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drift_detector_steps: str
Amount of steps, during which an autoencoder will be trained.
drift_detector_batch_size: str
Batch size, used for training an autoencoder.
model_name: str
Name of the classifier, which will be used for deployment.
model_drift_detector_name: str
Name of the autoencoder, which will be used for deployment.
acceptable_accuracy: str
Accuracy level indicating the final acceptable performance of the model
in the evaluation step, which will let let model to be either deployed
to production or cause workflow execution to fail.
"""
# Configure all steps to have ConfigMap and use aws secret
dsl.get_pipeline_conf().add_op_transformer(apply_config_map_and_aws_secret)
download = dsl.ContainerOp(
name="download",
image=f"{registry}/mnist-pipeline-download:{tag}",
file_outputs={
"output_data_path": "/output_data_path",
"logs_path": "/logs_path",
},
arguments=["--output-data-path", f"s3://{bucket}/data"],
)
train_drift_detector = dsl.ContainerOp(
name="train_drift_detector",
image=f"{registry}/mnist-pipeline-train-drift-detector:{tag}",
file_outputs={
"logs_path": "/logs_path",
tag: str = "latest",
namespace: str = "kubeflow",
bucket: str = "mybucket"
):
# configures artifact location
pipeline_artifact_location = dsl.ArtifactLocation.s3(
bucket=bucket,
endpoint="minio-service.%s:9000" % namespace, # parameterize minio-service endpoint
insecure=True,
access_key_secret=V1SecretKeySelector(name="mlpipeline-minio-artifact", key="accesskey"),
secret_key_secret={"name": "mlpipeline-minio-artifact", "key": "secretkey"}, # accepts dict also
)
# set pipeline level artifact location
dsl.get_pipeline_conf().set_artifact_location(pipeline_artifact_location)
# artifacts in this op are stored to endpoint `minio-service.:9000`
op = dsl.ContainerOp(name="foo", image="busybox:%s" % tag)
def custom_artifact_location(
tag: str, namespace: str = "kubeflow", bucket: str = "mybucket"
):
# configures artifact location
pipeline_artifact_location = dsl.ArtifactLocation.s3(
bucket=bucket,
endpoint="minio-service.%s:9000" % namespace, # parameterize minio-service endpoint
insecure=True,
access_key_secret=V1SecretKeySelector(name="minio", key="accesskey"),
secret_key_secret={"name": "minio", "key": "secretkey"}, # accepts dict also
)
# set pipeline level artifact location
dsl.get_pipeline_conf().set_artifact_location(pipeline_artifact_location)
# artifacts in this op are stored to endpoint `minio-service.:9000`
op = dsl.ContainerOp(name="foo", image="busybox:%s" % tag)
def transform_pipeline():
op1 = print_op('hey, what are you up to?')
op2 = print_op('train my model.')
dsl.get_pipeline_conf().add_op_transformer(add_annotation)
drift_detector_steps: str
Amount of steps, during which an autoencoder will be trained.
drift_detector_batch_size: str
Batch size, used for training an autoencoder.
model_name: str
Name of the classifier, which will be used for deployment.
model_drift_detector_name: str
Name of the autoencoder, which will be used for deployment.
acceptable_accuracy: str
Accuracy level indicating the final acceptable performance of the model
in the evaluation step, which will let let model to be either deployed
to production or cause workflow execution to fail.
"""
# Configure all steps to have ConfigMap and use aws secret
dsl.get_pipeline_conf().add_op_transformer(apply_config_map_and_aws_secret)
subsample = dsl.ContainerOp(
name="subsample",
image=f"{registry}/mnist-pipeline-subsample:{tag}",
file_outputs={
"output_data_path": "/output_data_path",
"logs_path": "/logs_path",
},
arguments=[
"--output-data-path", "s3://workshop-hydrosphere/mnist/data",
"--application-name", target_application_name,
"--limit", sample_limit,
"--train-part", train_part,
"--validation-part", validation_part
],
)
tag: str = '1.31.0',
namespace: str = "kubeflow",
bucket: str = "mlpipeline"
):
# configures artifact location
pipeline_artifact_location = dsl.ArtifactLocation.s3(
bucket=bucket,
endpoint="minio-service.%s:9000" % namespace, # parameterize minio-service endpoint
insecure=True,
access_key_secret=V1SecretKeySelector(name=secret_name, key="accesskey"),
secret_key_secret={"name": secret_name, "key": "secretkey"}, # accepts dict also
)
# set pipeline level artifact location
dsl.get_pipeline_conf().set_artifact_location(pipeline_artifact_location)
# artifacts in this op are stored to endpoint `minio-service.:9000`
op = dsl.ContainerOp(name="foo", image="busybox:%s" % tag,
command=['sh', '-c', 'echo hello > /tmp/output.txt'],
file_outputs={'output': '/tmp/output.txt'})