)
# Construct single-operation pipeline
pipeline = Pipeline(id="pipeline-id",
name="kfp_test",
runtime="kfp",
runtime_config="test",
source="no_input.pipeline")
pipeline.operations[operation.id] = operation
constructed_pipeline_function = lambda: processor._cc_pipeline(
pipeline=pipeline, pipeline_name="test_pipeline")
pipeline_path = str(Path(tmpdir) / "no_inputs_test.yaml")
# Compile pipeline and save into pipeline_path
kfp_argo_compiler.Compiler().compile(constructed_pipeline_function,
pipeline_path)
def process(self, pipeline: Pipeline) -> None:
"""
Submit the pipeline for execution on Apache Airflow.
"""
t0_all = time.time()
timestamp = datetime.now().strftime("%m%d%H%M%S")
# Create an instance id that will be used to store
# the pipelines' dependencies, if applicable
pipeline_instance_id = f"{pipeline.name}-{timestamp}"
runtime_configuration = self._get_metadata_configuration(
schemaspace=Runtimes.RUNTIMES_SCHEMASPACE_ID, name=pipeline.runtime_config
)
api_endpoint = runtime_configuration.metadata.get("api_endpoint")
cos_endpoint = runtime_configuration.metadata.get("cos_endpoint")
cos_bucket = runtime_configuration.metadata.get("cos_bucket")
git_type = SupportedGitTypes.get_instance_by_name(
runtime_configuration.metadata.get("git_type", SupportedGitTypes.GITHUB.name)
)
if git_type == SupportedGitTypes.GITLAB and SupportedGitTypes.is_enabled(SupportedGitTypes.GITLAB) is False:
raise ValueError(
"Python package `python-gitlab` is not installed. "
"Please install using `elyra[gitlab]` to use GitLab as DAG repository."
)
github_api_endpoint = runtime_configuration.metadata.get("github_api_endpoint")
github_repo_token = runtime_configuration.metadata.get("github_repo_token")
github_repo = runtime_configuration.metadata.get("github_repo")
github_branch = runtime_configuration.metadata.get("github_branch")
self.log_pipeline_info(pipeline.name, "Submitting pipeline")
with tempfile.TemporaryDirectory() as temp_dir:
pipeline_export_path = os.path.join(temp_dir, f"{pipeline.name}.py")
self.log.debug(f"Creating temp directory '{temp_dir}'")
pipeline_filepath = self.create_pipeline_file(
pipeline=pipeline,
pipeline_export_format="py",
pipeline_export_path=pipeline_export_path,
pipeline_name=pipeline.name,
pipeline_instance_id=pipeline_instance_id,
)
self.log.debug(f"Uploading pipeline file '{pipeline_filepath}'")
try:
if git_type == SupportedGitTypes.GITHUB:
git_client = GithubClient(
server_url=github_api_endpoint, token=github_repo_token, repo=github_repo, branch=github_branch
)
else:
git_client = GitLabClient(
server_url=github_api_endpoint,
token=github_repo_token,
project=github_repo,
branch=github_branch,
)
except BaseException as be:
raise RuntimeError(f"Unable to create a connection to {github_api_endpoint}: {str(be)}") from be
git_client.upload_dag(pipeline_filepath, pipeline_instance_id)
self.log.info("Waiting for Airflow Scheduler to process and start the pipeline")
download_url = git_client.get_git_url(
api_url=github_api_endpoint, repository_name=github_repo, repository_branch=github_branch
)
self.log_pipeline_info(
pipeline.name, f"pipeline pushed to git: {download_url}", duration=(time.time() - t0_all)
)
if pipeline.contains_generic_operations():
object_storage_url = f"{cos_endpoint}"
os_path = join_paths(pipeline.pipeline_parameters.get(COS_OBJECT_PREFIX), pipeline_instance_id)
object_storage_path = f"/{cos_bucket}/{os_path}"
else:
object_storage_url = None
object_storage_path = None
return AirflowPipelineProcessorResponse(
git_url=f"{download_url}",
run_url=f"{api_endpoint}",
object_storage_url=object_storage_url,
object_storage_path=object_storage_path,
)
