/
test_projects.py
1315 lines (1184 loc) · 46.1 KB
/
test_projects.py
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import copy
import datetime
import os
import typing
import unittest.mock
from http import HTTPStatus
from uuid import uuid4
import deepdiff
import mergedeep
import pytest
from fastapi.testclient import TestClient
from sqlalchemy.orm import Session
import mlrun.api.api.utils
import mlrun.api.crud
import mlrun.api.schemas
import mlrun.api.utils.singletons.db
import mlrun.api.utils.singletons.k8s
import mlrun.api.utils.singletons.logs_dir
import mlrun.api.utils.singletons.project_member
import mlrun.api.utils.singletons.scheduler
import mlrun.artifacts.dataset
import mlrun.artifacts.model
import mlrun.errors
import tests.api.conftest
from mlrun.api.db.sqldb.models import (
Artifact,
Entity,
Feature,
FeatureSet,
FeatureVector,
Function,
Project,
Run,
Schedule,
_classes,
)
@pytest.fixture(params=["leader", "follower"])
def project_member_mode(request, db: Session) -> str:
if request.param == "follower":
mlrun.config.config.httpdb.projects.leader = "nop"
mlrun.api.utils.singletons.project_member.initialize_project_member()
mlrun.api.utils.singletons.project_member.get_project_member()._leader_client.db_session = (
db
)
elif request.param == "leader":
mlrun.config.config.httpdb.projects.leader = "mlrun"
mlrun.api.utils.singletons.project_member.initialize_project_member()
else:
raise NotImplementedError(
f"Provided project member mode is not supported. mode={request.param}"
)
yield request.param
def test_create_project_failure_already_exists(
db: Session, client: TestClient, project_member_mode: str
) -> None:
name1 = f"prj-{uuid4().hex}"
project_1 = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=name1),
)
# create
response = client.post("projects", json=project_1.dict())
assert response.status_code == HTTPStatus.CREATED.value
_assert_project_response(project_1, response)
# create again
response = client.post("projects", json=project_1.dict())
assert response.status_code == HTTPStatus.CONFLICT.value
def test_get_non_existing_project(
db: Session, client: TestClient, project_member_mode: str
) -> None:
"""
At first we were doing auth before get - which caused get on non existing project to return unauthorized instead of
not found - which "ruined" the `mlrun.get_or_create_project` logic - so adding a specific test to verify it works
"""
project = "does-not-exist"
mlrun.api.utils.auth.verifier.AuthVerifier().query_project_permissions = (
unittest.mock.Mock(side_effect=mlrun.errors.MLRunUnauthorizedError("bla"))
)
response = client.get(f"projects/{project}")
assert response.status_code == HTTPStatus.NOT_FOUND.value
def test_delete_project_with_resources(
db: Session,
client: TestClient,
project_member_mode: str,
k8s_secrets_mock: tests.api.conftest.K8sSecretsMock,
):
# need to set this to False, otherwise impl will try to delete k8s resources, and will need many more
# mocks to overcome this.
k8s_secrets_mock.set_is_running_in_k8s_cluster(False)
project_to_keep = "project-to-keep"
project_to_remove = "project-to-remove"
_create_resources_of_all_kinds(db, k8s_secrets_mock, project_to_keep)
_create_resources_of_all_kinds(db, k8s_secrets_mock, project_to_remove)
(
project_to_keep_table_name_records_count_map_before_project_removal,
project_to_keep_object_records_count_map_before_project_removal,
) = _assert_resources_in_project(
db, k8s_secrets_mock, project_member_mode, project_to_keep
)
_assert_resources_in_project(
db, k8s_secrets_mock, project_member_mode, project_to_remove
)
# deletion strategy - check - should fail because there are resources
response = client.delete(
f"projects/{project_to_remove}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.check.value
},
)
assert response.status_code == HTTPStatus.PRECONDITION_FAILED.value
# deletion strategy - restricted - should fail because there are resources
response = client.delete(
f"projects/{project_to_remove}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.restricted.value
},
)
assert response.status_code == HTTPStatus.PRECONDITION_FAILED.value
# deletion strategy - cascading - should succeed and remove all related resources
response = client.delete(
f"projects/{project_to_remove}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.cascading.value
},
)
assert response.status_code == HTTPStatus.NO_CONTENT.value
(
project_to_keep_table_name_records_count_map_after_project_removal,
project_to_keep_object_records_count_map_after_project_removal,
) = _assert_resources_in_project(
db, k8s_secrets_mock, project_member_mode, project_to_keep
)
_assert_resources_in_project(
db,
k8s_secrets_mock,
project_member_mode,
project_to_remove,
assert_no_resources=True,
)
assert (
deepdiff.DeepDiff(
project_to_keep_object_records_count_map_before_project_removal,
project_to_keep_object_records_count_map_after_project_removal,
ignore_order=True,
)
== {}
)
assert (
deepdiff.DeepDiff(
project_to_keep_table_name_records_count_map_before_project_removal,
project_to_keep_table_name_records_count_map_after_project_removal,
ignore_order=True,
)
== {}
)
# deletion strategy - check - should succeed cause no project
response = client.delete(
f"projects/{project_to_remove}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.check.value
},
)
assert response.status_code == HTTPStatus.NO_CONTENT.value
# deletion strategy - restricted - should succeed cause no project
response = client.delete(
f"projects/{project_to_remove}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.restricted.value
},
)
assert response.status_code == HTTPStatus.NO_CONTENT.value
def test_list_and_get_project_summaries(
db: Session, client: TestClient, project_member_mode: str
) -> None:
# create empty project
empty_project_name = "empty-project"
empty_project = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=empty_project_name),
)
response = client.post("projects", json=empty_project.dict())
assert response.status_code == HTTPStatus.CREATED.value
# create project with resources
project_name = "project-with-resources"
project = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=project_name),
)
response = client.post("projects", json=project.dict())
assert response.status_code == HTTPStatus.CREATED.value
# create files for the project
files_count = 5
_create_artifacts(
client, project_name, files_count, mlrun.artifacts.ChartArtifact.kind
)
# create feature sets for the project
feature_sets_count = 9
_create_feature_sets(client, project_name, feature_sets_count)
# create model artifacts for the project
models_count = 4
_create_artifacts(
client, project_name, models_count, mlrun.artifacts.model.ModelArtifact.kind
)
# create dataset artifacts for the project to make sure we're not mistakenly counting them
_create_artifacts(
client, project_name, 7, mlrun.artifacts.dataset.DatasetArtifact.kind
)
# create runs for the project
running_runs_count = 5
_create_runs(
client,
project_name,
running_runs_count,
mlrun.runtimes.constants.RunStates.running,
)
# create completed runs for the project to make sure we're not mistakenly counting them
_create_runs(client, project_name, 2, mlrun.runtimes.constants.RunStates.completed)
# create failed runs for the project for less than 24 hours ago
recent_failed_runs_count = 6
one_hour_ago = datetime.datetime.now() - datetime.timedelta(hours=1)
_create_runs(
client,
project_name,
recent_failed_runs_count,
mlrun.runtimes.constants.RunStates.error,
one_hour_ago,
)
# create aborted runs for the project for less than 24 hours ago - make sure we count them as well
recent_aborted_runs_count = 6
one_hour_ago = datetime.datetime.now() - datetime.timedelta(hours=1)
_create_runs(
client,
project_name,
recent_failed_runs_count,
mlrun.runtimes.constants.RunStates.aborted,
one_hour_ago,
)
# create failed runs for the project for more than 24 hours ago to make sure we're not mistakenly counting them
two_days_ago = datetime.datetime.now() - datetime.timedelta(hours=48)
_create_runs(
client, project_name, 3, mlrun.runtimes.constants.RunStates.error, two_days_ago
)
# create schedules for the project
schedules_count = 3
_create_schedules(
client,
project_name,
schedules_count,
)
# mock pipelines for the project
running_pipelines_count = _mock_pipelines(
project_name,
)
# list project summaries
response = client.get("project-summaries")
project_summaries_output = mlrun.api.schemas.ProjectSummariesOutput(
**response.json()
)
for index, project_summary in enumerate(project_summaries_output.project_summaries):
if project_summary.name == empty_project_name:
_assert_project_summary(project_summary, 0, 0, 0, 0, 0, 0, 0)
elif project_summary.name == project_name:
_assert_project_summary(
project_summary,
files_count,
feature_sets_count,
models_count,
recent_failed_runs_count + recent_aborted_runs_count,
running_runs_count,
schedules_count,
running_pipelines_count,
)
else:
pytest.fail(f"Unexpected project summary returned: {project_summary}")
# get project summary
response = client.get(f"project-summaries/{project_name}")
project_summary = mlrun.api.schemas.ProjectSummary(**response.json())
_assert_project_summary(
project_summary,
files_count,
feature_sets_count,
models_count,
recent_failed_runs_count + recent_aborted_runs_count,
running_runs_count,
schedules_count,
running_pipelines_count,
)
def test_list_project_summaries_different_installation_modes(
db: Session, client: TestClient, project_member_mode: str
) -> None:
"""
The list project summaries endpoint is used in our projects screen and tend to break in different installation modes
"""
# create empty project
empty_project_name = "empty-project"
empty_project = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=empty_project_name),
)
response = client.post("projects", json=empty_project.dict())
assert response.status_code == HTTPStatus.CREATED.value
mlrun.api.crud.Pipelines().list_pipelines = unittest.mock.Mock(
return_value=(0, None, [])
)
# Enterprise installation configuration post 3.4.0
mlrun.mlconf.igz_version = "3.6.0-b26.20210904121245"
mlrun.mlconf.kfp_url = "https://somekfp-url.com"
mlrun.mlconf.namespace = "default-tenant"
response = client.get("project-summaries")
assert response.status_code == HTTPStatus.OK.value
project_summaries_output = mlrun.api.schemas.ProjectSummariesOutput(
**response.json()
)
_assert_project_summary(
# accessing the zero index as there's only one project
project_summaries_output.project_summaries[0],
0,
0,
0,
0,
0,
0,
0,
)
# Enterprise installation configuration pre 3.4.0
mlrun.mlconf.igz_version = "3.2.0-b26.20210904121245"
mlrun.mlconf.kfp_url = ""
mlrun.mlconf.namespace = "default-tenant"
response = client.get("project-summaries")
assert response.status_code == HTTPStatus.OK.value
project_summaries_output = mlrun.api.schemas.ProjectSummariesOutput(
**response.json()
)
_assert_project_summary(
# accessing the zero index as there's only one project
project_summaries_output.project_summaries[0],
0,
0,
0,
0,
0,
0,
0,
)
# Kubernetes installation configuration (mlrun-kit)
mlrun.mlconf.igz_version = ""
mlrun.mlconf.kfp_url = ""
mlrun.mlconf.namespace = "mlrun"
response = client.get("project-summaries")
assert response.status_code == HTTPStatus.OK.value
project_summaries_output = mlrun.api.schemas.ProjectSummariesOutput(
**response.json()
)
_assert_project_summary(
# accessing the zero index as there's only one project
project_summaries_output.project_summaries[0],
0,
0,
0,
0,
0,
0,
0,
)
# Docker installation configuration
mlrun.mlconf.igz_version = ""
mlrun.mlconf.kfp_url = ""
mlrun.mlconf.namespace = ""
response = client.get("project-summaries")
assert response.status_code == HTTPStatus.OK.value
project_summaries_output = mlrun.api.schemas.ProjectSummariesOutput(
**response.json()
)
_assert_project_summary(
# accessing the zero index as there's only one project
project_summaries_output.project_summaries[0],
0,
0,
0,
0,
0,
0,
0,
)
def test_delete_project_deletion_strategy_check(
db: Session,
client: TestClient,
project_member_mode: str,
k8s_secrets_mock: tests.api.conftest.K8sSecretsMock,
) -> None:
project = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name="project-name"),
spec=mlrun.api.schemas.ProjectSpec(),
)
# create
response = client.post("projects", json=project.dict())
assert response.status_code == HTTPStatus.CREATED.value
_assert_project_response(project, response)
# deletion strategy - check - should succeed because there are no resources
response = client.delete(
f"projects/{project.metadata.name}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.check.value
},
)
assert response.status_code == HTTPStatus.NO_CONTENT.value
# ensure project not deleted
response = client.get(f"projects/{project.metadata.name}")
assert response.status_code == HTTPStatus.OK.value
_assert_project_response(project, response)
# add function to project 1
function_name = "function-name"
function = {"metadata": {"name": function_name}}
response = client.post(
f"func/{project.metadata.name}/{function_name}", json=function
)
assert response.status_code == HTTPStatus.OK.value
# deletion strategy - check - should fail because there are resources
response = client.delete(
f"projects/{project.metadata.name}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.check.value
},
)
assert response.status_code == HTTPStatus.PRECONDITION_FAILED.value
def test_delete_project_not_deleting_versioned_objects_multiple_times(
db: Session,
client: TestClient,
project_member_mode: str,
k8s_secrets_mock: tests.api.conftest.K8sSecretsMock,
) -> None:
# need to set this to False, otherwise impl will try to delete k8s resources, and will need many more
# mocks to overcome this.
k8s_secrets_mock.set_is_running_in_k8s_cluster(False)
project_name = "project-name"
_create_resources_of_all_kinds(db, k8s_secrets_mock, project_name)
response = client.get("funcs", params={"project": project_name})
assert response.status_code == HTTPStatus.OK.value
distinct_function_names = {
function["metadata"]["name"] for function in response.json()["funcs"]
}
# ensure there are indeed several versions of the same function name
assert len(distinct_function_names) < len(response.json()["funcs"])
response = client.get("artifacts", params={"project": project_name, "tag": "*"})
assert response.status_code == HTTPStatus.OK.value
# ensure there are indeed several versions of the same artifact key
distinct_artifact_keys = {
(artifact["db_key"], artifact["iter"])
for artifact in response.json()["artifacts"]
}
assert len(distinct_artifact_keys) < len(response.json()["artifacts"])
response = client.get(
f"projects/{project_name}/feature-sets",
)
assert response.status_code == HTTPStatus.OK.value
distinct_feature_set_names = {
feature_set["metadata"]["name"]
for feature_set in response.json()["feature_sets"]
}
# ensure there are indeed several versions of the same feature_set name
assert len(distinct_feature_set_names) < len(response.json()["feature_sets"])
response = client.get(
f"projects/{project_name}/feature-vectors",
)
assert response.status_code == HTTPStatus.OK.value
distinct_feature_vector_names = {
feature_vector["metadata"]["name"]
for feature_vector in response.json()["feature_vectors"]
}
# ensure there are indeed several versions of the same feature_vector name
assert len(distinct_feature_vector_names) < len(response.json()["feature_vectors"])
mlrun.api.utils.singletons.db.get_db().delete_function = unittest.mock.Mock()
mlrun.api.utils.singletons.db.get_db().del_artifact = unittest.mock.Mock()
mlrun.api.utils.singletons.db.get_db().delete_feature_set = unittest.mock.Mock()
mlrun.api.utils.singletons.db.get_db().delete_feature_vector = unittest.mock.Mock()
# deletion strategy - check - should fail because there are resources
response = client.delete(
f"projects/{project_name}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.cascading.value
},
)
assert response.status_code == HTTPStatus.NO_CONTENT.value
assert mlrun.api.utils.singletons.db.get_db().delete_function.call_count == len(
distinct_function_names
)
assert mlrun.api.utils.singletons.db.get_db().del_artifact.call_count == len(
distinct_artifact_keys
)
assert mlrun.api.utils.singletons.db.get_db().delete_feature_set.call_count == len(
distinct_feature_set_names
)
assert (
mlrun.api.utils.singletons.db.get_db().delete_feature_vector.call_count
== len(distinct_feature_vector_names)
)
def test_delete_project_deletion_strategy_check_external_resource(
db: Session,
client: TestClient,
project_member_mode: str,
k8s_secrets_mock: tests.api.conftest.K8sSecretsMock,
) -> None:
project = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name="project-name"),
spec=mlrun.api.schemas.ProjectSpec(),
)
# create
response = client.post("projects", json=project.dict())
assert response.status_code == HTTPStatus.CREATED.value
_assert_project_response(project, response)
# Set a project secret
k8s_secrets_mock.store_project_secrets("project-name", {"secret": "value"})
# deletion strategy - check - should fail because there's a project secret
response = client.delete(
f"projects/{project.metadata.name}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.restricted.value
},
)
assert response.status_code == HTTPStatus.PRECONDITION_FAILED.value
assert "project secrets" in response.text
k8s_secrets_mock.delete_project_secrets("project-name", None)
response = client.delete(
f"projects/{project.metadata.name}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.restricted.value
},
)
assert response
# leader format is only relevant to follower mode
@pytest.mark.parametrize("project_member_mode", ["follower"], indirect=True)
def test_list_projects_leader_format(
db: Session, client: TestClient, project_member_mode: str
) -> None:
"""
See list_projects in follower.py for explanation on the rationality behind the leader format
"""
# create some projects in the db (mocking projects left there from before when leader format was used)
project_names = []
for _ in range(5):
project_name = f"prj-{uuid4().hex}"
project = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=project_name),
)
mlrun.api.utils.singletons.db.get_db().create_project(db, project)
project_names.append(project_name)
# list in leader format
response = client.get(
"projects",
params={"format": mlrun.api.schemas.ProjectsFormat.leader},
headers={
mlrun.api.schemas.HeaderNames.projects_role: mlrun.mlconf.httpdb.projects.leader
},
)
returned_project_names = [
project["data"]["metadata"]["name"] for project in response.json()["projects"]
]
assert (
deepdiff.DeepDiff(
project_names,
returned_project_names,
ignore_order=True,
)
== {}
)
def test_projects_crud(
db: Session,
client: TestClient,
project_member_mode: str,
k8s_secrets_mock: tests.api.conftest.K8sSecretsMock,
) -> None:
# need to set this to False, otherwise impl will try to delete k8s resources, and will need many more
# mocks to overcome this.
k8s_secrets_mock.set_is_running_in_k8s_cluster(False)
name1 = f"prj-{uuid4().hex}"
project_1 = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=name1),
spec=mlrun.api.schemas.ProjectSpec(
description="banana", source="source", goals="some goals"
),
)
# create
response = client.post("projects", json=project_1.dict())
assert response.status_code == HTTPStatus.CREATED.value
_assert_project_response(project_1, response)
# read
response = client.get(f"projects/{name1}")
_assert_project_response(project_1, response)
# patch
project_patch = {
"spec": {
"description": "lemon",
"desired_state": mlrun.api.schemas.ProjectState.archived,
}
}
response = client.patch(f"projects/{name1}", json=project_patch)
assert response.status_code == HTTPStatus.OK.value
_assert_project_response(
project_1, response, extra_exclude={"spec": {"description", "desired_state"}}
)
assert (
project_patch["spec"]["description"] == response.json()["spec"]["description"]
)
assert (
project_patch["spec"]["desired_state"]
== response.json()["spec"]["desired_state"]
)
assert project_patch["spec"]["desired_state"] == response.json()["status"]["state"]
name2 = f"prj-{uuid4().hex}"
labels_2 = {"key": "value"}
project_2 = mlrun.api.schemas.Project(
metadata=mlrun.api.schemas.ProjectMetadata(name=name2, labels=labels_2),
spec=mlrun.api.schemas.ProjectSpec(description="banana2", source="source2"),
)
# store
response = client.put(f"projects/{name2}", json=project_2.dict())
assert response.status_code == HTTPStatus.OK.value
_assert_project_response(project_2, response)
# list - names only
_list_project_names_and_assert(client, [name1, name2])
# list - names only - filter by label existence
_list_project_names_and_assert(
client, [name2], params={"label": list(labels_2.keys())[0]}
)
# list - names only - filter by label match
_list_project_names_and_assert(
client,
[name2],
params={"label": f"{list(labels_2.keys())[0]}={list(labels_2.values())[0]}"},
)
# list - full
response = client.get(
"projects", params={"format": mlrun.api.schemas.ProjectsFormat.full}
)
projects_output = mlrun.api.schemas.ProjectsOutput(**response.json())
expected = [project_1, project_2]
for project in projects_output.projects:
for _project in expected:
if _project.metadata.name == project.metadata.name:
_assert_project(
_project,
project,
extra_exclude={"spec": {"description", "desired_state"}},
)
expected.remove(_project)
break
# patch project 1 to have the labels as well
labels_1 = copy.deepcopy(labels_2)
labels_1.update({"another-label": "another-label-value"})
project_patch = {"metadata": {"labels": labels_1}}
response = client.patch(f"projects/{name1}", json=project_patch)
assert response.status_code == HTTPStatus.OK.value
_assert_project_response(
project_1,
response,
extra_exclude={
"spec": {"description", "desired_state"},
"metadata": {"labels"},
},
)
assert (
deepdiff.DeepDiff(
response.json()["metadata"]["labels"],
labels_1,
ignore_order=True,
)
== {}
)
# list - names only - filter by label existence
_list_project_names_and_assert(
client, [name1, name2], params={"label": list(labels_2.keys())[0]}
)
# list - names only - filter by label existence
_list_project_names_and_assert(
client, [name1], params={"label": list(labels_1.keys())[1]}
)
# list - names only - filter by state
_list_project_names_and_assert(
client, [name1], params={"state": mlrun.api.schemas.ProjectState.archived}
)
# add function to project 1
function_name = "function-name"
function = {"metadata": {"name": function_name}}
response = client.post(f"func/{name1}/{function_name}", json=function)
assert response.status_code == HTTPStatus.OK.value
# delete - restricted strategy, will fail because function exists
response = client.delete(
f"projects/{name1}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.restricted.value
},
)
assert response.status_code == HTTPStatus.PRECONDITION_FAILED.value
# delete - cascading strategy, will succeed and delete function
response = client.delete(
f"projects/{name1}",
headers={
mlrun.api.schemas.HeaderNames.deletion_strategy: mlrun.api.schemas.DeletionStrategy.cascading.value
},
)
assert response.status_code == HTTPStatus.NO_CONTENT.value
# ensure function is gone
response = client.get(f"func/{name1}/{function_name}")
assert response.status_code == HTTPStatus.NOT_FOUND.value
# list
_list_project_names_and_assert(client, [name2])
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": {"name": "run-name", "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 _assert_resources_in_project(
db_session: Session,
k8s_secrets_mock: tests.api.conftest.K8sSecretsMock,
project_member_mode: str,
project: str,
assert_no_resources: bool = False,
) -> typing.Tuple[typing.Dict, typing.Dict]:
object_type_records_count_map = {
"Logs": _assert_logs_in_project(project, assert_no_resources),
"Schedules": _assert_schedules_in_project(project, assert_no_resources),
}
secrets = (
{} if assert_no_resources else {f"secret_{i}": "a secret" for i in range(5)}
)
assert k8s_secrets_mock.get_project_secret_data(project) == secrets
return (
_assert_db_resources_in_project(
db_session, project_member_mode, project, assert_no_resources
),
object_type_records_count_map,
)
def _assert_schedules_in_project(
project: str,
assert_no_resources: bool = False,
) -> int:
number_of_schedules = len(
mlrun.api.utils.singletons.scheduler.get_scheduler()._list_schedules_from_scheduler(
project
)
)
if assert_no_resources:
assert number_of_schedules == 0
else:
assert number_of_schedules > 0
return number_of_schedules
def _assert_logs_in_project(