def test_project_file_db_roundtrip(create_server):
server: Server = create_server()
db: HTTPRunDB = server.conn
project_name = "project-name"
description = "project description"
goals = "project goals"
desired_state = mlrun.api.schemas.ProjectState.archived
params = {"param_key": "param value"}
artifact_path = "/tmp"
conda = "conda"
source = "source"
subpath = "subpath"
origin_url = "origin_url"
labels = {"key": "value"}
annotations = {"annotation-key": "annotation-value"}
project_metadata = mlrun.projects.project.ProjectMetadata(
project_name, labels=labels, annotations=annotations,
)
project_spec = mlrun.projects.project.ProjectSpec(
description,
params,
artifact_path=artifact_path,
conda=conda,
source=source,
subpath=subpath,
origin_url=origin_url,
goals=goals,
desired_state=desired_state,
)
project = mlrun.projects.project.MlrunProject(
metadata=project_metadata, spec=project_spec
)
function_name = "trainer-function"
function = mlrun.new_function(function_name, project_name)
project.set_function(function, function_name)
project.set_function("hub://describe", "describe")
workflow_name = "workflow-name"
workflow_file_path = Path(tests_root_directory) / "rundb" / "workflow.py"
project.set_workflow(workflow_name, str(workflow_file_path))
artifact_dict = {
"key": "raw-data",
"kind": "",
"iter": 0,
"tree": "latest",
"target_path": "https://raw.githubusercontent.com/mlrun/demos/master/customer-churn-prediction/WA_Fn-UseC_-Telc"
"o-Customer-Churn.csv",
"db_key": "raw-data",
}
project.artifacts = [artifact_dict]
created_project = db.create_project(project)
_assert_projects(project, created_project)
stored_project = db.store_project(project_name, project)
_assert_projects(project, stored_project)
patched_project = db.patch_project(project_name, {})
_assert_projects(project, patched_project)
get_project = db.get_project(project_name)
_assert_projects(project, get_project)
list_projects = db.list_projects()
_assert_projects(project, list_projects[0])
def test_sync_functions():
project_name = "project-name"
project = mlrun.new_project(project_name)
project.set_function("hub://describe")
project_function_object = project.spec._function_objects
project_file_path = pathlib.Path(tests.conftest.results) / "project.yaml"
project.export(str(project_file_path))
imported_project = mlrun.load_project(None, str(project_file_path))
assert imported_project.spec._function_objects == {}
imported_project.sync_functions()
_assert_project_function_objects(imported_project, project_function_object)
def test_sync_functions():
project_name = "project-name"
project = mlrun.new_project(project_name)
project.set_function("hub://describe", "describe")
project_function_object = project.spec._function_objects
project_file_path = pathlib.Path(tests.conftest.results) / "project.yaml"
project.export(str(project_file_path))
imported_project = mlrun.load_project("./", str(project_file_path))
assert imported_project.spec._function_objects == {}
imported_project.sync_functions()
_assert_project_function_objects(imported_project, project_function_object)
fn = project.func("describe")
assert fn.metadata.name == "describe", "func did not return"
# test that functions can be fetched from the DB (w/o set_function)
mlrun.import_function("hub://sklearn_classifier", new_name="train").save()
fn = project.func("train")
assert fn.metadata.name == "train", "train func did not return"