def test_save_get_function(db: RunDBInterface): func, name, proj, tag = {"data": {"x": 1, "y": 2}}, "f1", "p2", "t3u" db.store_function(func, name, proj, tag) db_func = db.get_function(name, proj, tag) # db methods enriches metadata assert func["data"] == db_func["data"]
def test_list_functions(db: RunDBInterface): name = "fn" fn1 = new_func({"l1": "v1", "l2": "v2"}, x=1) db.store_function(fn1, name) fn2 = new_func({"l2": "v2", "l3": "v3"}, x=2) db.store_function(fn2, name, tag="t1") fn3 = new_func({"l3": "v3"}, x=3) db.store_function(fn3, name, tag="t2") funcs = db.list_functions(name, labels={"l2": "v2"}) assert 2 == len(funcs), "num of funcs" assert {1, 2} == {fn["x"] for fn in funcs}, "xs"
def _create_resources_of_all_kinds(db: DBInterface, db_session: sqlalchemy.orm.Session, project: str): # Create several functions with several tags labels = { "name": "value", "name2": "value2", } function = { "bla": "blabla", "metadata": { "labels": labels }, "status": { "bla": "blabla" }, } function_names = ["function_name_1", "function_name_2", "function_name_3"] function_tags = ["some_tag", "some_tag2", "some_tag3"] for function_name in function_names: for function_tag in function_tags: db.store_function( db_session, function, function_name, project, tag=function_tag, versioned=True, ) # Create several artifacts with several tags artifact = { "bla": "blabla", "labels": labels, "status": { "bla": "blabla" }, } artifact_keys = ["artifact_key_1", "artifact_key_2", "artifact_key_3"] artifact_uids = ["some_uid", "some_uid2", "some_uid3"] artifact_tags = ["some_tag", "some_tag2", "some_tag3"] for artifact_key in artifact_keys: for artifact_uid in artifact_uids: for artifact_tag in artifact_tags: for artifact_iter in range(3): db.store_artifact( db_session, artifact_key, artifact, artifact_uid, artifact_iter, artifact_tag, project, ) # Create several runs run = { "bla": "blabla", "metadata": { "labels": labels }, "status": { "bla": "blabla" }, } run_uids = ["some_uid", "some_uid2", "some_uid3"] for run_uid in run_uids: for run_iter in range(3): db.store_run(db_session, run, run_uid, project, run_iter) # Create several logs log = b"some random log" log_uids = ["some_uid", "some_uid2", "some_uid3"] for log_uid in log_uids: db.store_log(db_session, log_uid, project, log) # Create several schedule schedule = { "bla": "blabla", "status": { "bla": "blabla" }, } schedule_cron_trigger = schemas.ScheduleCronTrigger(year=1999) schedule_names = ["schedule_name_1", "schedule_name_2", "schedule_name_3"] for schedule_name in schedule_names: db.create_schedule( db_session, project, schedule_name, schemas.ScheduleKinds.job, schedule, schedule_cron_trigger, labels, ) feature_set = schemas.FeatureSet( metadata=schemas.ObjectMetadata(name="dummy", tag="latest", labels={"owner": "nobody"}), spec=schemas.FeatureSetSpec( entities=[ schemas.Entity(name="ent1", value_type="str", labels={"label": "1"}) ], features=[ schemas.Feature(name="feat1", value_type="str", labels={"label": "1"}) ], ), status={}, ) db.create_feature_set(db_session, project, feature_set) feature_vector = schemas.FeatureVector( metadata=schemas.ObjectMetadata(name="dummy", tag="latest", labels={"owner": "somebody"}), spec=schemas.ObjectSpec(), status=schemas.ObjectStatus(state="created"), ) db.create_feature_vector(db_session, project, feature_vector)
def _create_resources_of_all_kinds( db_session: Session, k8s_secrets_mock: tests.api.conftest.K8sSecretsMock, project: str, ): db = mlrun.api.utils.singletons.db.get_db() # add labels to project project_schema = mlrun.api.schemas.Project( metadata=mlrun.api.schemas.ProjectMetadata( name=project, labels={"key": "value"} ), spec=mlrun.api.schemas.ProjectSpec(description="some desc"), ) mlrun.api.utils.singletons.project_member.get_project_member().store_project( db_session, project, project_schema ) # Create several functions with several tags labels = { "name": "value", "name2": "value2", } function = { "bla": "blabla", "metadata": {"labels": labels}, "spec": {"asd": "asdasd"}, "status": {"bla": "blabla"}, } function_names = ["function_name_1", "function_name_2", "function_name_3"] function_tags = ["some_tag", "some_tag2", "some_tag3"] for function_name in function_names: for function_tag in function_tags: # change spec a bit so different (un-tagged) versions will be created for index in range(3): function["spec"]["index"] = index db.store_function( db_session, function, function_name, project, tag=function_tag, versioned=True, ) # Create several artifacts with several tags artifact = { "bla": "blabla", "labels": labels, "status": {"bla": "blabla"}, } artifact_keys = ["artifact_key_1", "artifact_key_2", "artifact_key_3"] artifact_uids = ["some_uid", "some_uid2", "some_uid3"] artifact_tags = ["some_tag", "some_tag2", "some_tag3"] for artifact_key in artifact_keys: for artifact_uid in artifact_uids: for artifact_tag in artifact_tags: for artifact_iter in range(3): artifact["iter"] = artifact_iter artifact["tag"] = artifact_tag artifact["uid"] = artifact_uid db.store_artifact( db_session, artifact_key, artifact, artifact_uid, artifact_iter, artifact_tag, project, ) # Create several runs run = { "bla": "blabla", "metadata": {"labels": labels}, "status": {"bla": "blabla"}, } run_uids = ["some_uid", "some_uid2", "some_uid3"] for run_uid in run_uids: for run_iter in range(3): db.store_run(db_session, run, run_uid, project, run_iter) # Create several logs log = b"some random log" log_uids = ["some_uid", "some_uid2", "some_uid3"] for log_uid in log_uids: mlrun.api.crud.Logs().store_log(log, project, log_uid) # Create several schedule schedule = { "bla": "blabla", "status": {"bla": "blabla"}, } schedule_cron_trigger = mlrun.api.schemas.ScheduleCronTrigger(year=1999) schedule_names = ["schedule_name_1", "schedule_name_2", "schedule_name_3"] for schedule_name in schedule_names: mlrun.api.utils.singletons.scheduler.get_scheduler().create_schedule( db_session, mlrun.api.schemas.AuthInfo(), project, schedule_name, mlrun.api.schemas.ScheduleKinds.job, schedule, schedule_cron_trigger, labels, ) # Create several feature sets with several tags labels = { "owner": "nobody", } feature_set = mlrun.api.schemas.FeatureSet( metadata=mlrun.api.schemas.ObjectMetadata( name="dummy", tag="latest", labels=labels ), spec=mlrun.api.schemas.FeatureSetSpec( entities=[ mlrun.api.schemas.Entity( name="ent1", value_type="str", labels={"label": "1"} ) ], features=[ mlrun.api.schemas.Feature( name="feat1", value_type="str", labels={"label": "1"} ) ], ), status={}, ) feature_set_names = ["feature_set_1", "feature_set_2", "feature_set_3"] feature_set_tags = ["some_tag", "some_tag2", "some_tag3"] for feature_set_name in feature_set_names: for feature_set_tag in feature_set_tags: # change spec a bit so different (un-tagged) versions will be created for index in range(3): feature_set.metadata.name = feature_set_name feature_set.metadata.tag = feature_set_tag feature_set.spec.index = index db.store_feature_set(db_session, project, feature_set_name, feature_set) feature_vector = mlrun.api.schemas.FeatureVector( metadata=mlrun.api.schemas.ObjectMetadata( name="dummy", tag="latest", labels=labels ), spec=mlrun.api.schemas.ObjectSpec(), status=mlrun.api.schemas.ObjectStatus(state="created"), ) feature_vector_names = ["feature_vector_1", "feature_vector_2", "feature_vector_3"] feature_vector_tags = ["some_tag", "some_tag2", "some_tag3"] for feature_vector_name in feature_vector_names: for feature_vector_tag in feature_vector_tags: # change spec a bit so different (un-tagged) versions will be created for index in range(3): feature_vector.metadata.name = feature_vector_name feature_vector.metadata.tag = feature_vector_tag feature_vector.spec.index = index db.store_feature_vector( db_session, project, feature_vector_name, feature_vector ) secrets = {f"secret_{i}": "a secret" for i in range(5)} k8s_secrets_mock.store_project_secrets(project, secrets)
def _create_resources_of_all_kinds(db_session: Session, project: str): db = mlrun.api.utils.singletons.db.get_db() # add labels to project project_schema = mlrun.api.schemas.Project( metadata=mlrun.api.schemas.ProjectMetadata(name=project, labels={"key": "value"}), spec=mlrun.api.schemas.ProjectSpec(description="some desc"), ) db.store_project(db_session, project, project_schema) # Create several functions with several tags labels = { "name": "value", "name2": "value2", } function = { "bla": "blabla", "metadata": { "labels": labels }, "status": { "bla": "blabla" }, } function_names = ["function_name_1", "function_name_2", "function_name_3"] function_tags = ["some_tag", "some_tag2", "some_tag3"] for function_name in function_names: for function_tag in function_tags: db.store_function( db_session, function, function_name, project, tag=function_tag, versioned=True, ) # Create several artifacts with several tags artifact = { "bla": "blabla", "labels": labels, "status": { "bla": "blabla" }, } artifact_keys = ["artifact_key_1", "artifact_key_2", "artifact_key_3"] artifact_uids = ["some_uid", "some_uid2", "some_uid3"] artifact_tags = ["some_tag", "some_tag2", "some_tag3"] for artifact_key in artifact_keys: for artifact_uid in artifact_uids: for artifact_tag in artifact_tags: for artifact_iter in range(3): db.store_artifact( db_session, artifact_key, artifact, artifact_uid, artifact_iter, artifact_tag, project, ) # Create several runs run = { "bla": "blabla", "metadata": { "labels": labels }, "status": { "bla": "blabla" }, } run_uids = ["some_uid", "some_uid2", "some_uid3"] for run_uid in run_uids: for run_iter in range(3): db.store_run(db_session, run, run_uid, project, run_iter) # Create several logs log = b"some random log" log_uids = ["some_uid", "some_uid2", "some_uid3"] for log_uid in log_uids: db.store_log(db_session, log_uid, project, log) # Create several schedule schedule = { "bla": "blabla", "status": { "bla": "blabla" }, } schedule_cron_trigger = mlrun.api.schemas.ScheduleCronTrigger(year=1999) schedule_names = ["schedule_name_1", "schedule_name_2", "schedule_name_3"] for schedule_name in schedule_names: db.create_schedule( db_session, project, schedule_name, mlrun.api.schemas.ScheduleKinds.job, schedule, schedule_cron_trigger, mlrun.config.config.httpdb.scheduling.default_concurrency_limit, labels, ) feature_set = mlrun.api.schemas.FeatureSet( metadata=mlrun.api.schemas.ObjectMetadata(name="dummy", tag="latest", labels={"owner": "nobody"}), spec=mlrun.api.schemas.FeatureSetSpec( entities=[ mlrun.api.schemas.Entity(name="ent1", value_type="str", labels={"label": "1"}) ], features=[ mlrun.api.schemas.Feature(name="feat1", value_type="str", labels={"label": "1"}) ], ), status={}, ) db.create_feature_set(db_session, project, feature_set) feature_vector = mlrun.api.schemas.FeatureVector( metadata=mlrun.api.schemas.ObjectMetadata(name="dummy", tag="latest", labels={"owner": "somebody"}), spec=mlrun.api.schemas.ObjectSpec(), status=mlrun.api.schemas.ObjectStatus(state="created"), ) db.create_feature_vector(db_session, project, feature_vector)