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Version: v0.68.0

Monitoring options

This page describes how to configure monitoring options for new and existing deployments.

Connect to H2O MLOps​

import h2o_mlops
import time


mlops = h2o_mlops.Client()

New deployments​

Set up the prerequisites for creating a deployment.​

project = mlops.projects.create(name="demo")
experiment = project.experiments.create(
    data="/Users/jgranados/models/mojo.zip",
    name="experiment-from-client"
)
model = project.models.create(name="model-from-client")
model.register(experiment=experiment)
environment = project.environments.list(name="DEV")[0]
scoring_runtime = mlops.runtimes.scoring.list(
    artifact_type="dai_mojo_pipeline", 
    uid="dai_mojo_runtime",
)[0]

Configure the options you want for monitoring​

  • When enable is False, H2O MLOps does not store any information about scoring requests and the other options have no effect.
  • When enable is True, H2O MLOps will always capture operational data such as request count and scoring latency.
  • When enable and save_scoring_inputs are True, the data sent to the model is stored and used for further evaluation such as detecting data drift.
monitoring_options = h2o_mlops.options.MonitoringOptions(
    enable=True,
    save_scoring_inputs=True
)

Pass the monitoring options to the deployment creation method​

deployment = environment.deployments.create_single(
    name = "deployment-from-client",
    model = model,
    scoring_runtime = scoring_runtime,
    monitoring_options = monitoring_options,
    security_options=h2o_mlops.options.SecurityOptions(disabled_security=True),   
)

while not deployment.is_healthy():
    deployment.raise_for_failure()
    time.sleep(5)
    
deployment.status()

Output:

'HEALTHY'

View the current configured options for the deployment​

print(deployment.monitoring_options)

Output:

enable: True
save_scoring_inputs: True

Existing deployments​

If you already have a deployment, you can use the update_monitoring_options method to change it. Note that the deployment will report itself as UNHEALTHY until the updated options have taken effect.

deployment.update_monitoring_options(enable=False)

while not deployment.is_healthy():
    deployment.raise_for_failure()
    time.sleep(5)
    
deployment.status()

Output:

'HEALTHY'

Confirm that the current configured options for the deployment have been updated​

print(deployment.monitoring_options)

Output:

enable: False
save_scoring_inputs: True
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