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, 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)