def get_latest_run_id(experiment_id):
"""
Get the latest run id for an experiment.
Parameters
----------
experiment_id : str
Returns
-------
str
"""
try:
kfp_experiment = kfp_client().get_experiment(experiment_name=experiment_id)
except ValueError:
return None
# lists runs for trainings and deployments of an experiment
kfp_runs = kfp_client().list_runs(
page_size="1",
sort_by="created_at desc",
experiment_id=kfp_experiment.id,
)
# find the latest training run
latest_run_id = None
for kfp_run in kfp_runs.runs:
latest_run_id = kfp_run.id
break
return latest_run_id
def retry_run(run_id, experiment_id):
"""
Retry a run in Kubeflow Pipelines.
Parameters
----------
run_id : str
experiment_id : str
Returns
-------
dict
Retry response confirmation.
Raises
------
ApiException
BadRequest
"""
if run_id == "latest":
run_id = get_latest_run_id(experiment_id)
kfp_run = kfp_client().get_run(
run_id=run_id,
)
if kfp_run.run.status == "Failed":
kfp_client().runs.retry_run(run_id=kfp_run.run.id)
else:
raise BadRequest("Not a failed run")
return {"message": "Run re-initiated successfully"}
def deploy_monitoring(deployment_id,
experiment_id,
run_id,
task_id,
monitoring_id):
"""
Deploy a service and trigger for monitoring.
Parameters
----------
deployment_id : str
experiment_id : str
run_id : str
task_id : str
monitoring_id : str
Returns
-------
dict
The run attributes.
"""
@dsl.pipeline(name="Monitoring")
def monitoring():
service_name = f"service-{monitoring_id}"
service = MONITORING_SERVICE.substitute({
"name": service_name,
"namespace": KF_PIPELINES_NAMESPACE,
"experimentId": experiment_id,
"deploymentId": deployment_id,
"runId": run_id,
"configMap": f"configmap-{task_id}"
})
service_resource = loads(service)
monitoring_service = dsl.ResourceOp(
name=service_name,
k8s_resource=service_resource,
success_condition="status.conditions.1.status == True"
)
trigger_name = f"trigger-{monitoring_id}"
trigger = MONITORING_TRIGGER.substitute({
"name": trigger_name,
"namespace": KF_PIPELINES_NAMESPACE,
"deploymentId": deployment_id,
"service": service_name,
})
trigger_resource = loads(trigger)
dsl.ResourceOp(
name="monitoring_trigger",
k8s_resource=trigger_resource,
success_condition="status.conditions.2.status == True"
).after(monitoring_service)
kfp_client().create_run_from_pipeline_func(
monitoring,
{},
run_name="monitoring",
namespace=KF_PIPELINES_NAMESPACE
)
def start_run(operators, project_id, experiment_id, deployment_id=None, deployment_name=None):
"""
Start a new run in Kubeflow Pipelines.
Parameters
----------
operators : list
project_id : str
experiment_id : str
deployment_id : str or None
deployment_name : str or None
Returns
-------
dict
The run attributes.
"""
if len(operators) == 0:
raise ValueError("Necessary at least one operator.")
if deployment_id is None:
name = f"experiment-{experiment_id}"
else:
name = f"deployment-{deployment_id}"
if not deployment_name:
deployment_name = deployment_id
compile_pipeline(name=name,
operators=operators,
project_id=project_id,
experiment_id=experiment_id,
deployment_id=deployment_id,
deployment_name=deployment_name)
if deployment_id is not None:
kfp_experiment = kfp_client().create_experiment(name=deployment_id)
else:
kfp_experiment = kfp_client().create_experiment(name=experiment_id)
tag = datetime.utcnow().strftime("%Y-%m-%d %H-%M-%S")
job_name = f"{name}-{tag}"
pipeline_package_path = f"{name}.yaml"
run = kfp_client().run_pipeline(
experiment_id=kfp_experiment.id,
job_name=job_name,
pipeline_package_path=pipeline_package_path,
)
os.remove(pipeline_package_path)
return get_run(run.id, experiment_id)
def undeploy_pipeline(resource):
"""
Undeploy a deployment pipeline.
Parameters
----------
resource : dict
A k8s resource which will be submitted to the cluster.
"""
@dsl.pipeline(name="Undeploy")
def undeploy():
dsl.ResourceOp(name="undeploy", k8s_resource=resource, action="delete")
kfp_client().create_run_from_pipeline_func(
undeploy, {}, run_name="undeploy", namespace=KF_PIPELINES_NAMESPACE)
def list_deployments_runs():
"""
Retrive runs associated with a deployment.
Returns
-------
list
Deployments runs list.
"""
token = ""
runs = []
while True:
list_runs = kfp_client().list_runs(page_token=token,
sort_by="created_at desc",
page_size=100)
if list_runs.runs:
runs_details = get_deployment_details(list_runs.runs)
runs.extend(runs_details)
token = list_runs.next_page_token
if token is None:
break
else:
break
return runs
def terminate_run(self, deployment_id):
"""
Terminates a run in Kubeflow Pipelines.
Parameters
----------
deployment_id : str
Returns
-------
projects.schemas.message.Message
Raises
------
NotFound
When deployment run does not exist.
"""
load_kube_config()
api = client.CustomObjectsApi()
custom_objects = api.list_namespaced_custom_object(
"machinelearning.seldon.io",
"v1",
KF_PIPELINES_NAMESPACE,
"seldondeployments",
)
deployments_objects = custom_objects["items"]
if deployments_objects:
for deployment in deployments_objects:
if deployment["metadata"]["name"] == deployment_id:
undeploy_pipeline(
name=deployment["metadata"]["name"],
kind=deployment["kind"],
namespace=deployment["metadata"]["namespace"],
)
deployment_run = get_deployment_runs(deployment_id)
if not deployment_run:
raise NotFound(code="RunNotFound",
message="The specified run does not exist.")
kfp_client().runs.delete_run(deployment_run["runId"])
return schemas.Message(message="Deployment deleted")
def terminate_run(run_id, experiment_id):
"""
Terminates a run in Kubeflow Pipelines.
Parameters
----------
run_id : str
experiment_id : str
Returns
-------
dict
Deleted response confirmation.
Raises
------
ApiException
"""
if run_id == "latest":
run_id = get_latest_run_id(experiment_id)
kfp_client().runs.terminate_run(run_id=run_id)
return {"message": "Run terminated."}
def undeploy_pipeline(name, kind, namespace):
"""
Undeploy a deployment pipeline.
Parameters
----------
name : str
kind : str
namespace : str
"""
@dsl.pipeline(name="Undeploy")
def undeploy():
kubernetes_resource_delete_op(
name=name,
kind=kind,
namespace=namespace,
)
kfp_client().create_run_from_pipeline_func(
undeploy,
{},
run_name="undeploy",
)
def list_runs(experiment_id):
"""
Lists all runs of an experiment.
Parameters
----------
experiment_id : str
Returns
-------
list
A list of all runs.
"""
# In order to list_runs, we need to find KFP experiment id.
# KFP experiment id is different from PlatIAgro's experiment_id,
# so calling kfp_client().get_experiment(experiment_name='..') is required first.
try:
kfp_experiment = kfp_client().get_experiment(experiment_name=experiment_id)
except ValueError:
return []
# Now, lists runs
kfp_runs = kfp_client().list_runs(
page_size="10",
sort_by="created_at desc",
experiment_id=kfp_experiment.id,
)
runs = []
for kfp_run in kfp_runs.runs:
run_id = kfp_run.id
run = get_run(experiment_id=experiment_id,
run_id=run_id)
runs.append(run)
return runs
def get_container_status(experiment_id, operator_id):
"""
Get operator container status.
Parameters
----------
experiment_id : str
operator_id : str
Returns
-------
str
The container status.
"""
# always get the status from the latest run
run_id = get_latest_run_id(experiment_id)
try:
kfp_run = kfp_client().get_run(run_id=run_id)
found_operator = False
workflow_manifest = json.loads(kfp_run.pipeline_runtime.workflow_manifest)
workflow_status = workflow_manifest["status"].get("phase")
if workflow_status in {"Succeeded", "Failed"}:
status = "Unset"
else:
status = "Pending"
for node in workflow_manifest["status"].get("nodes", {}).values():
if node["displayName"] == operator_id:
found_operator = True
if "message" in node and str(node["message"]) == "terminated":
status = "Terminated"
else:
status = str(node["phase"])
break
if found_operator and workflow_status == "Failed" and status == "Pending":
status = "Failed"
return status
except ApiValueError:
return ""
def get_logs(self, project_id: str, experiment_id: str, run_id: str,
operator_id: str):
"""
Get logs from a experiment run.
Parameters
----------
project_id : str
experiment_id : str
run_id : str
The run_id. If `run_id=latest`, then returns logs from the latest run_id.
operator_id : str
Returns
-------
dict
A dict of logs from a run.
"""
logs = get_notebook_logs(experiment_id=experiment_id,
operator_id=operator_id)
if not logs:
# No notebooks or logs were found in the Jupyter API.
# Search for logs in the operator pod details.
if run_id == "latest":
run_id = get_latest_run_id(experiment_id)
run_details = kfp_client().get_run(run_id=run_id)
details = loads(run_details.pipeline_runtime.workflow_manifest)
operator = search_for_pod_info(details, operator_id)
if operator and operator["status"] == "Failed":
logs = {
"exception": operator["message"],
"traceback": [f"Kernel has died: {operator['message']}"]
}
else:
logs = {"message": "Notebook finished with status completed."}
return logs
def make_task_deletion_job(
task: models.Task, all_tasks: List[models.Task], namespace: str
):
"""
Runs a Kubeflow Pipeline that deletes the volume of a task and removes it from JupyterLab.
Parameters
----------
task : models.Task
all_tasks : List[models.Task]
namespace : str
Returns
-------
RunPipelineResult
"""
@dsl.pipeline(
name="Delete Task",
description="A pipeline that deletes K8s resources associated with a given task.",
)
def pipeline_func():
# Patches JupyterLab to remove volumeMount
resource_op = patch_notebook_volume_mounts_op(
tasks=all_tasks, namespace=namespace
)
delete_volume_op(name=f"task-{task.uuid}", namespace=namespace).after(
resource_op
)
run_name = f"Delete Task - {task.name}"
return kfp_client().create_run_from_pipeline_func(
pipeline_func=pipeline_func,
arguments={},
run_name=run_name,
experiment_name=task.uuid,
)
def setUp(self):
self.maxDiff = None
with open("tests/resources/mocked_deployment.yaml", "r") as file:
content = file.read()
content = content.replace("$deploymentId", DEPLOYMENT_ID)
with open("tests/resources/mocked.yaml", "w") as file:
file.write(content)
kfp_experiment = kfp_client().create_experiment(name=DEPLOYMENT_ID)
kfp_client().run_pipeline(
experiment_id=kfp_experiment.id,
job_name=DEPLOYMENT_ID,
pipeline_package_path="tests/resources/mocked.yaml",
)
conn = engine.connect()
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, parameters, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
TASK_ID,
'name',
'desc',
IMAGE,
None,
None,
TAGS_JSON,
dumps([]),
EX_NOTEBOOK_PATH,
DEPLOY_NOTEBOOK_PATH,
0,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, parameters, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
TASK_ID_2,
'name',
'desc',
IMAGE,
None,
None,
TAGS_JSON,
dumps([]),
EX_NOTEBOOK_PATH,
None,
0,
CREATED_AT,
UPDATED_AT,
))
text = (f"INSERT INTO projects (uuid, name, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s)")
conn.execute(text, (
PROJECT_ID,
NAME,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO experiments (uuid, name, project_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
EXPERIMENT_ID,
NAME,
PROJECT_ID,
POSITION,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO deployments (uuid, name, project_id, experiment_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
DEPLOYMENT_ID,
NAME,
PROJECT_ID,
EXPERIMENT_ID,
POSITION,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO deployments (uuid, name, project_id, experiment_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
DEPLOYMENT_ID_2,
NAME,
PROJECT_ID,
EXPERIMENT_ID,
POSITION,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, deployment_id, task_id, parameters, dependencies, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID,
EXPERIMENT_ID,
DEPLOYMENT_ID,
TASK_ID_2,
PARAMETERS_JSON,
DEP_EMPTY_JSON,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, deployment_id, task_id, parameters, dependencies, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID_2,
EXPERIMENT_ID,
DEPLOYMENT_ID,
TASK_ID,
PARAMETERS_JSON,
dumps([OPERATOR_ID]),
CREATED_AT,
UPDATED_AT,
))
conn.close()
def make_task_creation_job(
task: models.Task,
all_tasks: List[models.Task],
namespace: str,
copy_from: Optional[models.Task] = None,
):
"""
Runs a Kubeflow Pipeline that creates all resources necessary for a new task.
Creates a persistent volume, copies files into the volume, and patches
notebook server (updates volumes and volumeMounts).
Parameters
----------
task : models.Task
all_tasks : List[models.Task]
namespace : str
copy_from : models.Task, optional
Returns
-------
RunPipelineResult
"""
@dsl.pipeline(
name="Create Task",
description="A pipeline that creates all resources necessary for a new task.",
)
def pipeline_func():
# Creates a volume for this task
volume_op_task = create_volume_op(name=f"task-{task.uuid}", namespace=namespace)
# And a ContainerOp to initialize task contents
# Either the contents of other task are copied,
# or some empty notebooks are copied into the volume
container_op = create_init_task_container_op(copy_from=copy_from)
container_op.add_pvolumes(
{DESTINATION_TASK_VOLUME_MOUNT_PATH: volume_op_task.volume}
)
container_op.set_timeout(DEFAULT_TIMEOUT_IN_SECONDS)
if copy_from:
# If task is a copy, also adds a volume mount to the source task volume
volume_op_task = create_volume_op(
name=f"task-{copy_from.uuid}", namespace=namespace
)
container_op.add_pvolumes(
{SOURCE_TASK_VOLUME_MOUNT_PATH: volume_op_task.volume}
)
if task.category == "MONITORING":
# if it is a "MONITORING task", creates a configmap using the contents of this task.
# knative serving (monitoring service) does not support stateful resources like
# persistentvolumes, so we have to use a configmap to create a volumeMount into
# the monitoring service.
# TODO add real task content
create_configmap_op(task=task, namespace=namespace, content="")
# Patches JupyterLab to mount new task volume
patch_notebook_volume_mounts_op(tasks=all_tasks, namespace=namespace).after(
container_op
)
run_name = f"Create Task - {task.name}"
return kfp_client().create_run_from_pipeline_func(
pipeline_func=pipeline_func,
arguments={},
run_name=run_name,
experiment_name=task.uuid,
namespace=namespace,
)
def setUp(self):
self.maxDiff = None
# Run experiment to succeed
experiment = kfp_client().create_experiment(name=EXPERIMENT_ID)
self.run = kfp_client().run_pipeline(
experiment.id, OPERATOR_ID_2,
"tests/resources/mocked_operator_succeed.yaml")
session = requests.Session()
session.cookies.update(COOKIES)
session.headers.update(HEADERS)
session.hooks = {
"response": lambda r, *args, **kwargs: r.raise_for_status(),
}
conn = engine.connect()
text = (
f"INSERT INTO projects (uuid, name, description, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s)")
conn.execute(text, (
PROJECT_ID,
NAME,
DESCRIPTION,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO experiments (uuid, name, project_id, position, is_active, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
EXPERIMENT_ID,
EXPERIMENT_NAME,
PROJECT_ID,
0,
1,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO tasks (uuid, name, description, image, commands, arguments, tags, parameters, experiment_notebook_path, deployment_notebook_path, is_default, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
TASK_ID,
NAME,
DESCRIPTION,
IMAGE,
COMMANDS_JSON,
ARGUMENTS_JSON,
TAGS_JSON,
dumps([]),
EXPERIMENT_NOTEBOOK_PATH,
EXPERIMENT_NOTEBOOK_PATH,
0,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, task_id, parameters, position_x, position_y, dependencies, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID,
EXPERIMENT_ID,
TASK_ID,
PARAMETERS_JSON,
POSITION_X,
POSITION_Y,
DEPENDENCIES_OP_ID_JSON,
CREATED_AT,
UPDATED_AT,
))
text = (
f"INSERT INTO operators (uuid, experiment_id, task_id, parameters, position_x, position_y, dependencies, created_at, updated_at) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)")
conn.execute(text, (
OPERATOR_ID_2,
EXPERIMENT_ID,
TASK_ID,
PARAMETERS_JSON,
POSITION_X,
POSITION_Y,
DEPENDENCIES_OP_ID_JSON,
CREATED_AT,
UPDATED_AT,
))
conn.close()
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/experiments",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=f"{JUPYTER_ENDPOINT}/api/contents/experiments/{EXPERIMENT_ID}",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/experiments/{EXPERIMENT_ID}/operators",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/experiments/{EXPERIMENT_ID}/operators/{OPERATOR_ID}",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/experiments/{EXPERIMENT_ID}/operators/{OPERATOR_ID}/Experiment.ipynb",
data=dumps({
"type": "notebook",
"content": loads(SAMPLE_FAILED_NOTEBOOK)
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/experiments/{EXPERIMENT_ID}/operators/{OPERATOR_ID_2}",
data=dumps({
"type": "directory",
"content": None
}),
)
session.put(
url=
f"{JUPYTER_ENDPOINT}/api/contents/experiments/{EXPERIMENT_ID}/operators/{OPERATOR_ID_2}/Experiment.ipynb",
data=dumps({
"type": "notebook",
"content": loads(SAMPLE_COMPLETED_NOTEBOOK)
}),
)
def get_run(run_id, experiment_id):
"""
Details a run in Kubeflow Pipelines.
Parameters
----------
run_id : str
experiment_id : str
Returns
-------
dict
The run attributes.
Raises
------
ApiException
ValueError
"""
if run_id == "latest":
run_id = get_latest_run_id(experiment_id)
kfp_run = kfp_client().get_run(
run_id=run_id,
)
workflow_manifest = json.loads(kfp_run.pipeline_runtime.workflow_manifest)
operators = {}
workflow_status = workflow_manifest["status"].get("phase")
if workflow_status in {"Succeeded", "Failed"}:
default_node_status = "Unset"
else:
default_node_status = "Pending"
# initializes all operators with status=Pending and parameters={}
template = next(t for t in workflow_manifest["spec"]["templates"] if "dag" in t)
tasks = (tsk for tsk in template["dag"]["tasks"] if not tsk["name"].startswith("vol-"))
operators = dict((t["name"], {"status": default_node_status, "parameters": {}}) for t in tasks)
# set status for each operator
for node in workflow_manifest["status"].get("nodes", {}).values():
if node["displayName"] in operators:
operator_id = node["displayName"]
operators[operator_id]["status"] = get_status(node)
# sets taskId and parameters for each operator
for template in workflow_manifest["spec"]["templates"]:
operator_id = template["name"]
if "inputs" in template and "parameters" in template["inputs"]:
operators[operator_id]["taskId"] = get_task_id(template)
if "container" in template and "env" in template["container"]:
operators[operator_id]["parameters"] = get_parameters(template)
return {
"uuid": kfp_run.run.id,
"operators": operators,
"createdAt": kfp_run.run.created_at,
}
