def find_pipeline_id(pipeline_name: str,
client: kfp.Client,
page_size: str = 100,
page_token: str = "") -> str:
"""Function to find the pipeline id of a pipeline.
Arguments:
pipeline_name {str} -- The name of the pipeline of interest
client {kfp.Client} -- The kfp client
page_size {str} -- The number of pipelines to collect a each API request
Keyword Arguments:
page_token {str} -- The page token to use for the API request (default: {" "})
Returns:
[type] -- The pipeline id. If None no match
"""
while True:
pipelines = client.list_pipelines(page_size=page_size,
page_token=page_token)
for pipeline in pipelines.pipelines:
if pipeline.name == pipeline_name:
logging.info(f"The pipeline id is: {pipeline.id}")
return pipeline.id
# Start need to know where to do next itteration from
page_token = pipelines.next_page_token
# If no next tooken break
if not page_token:
logging.info(
f"Could not find the pipeline, is the name: {pipeline_name} correct?"
)
break
def pipeline_id(client: kfp.Client, name: str):
"""Gets the ID of the kubeflow pipeline with the name 'name'
Args:
name of the pipeline
Returns:
id of the pipeline
"""
page_token = ""
while page_token is not None:
p = client.list_pipelines(page_token=page_token, page_size=100)
if p.pipelines is None:
return ""
for p in p.pipelines:
if p.name == name:
return p.id
page_token = p.next_page_token
return ""
class KubeflowClient(object):
log = logging.getLogger(__name__)
def __init__(self, config, project_name, context):
token = AuthHandler().obtain_id_token()
self.host = config.host
self.client = Client(self.host, existing_token=token)
self.project_name = project_name
self.pipeline_description = config.run_config.description
self.generator = PipelineGenerator(config, project_name, context)
def list_pipelines(self):
pipelines = self.client.list_pipelines(page_size=30).pipelines
return tabulate(map(lambda x: [x.name, x.id], pipelines),
headers=["Name", "ID"])
def run_once(
self,
pipeline,
image,
experiment_name,
run_name,
wait,
image_pull_policy="IfNotPresent",
) -> None:
run = self.client.create_run_from_pipeline_func(
self.generator.generate_pipeline(pipeline, image,
image_pull_policy),
arguments={},
experiment_name=experiment_name,
run_name=run_name,
)
if wait:
run.wait_for_run_completion(timeout=WAIT_TIMEOUT)
def compile(self,
pipeline,
image,
output,
image_pull_policy="IfNotPresent"):
Compiler().compile(
self.generator.generate_pipeline(pipeline, image,
image_pull_policy),
output,
)
self.log.info("Generated pipeline definition was saved to %s" % output)
def upload(self, pipeline, image, image_pull_policy="IfNotPresent"):
pipeline = self.generator.generate_pipeline(pipeline, image,
image_pull_policy)
if self._pipeline_exists(self.project_name):
pipeline_id = self._get_pipeline_id(self.project_name)
version_id = self._upload_pipeline_version(pipeline, pipeline_id)
self.log.info("New version of pipeline created: %s", version_id)
else:
(pipeline_id, version_id) = self._upload_pipeline(pipeline)
self.log.info("Pipeline created")
self.log.info(
f"Pipeline link: {self.host}/#/pipelines/details/%s/version/%s",
pipeline_id,
version_id,
)
def _pipeline_exists(self, pipeline_name):
return self._get_pipeline_id(pipeline_name) is not None
def _get_pipeline_id(self, pipeline_name):
pipelines = self.client.pipelines.list_pipelines(filter=json.dumps({
"predicates": [{
"key": "name",
"op": 1,
"string_value": pipeline_name,
}]
})).pipelines
if pipelines:
return pipelines[0].id
def _upload_pipeline_version(self, pipeline_func, pipeline_id):
version_name = f"{clean_name(self.project_name)}-{uuid.uuid4()}"[:100]
with NamedTemporaryFile(suffix=".yaml") as f:
Compiler().compile(pipeline_func, f.name)
return self.client.pipeline_uploads.upload_pipeline_version(
f.name,
name=version_name,
pipelineid=pipeline_id,
_request_timeout=10000,
).id
def _upload_pipeline(self, pipeline_func):
with NamedTemporaryFile(suffix=".yaml") as f:
Compiler().compile(pipeline_func, f.name)
pipeline = self.client.pipeline_uploads.upload_pipeline(
f.name,
name=self.project_name,
description=self.pipeline_description,
_request_timeout=10000,
)
return (pipeline.id, pipeline.default_version.id)
def _ensure_experiment_exists(self, experiment_name):
try:
experiment = self.client.get_experiment(
experiment_name=experiment_name)
self.log.info(f"Existing experiment found: {experiment.id}")
except ValueError as e:
if not str(e).startswith("No experiment is found"):
raise
experiment = self.client.create_experiment(experiment_name)
self.log.info(f"New experiment created: {experiment.id}")
return experiment.id
def schedule(self, experiment_name, cron_expression):
experiment_id = self._ensure_experiment_exists(experiment_name)
pipeline_id = self._get_pipeline_id(self.project_name)
self._disable_runs(experiment_id, pipeline_id)
self.client.create_recurring_run(
experiment_id,
f"{self.project_name} on {cron_expression}",
cron_expression=cron_expression,
pipeline_id=pipeline_id,
)
self.log.info("Pipeline scheduled to %s", cron_expression)
def _disable_runs(self, experiment_id, pipeline_id):
runs = self.client.list_recurring_runs(experiment_id=experiment_id)
if runs.jobs is not None:
my_runs = [
job for job in runs.jobs
if job.pipeline_spec.pipeline_id == pipeline_id
]
for job in my_runs:
self.client.jobs.delete_job(job.id)
self.log.info(f"Previous schedule deleted {job.id}")
class KubeflowClient(object):
log = logging.getLogger(__name__)
def __init__(self, config, project_name, context):
token = self.obtain_id_token()
self.host = config.host
self.client = Client(self.host, existing_token=token)
self.project_name = project_name
self.context = context
dsl.ContainerOp._DISABLE_REUSABLE_COMPONENT_WARNING = True
self.volume_meta = config.run_config.volume
def list_pipelines(self):
pipelines = self.client.list_pipelines(page_size=30).pipelines
return tabulate(map(lambda x: [x.name, x.id], pipelines),
headers=["Name", "ID"])
def run_once(
self,
pipeline,
image,
experiment_name,
run_name,
wait,
image_pull_policy="IfNotPresent",
) -> None:
run = self.client.create_run_from_pipeline_func(
self.generate_pipeline(pipeline, image, image_pull_policy),
arguments={},
experiment_name=experiment_name,
run_name=run_name,
)
if wait:
run.wait_for_run_completion(timeout=WAIT_TIMEOUT)
def obtain_id_token(self):
from google.auth.transport.requests import Request
from google.oauth2 import id_token
from google.auth.exceptions import DefaultCredentialsError
client_id = os.environ.get(IAP_CLIENT_ID, None)
jwt_token = None
if not client_id:
self.log.info(
"No IAP_CLIENT_ID provided, skipping custom IAP authentication"
)
return jwt_token
try:
self.log.debug("Obtaining JWT token for %s." + client_id)
jwt_token = id_token.fetch_id_token(Request(), client_id)
self.log.info("Obtained JWT token for MLFLOW connectivity.")
except DefaultCredentialsError as ex:
self.log.warning(
str(ex) +
(" Note that this authentication method does not work with default"
" credentials obtained via 'gcloud auth application-default login'"
" command. Refer to documentation on how to configure service account"
" locally"
" (https://cloud.google.com/docs/authentication/production#manually)"
))
except Exception as e:
self.log.error("Failed to obtain IAP access token. " + str(e))
finally:
return jwt_token
def generate_pipeline(self, pipeline, image, image_pull_policy):
@dsl.pipeline(
name=self.project_name,
description="Kubeflow pipeline for Kedro project",
)
def convert_kedro_pipeline_to_kfp() -> None:
"""Convert from a Kedro pipeline into a kfp container graph."""
node_volumes = (_setup_volumes()
if self.volume_meta is not None else {})
node_dependencies = self.context.pipelines.get(
pipeline).node_dependencies
kfp_ops = _build_kfp_ops(node_dependencies, node_volumes)
for node, dependencies in node_dependencies.items():
for dependency in dependencies:
kfp_ops[node.name].after(kfp_ops[dependency.name])
def _setup_volumes():
vop = dsl.VolumeOp(
name="data-volume-create",
resource_name="data-volume",
size=self.volume_meta.size,
modes=self.volume_meta.access_modes,
storage_class=self.volume_meta.storageclass,
)
if self.volume_meta.skip_init:
return {"/home/kedro/data": vop.volume}
else:
volume_init = dsl.ContainerOp(
name="data-volume-init",
image=image,
command=["sh", "-c"],
arguments=[
" ".join([
"cp",
"--verbose",
"-r",
"/home/kedro/data/*",
"/home/kedro/datavolume",
])
],
pvolumes={"/home/kedro/datavolume": vop.volume},
)
volume_init.container.set_image_pull_policy(image_pull_policy)
return {"/home/kedro/data": volume_init.pvolume}
def _build_kfp_ops(node_dependencies: Dict[Node, Set[Node]],
node_volumes: Dict) -> Dict[str, dsl.ContainerOp]:
"""Build kfp container graph from Kedro node dependencies. """
kfp_ops = {}
env = [
V1EnvVar(name=IAP_CLIENT_ID,
value=os.environ.get(IAP_CLIENT_ID, ""))
]
if is_mlflow_enabled():
kfp_ops["mlflow-start-run"] = dsl.ContainerOp(
name="mlflow-start-run",
image=image,
command=["kedro"],
arguments=[
"kubeflow",
"mlflow-start",
dsl.RUN_ID_PLACEHOLDER,
],
file_outputs={"mlflow_run_id": "/tmp/mlflow_run_id"},
)
kfp_ops["mlflow-start-run"].container.set_image_pull_policy(
image_pull_policy)
env.append(
V1EnvVar(
name="MLFLOW_RUN_ID",
value=kfp_ops["mlflow-start-run"].output,
))
for node in node_dependencies:
name = _clean_name(node.name)
kfp_ops[node.name] = dsl.ContainerOp(
name=name,
image=image,
command=["kedro"],
arguments=["run", "--node", node.name],
pvolumes=node_volumes,
container_kwargs={"env": env},
)
kfp_ops[node.name].container.set_image_pull_policy(
image_pull_policy)
return kfp_ops
return convert_kedro_pipeline_to_kfp
def compile(self,
pipeline,
image,
output,
image_pull_policy="IfNotPresent"):
Compiler().compile(
self.generate_pipeline(pipeline, image, image_pull_policy), output)
self.log.info("Generated pipeline definition was saved to %s" % output)
def upload(self, pipeline, image, image_pull_policy="IfNotPresent"):
pipeline = self.generate_pipeline(pipeline, image, image_pull_policy)
if self._pipeline_exists(self.project_name):
pipeline_id = self._get_pipeline_id(self.project_name)
version_id = self._upload_pipeline_version(pipeline, pipeline_id,
self.project_name)
self.log.info("New version of pipeline created: %s", version_id)
else:
(pipeline_id,
version_id) = self._upload_pipeline(pipeline, self.project_name)
self.log.info("Pipeline created")
self.log.info(
f"Pipeline link: {self.host}/#/pipelines/details/%s/version/%s",
pipeline_id,
version_id,
)
def _pipeline_exists(self, pipeline_name):
return self._get_pipeline_id(pipeline_name) is not None
def _get_pipeline_id(self, pipeline_name):
pipelines = self.client.pipelines.list_pipelines(filter=json.dumps({
"predicates": [{
"key": "name",
"op": 1,
"string_value": pipeline_name,
}]
})).pipelines
if pipelines:
return pipelines[0].id
def _upload_pipeline_version(self, pipeline_func, pipeline_id,
pipeline_name):
version_name = f"{_clean_name(pipeline_name)}-{uuid.uuid4()}"[:100]
with NamedTemporaryFile(suffix=".yaml") as f:
Compiler().compile(pipeline_func, f.name)
return self.client.pipeline_uploads.upload_pipeline_version(
f.name, name=version_name, pipelineid=pipeline_id).id
def _upload_pipeline(self, pipeline_func, pipeline_name):
with NamedTemporaryFile(suffix=".yaml") as f:
Compiler().compile(pipeline_func, f.name)
pipeline = self.client.pipeline_uploads.upload_pipeline(
f.name, name=pipeline_name)
return (pipeline.id, pipeline.default_version.id)
def _ensure_experiment_exists(self, experiment_name):
try:
experiment = self.client.get_experiment(
experiment_name=experiment_name)
self.log.info(f"Existing experiment found: {experiment.id}")
except ValueError as e:
if not str(e).startswith("No experiment is found"):
raise
experiment = self.client.create_experiment(experiment_name)
self.log.info(f"New experiment created: {experiment.id}")
return experiment.id
def schedule(self, experiment_name, cron_expression):
experiment_id = self._ensure_experiment_exists(experiment_name)
pipeline_id = self._get_pipeline_id(self.project_name)
self._disable_runs(experiment_id, pipeline_id)
self.client.create_recurring_run(
experiment_id,
f"{self.project_name} on {cron_expression}",
cron_expression=cron_expression,
pipeline_id=pipeline_id,
)
self.log.info("Pipeline scheduled to %s", cron_expression)
def _disable_runs(self, experiment_id, pipeline_id):
runs = self.client.list_recurring_runs(experiment_id=experiment_id)
if runs.jobs is not None:
my_runs = [
job for job in runs.jobs
if job.pipeline_spec.pipeline_id == pipeline_id
]
for job in my_runs:
self.client.jobs.delete_job(job.id)
self.log.info(f"Previous schedule deleted {job.id}")
class KubeflowClient:
"""
A wrapper of the existing Kubeflow Pipelines Client which enriches it to
be able to access more of the Kubeflow Pipelines API.
"""
def __init__(self,
host: Optional[str] = None,
client_id: Optional[str] = None,
namespace: Optional[str] = "kubeflow"):
"""
Instandiate a new KubeflowClient
Args:
host (str): The host we can find the Kubeflow API at (e.g. https://{APP_NAME}.endpoints.{PROJECT_ID}.cloud.goog/pipeline)
client_id (str): The IAP client id we can use for authorisate (e.g. "XXXXXX-XXXXXXXXX.apps.googleusercontent.com")
namespace (str): The Kuberenetes / Kubeflow namespace to deploy to (e.g. kubeflow)
"""
self.host = host
self.client_id = client_id
self.namespace = namespace
logging.info(f"KubeflowClient: host: {host}, client_id: {client_id}")
self.kfp_client = Client(host, client_id, namespace)
self.config = self.kfp_client._load_config(self.host, self.client_id,
self.namespace, None, None)
# print(f"kfp auth:")
# print(f"\thost: {self.host}")
# print(f"\tclient_id: {self.client_id}")
# print(f"\tnamespace: {self.namespace}")
# print(f"\tapi_key: {self.config.api_key}")
self.kfp_pipelines = self._connect_pipelines_api()
self.kfp_runs = self._connect_runs_api()
self.kfp_jobs = self._connect_jobs_api()
def create_pipeline(self, pipeline_func, pipeline_name):
"""
Create a new Kubeflow Pipeline using the provided pipeline function
Args:
pipeline_func: The method decorated by @dsl.pipeline which defines the pipeline
Returns:
The Kubeflow Pipeline object created
"""
try:
(_, pipeline_package_path) = tempfile.mkstemp(suffix=".zip")
compiler.Compiler().compile(pipeline_func, pipeline_package_path)
logging.info(f"Compiled piopeline to: {pipeline_package_path}")
return self.kfp_client.upload_pipeline(pipeline_package_path,
pipeline_name)
finally:
pass
# os.remove(pipeline_package_path)
def create_experiment(self, experiment_name):
"""
Create a new Kubeflow Pipelines Experiment (grouping of pipeliens / runs)
Args:
experiment_name (str): The name of the experiment
Returns:
The Kubeflow experiement object created
"""
return self.kfp_client.create_experiment(name=experiment_name)
def list_experiments(self):
"""
List the Experiments in the current namespace
Returns:
A list of all the Experiments
"""
all_experiments = list()
next_page_token = ""
while next_page_token is not None:
response = self.kfp_client.list_experiments(
page_size=100, page_token=next_page_token)
if response.experiments is None:
break
all_experiments.extend(response.experiments)
next_page_token = response.next_page_token
count = len(all_experiments)
# print(f"list_experiments: found {count}")
return all_experiments
def find_job(self, job_name):
"""
Look up a job by its name (in the current namespace). Returns
None if the job cannot be found
Args:
job_name (str): The name of the job to find
Returns:
A reference to the job if found, and None if not.
"""
jobs = self.list_jobs()
if jobs is None:
return None
for j in jobs:
if j.name == job_name:
return j
return None
def list_jobs(self):
"""
List the Jobs in the current namespace
Returns:
A list of all the Jobs
"""
all_jobs = list()
next_page_token = ""
while next_page_token is not None:
response = self.kfp_jobs.list_jobs(page_size=100,
page_token=next_page_token)
if response.jobs is None:
break
all_jobs.extend(response.jobs)
next_page_token = response.next_page_token
count = len(all_jobs)
# print(f"all_jobs: found {count}")
return all_jobs
def delete_job(self, job):
"""
Delete a `Job` using its job.id
Args:
job (KubeflowJob): A `Job` object to delete
Returns:
True if the `Job` was deleted succesfully
"""
self.kfp_jobs.delete_job(id=job.id)
return True
def create_job(self,
name: str,
pipeline,
experiment,
description=None,
enabled=True,
max_concurrency=1,
cron=None):
"""
Create a new Kubeflow Pipelines Job
Args:
name (str): The name of the `Job`
pipeline (Pipeline): The `Pipeline` object to execute when the `Job` is called
experiment (Experiment): The `Experiment` object to create the `Job` in.
description (str): A description of what the `Job` is all about
enabled (bool): Should be `Job` be enabled?
max_concurrency (int): How many concurrent executions of the `Job` are allowed?
cron (str): The CRON expression to use to execute the job periodicalls
Returns:
The Kubeflow API response object.
"""
key = kfp_server_api.models.ApiResourceKey(
id=experiment.id,
type=kfp_server_api.models.ApiResourceType.EXPERIMENT)
reference = kfp_server_api.models.ApiResourceReference(
key, kfp_server_api.models.ApiRelationship.OWNER)
spec = kfp_server_api.models.ApiPipelineSpec(pipeline_id=pipeline.id)
trigger = None
if cron is not None:
cron_schedule = kfp_server_api.models.api_cron_schedule.ApiCronSchedule(
cron=cron)
trigger = kfp_server_api.models.api_trigger.ApiTrigger(
cron_schedule=cron_schedule)
run_body = kfp_server_api.models.ApiJob(
name=name,
description=description,
pipeline_spec=spec,
resource_references=[reference],
enabled=True,
trigger=trigger,
max_concurrency=str(max_concurrency),
)
response = self.kfp_jobs.create_job(body=run_body)
return response
def list_runs(self, experiment_name):
"""
List the `Runs` in the specified Exper`iment
Args:
experiment_name (str): The name of the `Experiment`
Returns:
A list of all the `Runs` in the current `Experiment`
"""
experiment = self.get_experiment(experiment_name=experiment_name)
all_runs = list()
next_page_token = ""
while next_page_token is not None:
response = self.kfp_client.list_runs(page_size=100,
page_token=next_page_token)
if response.runs is None:
break
all_runs.extend(response.runs)
next_page_token = response.next_page_token
run_count = len(all_runs)
# print(f"list_runs: found {run_count}")
return all_runs
def list_pipelines(self):
"""
List the `Pipelines` in the current namespace
Returns:
A list of all the `Pipelines` in the current `Experiment`
"""
all_pipelines = list()
response = self.kfp_client.list_pipelines(page_size=100)
next_page_token = ""
while next_page_token is not None:
response = self.kfp_client.list_pipelines(
page_size=100, page_token=next_page_token)
if response.pipelines is None:
break
all_pipelines.extend(response.pipelines)
next_page_token = response.next_page_token
pipeline_count = len(all_pipelines)
# print(f"list_pipelines: found {pipeline_count}")
return all_pipelines
def find_experiment(self, id=None, name=None):
"""
Look up an `Experiment` by its name or id. Returns
None if the `Experiment` cannot be found. Both `id` and
`name` are optional, but atleast one must be provided.
Where both a provided, the function will return with the
first `Experiment` matching either id or name.
Args:
id (str): The `id` of the `Experiment` to find
name (string): The `name` of the `Experiment` to find
Returns:
A reference to the `Experiment` if found, and None if not.
"""
experiments = self.list_experiments()
if experiments is None:
return None
for e in experiments:
if e.name == name:
return e
if e.id == id:
return e
return None
def find_pipeline(self, name):
"""
Look up a `Pipeline` by its name (in the current namespace). Returns
None if the `Pipeline` cannot be found
Args:
name (str): The name of the `Pipeline` to find
Returns:
A reference to the `Pipeline` if found, and `None` if not.
"""
pipelines = self.list_pipelines()
if pipelines is None:
return None
for p in pipelines:
if p.name == name:
return p
return None
def delete_pipeline(self, pipeline):
"""
Delete the specified `Pipeline`
Args:
pipeline: The pipeline object to delete
Returns:
True if successfull
"""
# Go through all my pipelines to find the one to delete
self.kfp_pipelines.delete_pipeline(pipeline.id)
return True
def _connect_pipelines_api(self):
"""
Create a new PipelineServiceApi client
"""
api_client = kfp_server_api.api_client.ApiClient(self.config)
pipelines_api = kfp_server_api.api.pipeline_service_api.PipelineServiceApi(
api_client)
return pipelines_api
def _connect_runs_api(self):
"""
Create a new PipelineServiceApi client
"""
api_client = kfp_server_api.api_client.ApiClient(self.config)
runs_api = kfp_server_api.api.run_service_api.RunServiceApi(api_client)
return runs_api
def _connect_jobs_api(self):
"""
Create a new PipelineServiceApi client
"""
api_client = kfp_server_api.api_client.ApiClient(self.config)
runs_api = kfp_server_api.api.job_service_api.JobServiceApi(api_client)
return runs_api
