def get_pipeline(run_id, namespace=None):
"""Get Pipeline status
:param run_id: id of pipelines run
:param namespace: k8s namespace if not default
:return: kfp run dict
"""
namespace = namespace or mlconf.namespace
remote = not get_k8s_helper(
silent=True).is_running_inside_kubernetes_cluster()
if remote:
mldb = get_run_db()
if mldb.kind != "http":
raise ValueError(
"get pipeline require access to remote api-service"
", please set the dbpath url")
resp = mldb.get_pipeline(run_id, namespace=namespace)
else:
client = Client(namespace=namespace)
resp = client.get_run(run_id)
if resp:
resp = resp.to_dict()
return resp
def _mock_get_run(
kfp_client_mock: kfp.Client,
api_run_detail: kfp_server_api.models.api_run_detail.ApiRunDetail,
):
def get_run_mock(*args, **kwargs):
return api_run_detail
kfp_client_mock.get_run = get_run_mock
def get_pipline(run_id, wait=0, namespace=None):
"""Get or wait for Pipeline status, wait time in sec"""
client = Client(namespace=namespace or mlconf.namespace)
if wait:
resp = client.wait_for_run_completion(run_id, wait)
else:
resp = client.get_run(run_id)
return resp
def get_pipeline(
run_id,
namespace=None,
format_: Union[str, mlrun.api.schemas.PipelinesFormat] = mlrun.api.schemas.
PipelinesFormat.summary,
project: str = None,
remote: bool = True,
):
"""Get Pipeline status
:param run_id: id of pipelines run
:param namespace: k8s namespace if not default
:param format_: Format of the results. Possible values are:
- ``summary`` (default value) - Return summary of the object data.
- ``full`` - Return full pipeline object.
:param project: the project of the pipeline run
:param remote: read kfp data from mlrun service (default=True)
:return: kfp run dict
"""
namespace = namespace or mlconf.namespace
if remote:
mldb = get_run_db()
if mldb.kind != "http":
raise ValueError(
"get pipeline require access to remote api-service"
", please set the dbpath url")
resp = mldb.get_pipeline(run_id,
namespace=namespace,
format_=format_,
project=project)
else:
client = Client(namespace=namespace)
resp = client.get_run(run_id)
if resp:
resp = resp.to_dict()
if (not format_ or format_
== mlrun.api.schemas.PipelinesFormat.summary.value):
resp = format_summary_from_kfp_run(resp)
show_kfp_run(resp)
return resp
