多用户隔离

import kfp

# the namespace in which you deployed Kubeflow Pipelines
kubeflow_namespace = "kubeflow"

# the namespace of your pipelines user (where the pipeline will be executed)
user_namespace = "jane-doe"

# the KF_PIPELINES_SA_TOKEN_PATH environment variable is used when no `path` is set
# the default KF_PIPELINES_SA_TOKEN_PATH is /var/run/secrets/kubeflow/pipelines/token
credentials = kfp.auth.ServiceAccountTokenVolumeCredentials(path=None)

# create a client
client = kfp.Client(host=f"http://ml-pipeline-ui.{kubeflow_namespace}", credentials=credentials)

# create an experiment
client.create_experiment(name="<YOUR_EXPERIMENT_ID>", namespace=user_namespace)
print(client.list_experiments(namespace=user_namespace))

# create a pipeline run
client.run_pipeline(
    experiment_id="<YOUR_EXPERIMENT_ID>",  # the experiment determines the namespace
    job_name="<YOUR_RUN_NAME>",
    pipeline_id="<YOUR_PIPELINE_ID>"  # the pipeline definition to run
)
print(client.list_runs(experiment_id="<YOUR_EXPERIMENT_ID>"))
print(client.list_runs(namespace=user_namespace))

import kfp
from kfp_server_api import *

# the namespace in which you deployed Kubeflow Pipelines
kubeflow_namespace = "kubeflow"

# the namespace of your pipelines user (where the pipeline will be executed)
user_namespace = "jane-doe"

# the KF_PIPELINES_SA_TOKEN_PATH environment variable is used when no `path` is set
# the default KF_PIPELINES_SA_TOKEN_PATH is /var/run/secrets/kubeflow/pipelines/token
credentials = kfp.auth.ServiceAccountTokenVolumeCredentials(path=None)

# create a client
client = kfp.Client(host=f"http://ml-pipeline-ui.{kubeflow_namespace}", credentials=credentials)

# create an experiment
experiment: ApiExperiment = client._experiment_api.create_experiment(
    body=ApiExperiment(
        name="<YOUR_EXPERIMENT_ID>",
        resource_references=[
            ApiResourceReference(
                key=ApiResourceKey(
                    id=user_namespace,
                    type=ApiResourceType.NAMESPACE,
                ),
                relationship=ApiRelationship.OWNER,
            )
        ],
    )
)
print("-------- BEGIN: EXPERIMENT --------")
print(experiment)
print("-------- END: EXPERIMENT ----------")

# get the experiment by name (only necessary if you comment out the `create_experiment()` call)
# experiment: ApiExperiment = client.get_experiment(
#     experiment_name="<YOUR_EXPERIMENT_ID>",
#     namespace=user_namespace
# )

# create a pipeline run
run: ApiRunDetail = client._run_api.create_run(
    body=ApiRun(
        name="<YOUR_RUN_NAME>",
        pipeline_spec=ApiPipelineSpec(
            # replace <YOUR_PIPELINE_ID> with the UID of a pipeline definition you have previously uploaded
            pipeline_id="<YOUR_PIPELINE_ID>",
        ),
        resource_references=[ApiResourceReference(
            key=ApiResourceKey(
                id=experiment.id,
                type=ApiResourceType.EXPERIMENT,
            ),
            relationship=ApiRelationship.OWNER,
        )
        ],
    )
)
print("-------- BEGIN: RUN --------")
print(run)
print("-------- END: RUN ----------")

# view the pipeline run
runs: ApiListRunsResponse = client._run_api.list_runs(
    resource_reference_key_type=ApiResourceType.EXPERIMENT,
    resource_reference_key_id=experiment.id,
)
print("-------- BEGIN: RUNS --------")
print(runs)
print("-------- END: RUNS ----------")