Manage deployments
This page describes how to create, view, update, and delete deployments, as well as how to view logs and configure endpoints using the H2O MLOps Python client.
To learn more about deployments, see Understand deployments in MLOps.
Prerequisites
Before you begin,
- Import the necessary Python packages. For instructions, see Step 1: Import the required packages.
- Connect to H2O MLOps. For instructions, see Connect to H2O MLOps.
- Create a project. For instructions, see Create a project.
- Create one or two experiments. For instructions, see Create an experiment.
- Create models and register the experiments with them. For instructions, see Register an experiment with a model.
Create a deployment
Create a deployment within the project using the create() method by specifying the deployment name, composition options, and security options.
deployment = project.deployments.create(
name="my-deployment",
composition_options=options.CompositionOptions(
model=model,
scoring_runtime=mlops.runtimes.scoring.get(
artifact_type="h2o3_mojo",
runtime_uid="h2o3_mojo_runtime",
),
),
security_options=options.SecurityOptions(
security_type=types.SecurityType.DISABLED,
),
)
The create() method supports the following optional arguments:
mode:types.DeploymentModeType = types.DeploymentModeType.SINGLE_MODEL– The type of deployment.
Available values:SINGLE_MODELAB_TESTCHAMPION_CHALLENGER
description:Optional[str] = None– The deployment description.kubernetes_options:Optional[options.KubernetesOptions] = None– Customize Kubernetes deployment settings.vpa_options:Optional[List[options.VPAOptions]] = None– Configure Vertical Pod Autoscaler (VPA) settings.pdb_options:Optional[options.PDBOptions] = None– Control pod availability during voluntary disruptions.environment_variables:Optional[Dict[str, str]] = None– Specify environment variables to add to the scoring runtime.cors_origins:Optional[List[str]] = None– Define allowed CORS origins.monitoring_options:Optional[options.MonitoringOptions] = None– Configure monitoring settings for the deployment.
For more details on these configuration options, see Configure deployments.
The deployment might take a few seconds. Use the following command to wait until the deployment is healthy and ready to use:
deployment.wait_for_healthy()
To retry a failed deployment, use the following command. It checks the current deployment status and, if it's in a failed state, attempts to redeploy it:
deployment.redeploy_if_failed()
View deployments
List deployments
List all deployments in a project:
Input:
project.deployments.list()
Output:
| name | mode | uid
---+---------------+--------------+--------------------------------------
0 | my-deployment | Single Model | 48ece40f-8608-473a-92a6-388e164e995
List deployment statuses
Use the deployments.statuses() method to view the statuses of all deployments.
Input:
project.deployments.statuses()
This returns a list of deployments and their statuses in a table.
Output:
| uid | state
---+--------------------------------------+---------
0 | 48ece40f-8608-473a-92a6-388e164e9952 | HEALTHY
Possible status values:
PREPARING: Preparing the deployment for launch (for example, building assets).LAUNCHING: Deployment is launching to an environment (for example, waiting for the environment to start).FAILED: Deployment failed during preparation or launch.HEALTHY: Deployment is alive and healthy.UNHEALTHY: Health issues detected in the launched deployment.TERMINATING: Deployment is terminating (that is, environment resources are being brought down).PENDING: Deployment created and awaiting processing.
Filter deployments
Use the deployments.list() method with key-value arguments to filter deployments.
Input:
project.deployments.list(name="my-deployment")
This returns a list of matching deployments as a table.
Output:
| name | mode | uid
---+---------------+--------------+--------------------------------------
0 | my-deployment | Single Model | 48ece40f-8608-473a-92a6-388e164e9952
Retrieve a deployment
Retrieve a deployment by UID:
Input:
deployment = project.deployments.get(uid="48ece40f-8608-473a-92a6-388e164e9952")
deployment
You can also retrieve a deployment from the list returned by deployments.list() using indexing.
For example, deployment = project.deployments.list(key=value)[index]. The key and value arguments are optional.
Output:
<class 'h2o_mlops._deployments.MLOpsScoringDeployment(
uid='48ece40f-8608-473a-92a6-388e164e9952',
name='my-deployment',
description='',
mode=<DeploymentModeType.SINGLE_MODEL: 'Single Model'>,
state='HEALTHY',
owner_username='user@test.com',
created_time=datetime.datetime(2025, 6, 27, 5, 4, 32, 14134, tzinfo=tzutc()),
last_modified_time=datetime.datetime(2025, 6, 27, 5, 4, 32, 14134, tzinfo=tzutc())
)'>
Deployment properties
A deployment has the following main properties:
uid: The unique identifier for the deployment.name: The name of the deployment.description: A description of the deployment.mode: The deployment mode (for example,SINGLE_MODELorAB_TEST).state: The current status of the deployment.owner: The deployment owner.created_time: The timestamp when the deployment was created.last_modified_time: The timestamp of the last update.revision_uid: The revision ID of the deployment.is_healthy: A boolean indicating whether the deployment is healthy.composition_options: Define how models are composed for deployment.security_options: Security configuration options.kubernetes_options: Kubernetes deployment configuration.vpa_options: Vertical Pod Autoscaler (VPA) settings.pdb_options: Pod disruption budget settings.environment_variables: Environment variables to add to the scoring runtime.cors_origins: A list of allowed CORS origins.monitoring_options: Monitoring configuration.experiments: The associated experiment(s) for the deployment.
View deployment logs
Call the logs() method to access the logs for a deployment:
logs = deployment.logs()
This returns a dictionary of log entries from different deployment pods.
To see which pods' log entries were retrieved, use the keys() method:
Input:
keys = list(logs.keys())
keys
Output:
['primary.pr-48ece40f-8608-473a-92a6-388e164e9952-769f88644b-7qzcs.artifact-fetcher',
'primary.pr-48ece40f-8608-473a-92a6-388e164e9952-769f88644b-7qzcs.artifact-processor',
'primary.pr-48ece40f-8608-473a-92a6-388e164e9952-769f88644b-7qzcs.events',
'primary.pr-48ece40f-8608-473a-92a6-388e164e9952-769f88644b-7qzcs.proxy',
'primary.pr-48ece40f-8608-473a-92a6-388e164e9952-769f88644b-7qzcs.runtime']
As you already know the available dictionary keys, you can access the log entries for a specific pod.
For example, to view the full log for the runtime pod:
Input:
logs["primary.pr-48ece40f-8608-473a-92a6-388e164e9952-769f88644b-7qzcs.runtime"]
Output:
['2025-06-27 05:04:36.423321198 +0000 UTC: Picked up _JAVA_OPTIONS: -Dmojo.path=/data/model',
'2025-06-27 05:04:36.63684369 +0000 UTC: Standard Commons Logging discovery in action with spring-jcl: please remove commons-logging.jar from classpath in order to avoid potential conflicts',
'2025-06-27 05:04:37.092592485 +0000 UTC: ',
'2025-06-27 05:04:37.092627005 +0000 UTC: . ____ _ __ _ _',
"2025-06-27 05:04:37.092634066 +0000 UTC: /\\\\ / ___'_ __ _ _(_)_ __ __ _ \\ \\ \\ \\",
"2025-06-27 05:04:37.092639646 +0000 UTC: ( ( )\\___ | '_ | '_| | '_ \\/ _` | \\ \\ \\ \\",
'2025-06-27 05:04:37.092645106 +0000 UTC: \\\\/ ___)| |_)| | | | | || (_| | ) ) ) )',
"2025-06-27 05:04:37.092650616 +0000 UTC: ' |____| .__|_| |_|_| |_\\__, | / / / /",
'2025-06-27 05:04:37.092656126 +0000 UTC: =========|_|==============|___/=/_/_/_/',
'2025-06-27 05:04:37.092661606 +0000 UTC: ',
'2025-06-27 05:04:37.094651073 +0000 UTC: :: Spring Boot :: (v3.3.12)',
'2025-06-27 05:04:37.094674983 +0000 UTC: ',
'2025-06-27 05:04:37.143507059 +0000 UTC: 2025-06-27T05:04:37.142Z INFO 1 --- [ main] a.h.m.d.local.rest.ScorerApplication : Starting ScorerApplication v0.0.0.dev0+main.3c6156731c9d1c63d27498637cd3b4fea8bfd29e using Java 21.0.7 with PID 1 (/app/BOOT-INF/classes started by ? in /app)',
'2025-06-27 05:04:37.144078602 +0000 UTC: 2025-06-27T05:04:37.143Z INFO 1 --- [ main] a.h.m.d.local.rest.ScorerApplication : No active profile set, falling back to 1 default profile: "default"',
'2025-06-27 05:04:37.974359799 +0000 UTC: 2025-06-27T05:04:37.974Z INFO 1 --- [ main] o.s.b.w.embedded.tomcat.TomcatWebServer : Tomcat initialized with port 8080 (http)',
'2025-06-27 05:04:37.984421453 +0000 UTC: 2025-06-27T05:04:37.984Z INFO 1 --- [ main] o.apache.catalina.core.StandardService : Starting service [Tomcat]',
'2025-06-27 05:04:37.984547596 +0000 UTC: 2025-06-27T05:04:37.984Z INFO 1 --- [ main] o.apache.catalina.core.StandardEngine : Starting Servlet engine: [Apache Tomcat/10.1.42]',
'2025-06-27 05:04:38.01826987 +0000 UTC: 2025-06-27T05:04:38.017Z INFO 1 --- [ main] o.a.c.c.C.[Tomcat].[localhost].[/] : Initializing Spring embedded WebApplicationContext',
'2025-06-27 05:04:38.01910209 +0000 UTC: 2025-06-27T05:04:38.018Z INFO 1 --- [ main] w.s.c.ServletWebServerApplicationContext : Root WebApplicationContext: initialization completed in 742 ms',
'2025-06-27 05:04:38.021103686 +0000 UTC: Standard Commons Logging discovery in action with spring-jcl: please remove commons-logging.jar from classpath in order to avoid potential conflicts',
'2025-06-27 05:04:38.071485518 +0000 UTC: 2025-06-27T05:04:38.070Z INFO 1 --- [ main] a.h.m.d.common.transform.MojoScorer : Loading Mojo pipeline from path /data/model',
'2025-06-27 05:04:38.075504291 +0000 UTC: 2025-06-27T05:04:38.075Z INFO 1 --- [ main] a.h.m.r.a.backend.ZipFileReaderBackend : Opening mojo file: /data/model',
'2025-06-27 05:04:38.165548915 +0000 UTC: 2025-06-27T05:04:38.165Z INFO 1 --- [ main] a.h.m.d.common.transform.MojoScorer : Loading Mojo pipeline from path /data/model',
'2025-06-27 05:04:38.165589516 +0000 UTC: 2025-06-27T05:04:38.165Z INFO 1 --- [ main] a.h.m.r.a.backend.ZipFileReaderBackend : Opening mojo file: /data/model',
'2025-06-27 05:04:38.18428143 +0000 UTC: 2025-06-27T05:04:38.183Z INFO 1 --- [ main] a.h.m.d.common.transform.MojoScorer : Mojo pipeline successfully loaded (4295412422633975888).',
"2025-06-27 05:04:38.486280193 +0000 UTC: 2025-06-27T05:04:38.485Z INFO 1 --- [ main] o.s.b.w.embedded.tomcat.TomcatWebServer : Tomcat started on port 8080 (http) with context path '/'",
'2025-06-27 05:04:38.50331213 +0000 UTC: 2025-06-27T05:04:38.502Z INFO 1 --- [ main] a.h.m.d.local.rest.ScorerApplication : Started ScorerApplication in 1.708 seconds (process running for 2.079)',
"2025-06-27 05:04:39.403663076 +0000 UTC: 2025-06-27T05:04:39.403Z INFO 1 --- [nio-8080-exec-1] o.a.c.c.C.[Tomcat].[localhost].[/] : Initializing Spring DispatcherServlet 'dispatcherServlet'",
"2025-06-27 05:04:39.4038616 +0000 UTC: 2025-06-27T05:04:39.403Z INFO 1 --- [nio-8080-exec-1] o.s.web.servlet.DispatcherServlet : Initializing Servlet 'dispatcherServlet'",
'2025-06-27 05:04:39.40514298 +0000 UTC: 2025-06-27T05:04:39.404Z INFO 1 --- [nio-8080-exec-1] o.s.web.servlet.DispatcherServlet : Completed initialization in 1 ms']
View deployment logs from a specific time
To get logs starting from a specific date and time:
from datetime import datetime, timedelta
from zoneinfo import ZoneInfo
since_time = datetime(2025, 6, 25, 9, 0, 0, tzinfo=ZoneInfo("Asia/Colombo"))
logs = deployment.logs(since_time=since_time)
You can retrieve logs relative to the deployment’s creation time by using timedelta.
For example, to get logs starting 5 minutes after the deployment was created:
since_time = deployment.created_time + timedelta(minutes=5)
logs = deployment.logs(since_time=since_time)
Alternatively, you can get recent logs by subtracting a time duration from the current time.
Make sure to use timezone-aware datetime values.
For example, to get logs from the past 5 minutes using the Asia/Colombo timezone:
since_time = datetime.now(ZoneInfo("Asia/Colombo")) - timedelta(minutes=5)
logs = deployment.logs(since_time=since_time)
Manage endpoint
Configure endpoint
Configure a static path for the MLOps deployment REST endpoint:
endpoint = deployment.configure_endpoint(
path="static-path",
)
The configure_endpoint() method supports the following arguments:
path:str- Path to use for the target deployment URLs.name:Optional[str] = None- Display name for the MLOps endpoint. (Used only if a new endpoint is created.)description:Optional[str] = None- Description for the MLOps endpoint. (Used only if a new endpoint is created.)force:bool = False- Whether to attempt to reassign the path if it’s already in use by another deployment.
List deployment endpoints
To list all the endpoints attached to the deployment:
Input:
endpoints = deployment.endpoints
endpoints
Output:
| name | path | uid | target_deployment_uid
---+-------------+-------------+--------------------------------------+--------------------------------------
0 | static-path | static-path | 6ab2b92e-98c0-4c38-b4ef-4f6d9ba66e6f | 48ece40f-8608-473a-92a6-388e164e9952
To list all the endpoints available within the project:
project.endpoints.list()
Retrieve a deployment endpoint
To retrieve a deployment endpoint:
Input:
endpoint = endpoints[0]
endpoint
Output:
<class 'h2o_mlops._endpoints.MLOpsEndpoint(
uid='6ab2b92e-98c0-4c38-b4ef-4f6d9ba66e6f',
name='static-path',
description='',
path='static-path',
target_deployment_uid='48ece40f-8608-473a-92a6-388e164e9952',
created_time=datetime.datetime(2025, 6, 27, 5, 5, 40, 146102, tzinfo=tzutc()),
last_modified_time=datetime.datetime(2025, 6, 27, 5, 5, 40, 146481, tzinfo=tzutc())
)'>
To retrieve an endpoint available within the project:
project.endpoints.get(uid=...)
Delete an endpoint
Deleting an endpoint also detaches the deployment associated with it.
To delete the endpoint:
endpoint.delete()
To verify deletion:
Input:
deployment.endpoints
Output:
| name | path | uid | target_deployment_uid
---+--------+--------+-------+-------------------------
Update a deployment
You can update a deployment's properties, including name, description, security_options, kubernetes_options, vpa_options, pdb_options, environment_variables, cors_origins, and monitoring_options.
For details on how to configure these properties, see Configure deployments.
Make sure to retrieve the deployment before updating it. See Retrieve a deployment.
Input:
deployment.update(name="my-new-deployment")
deployment
Output:
<class 'h2o_mlops._deployments.MLOpsScoringDeployment(
uid='48ece40f-8608-473a-92a6-388e164e9952',
name='my-new-deployment',
description='',
mode=<DeploymentModeType.SINGLE_MODEL: 'Single Model'>,
state='HEALTHY',
owner_username='user@test.com',
created_time=datetime.datetime(2025, 6, 27, 5, 4, 32, 14134, tzinfo=tzutc()),
last_modified_time=datetime.datetime(2025, 6, 27, 5, 6, 7, 730844, tzinfo=tzutc())
)'>
Delete deployments
Deleting a deployment doesn’t delete the endpoint attached to it. Instead, the endpoint becomes detached and enters a dangling state. You can reuse the same endpoint in a future deployment.
To delete a deployment:
deployment.delete()
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