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Version: v1.0.22

Manage Experiments

This page explains how to create, view, update, and delete experiments; add comments; and manage experiment tags in H2O MLOps using the Python client. It also describes experiment properties and how to compute Kubernetes options.

To learn more about experiments, see Experiments.

Prerequisites​

Before you begin,

  1. Connect to H2O MLOps. For instructions, see Connect to H2O MLOps.
  2. Create a workspace. For steps, see Create a workspace.

Create an experiment​

Use the create() method to create a new experiment in a workspace:

experiment = workspace.experiments.create(
   data="/path/test.zip",
   name="my-experiment",
   description="Test experiment",
)
note

You can link or unlink an H2O Driverless AI (DAI) experiment, or an existing DAI or H2O MLOps experiment in storage, to a workspace.

Link an experiment by UID:

workspace.experiments.link(uid="your-experiment-uid")

Unlink an experiment:

workspace.experiments.unlink(uid="your-experiment-uid")

:::

View experiments​

Count experiments​

Get the total number of experiments in a workspace:

Input:

workspace.experiments.count()

Output:

1

List experiments​

List all experiments in a workspace:

Input:

experiments = workspace.experiments.list()
experiments

Output:

   | name          | uid                                  | tags
---+---------------+--------------------------------------+--------
 0 | my-experiment | d9a47c99-c66c-4ff9-b2b6-30faf5f413ef |
note

  • The output of list() method is displayed in a neatly formatted view. By default, only the first 50 rows are displayed to keep the output concise and manageable.

  • Calling len(experiments) returns the total number of rows it contains, not just the number currently displayed.

  • To customize the number of rows displayed, you can call the show() method with the n argument. This allows more rows to be shown when needed. For example:

    experiments.show(n=100)

    This will display up to 100 experiments.

  • The experiments can be iterated over, as it is designed to behave like an iterator.

Filter experiments​

Use the list() method with key-value arguments to filter the experiments.

Input:

workspace.experiments.list(name="my-experiment")

This returns a list of matching experiments as a table.

Output:

   | name                 | uid                                  | tags
---+----------------------+--------------------------------------+--------
 0 | my-experiment        | d9a47c99-c66c-4ff9-b2b6-30faf5f413ef |

Retrieve an experiment​

Retrieve a specific experiment by UID:

Input:

experiment = workspace.experiments.get(uid="d9a47c99-c66c-4ff9-b2b6-30faf5f413ef")
experiment
note

You can also retrieve a specific experiment from the list returned by list() using indexing.
For example, experiment = workspace.experiments.list(key=value)[index]. The key and value arguments are optional.

Output:

<class 'h2o_mlops._experiments.MLOpsExperiment(
   uid='d9a47c99-c66c-4ff9-b2b6-30faf5f413ef',
   name='my-experiment',
   description='Test experiment',
   creator_uid='4c4eb198-bcbc-4442-91f6-a27deb53e9c1',
   created_time=datetime.datetime(2025, 5, 22, 7, 2, 48, 185159, tzinfo=tzutc()),
   last_modified_time=datetime.datetime(2025, 5, 22, 7, 2, 48, 185159, tzinfo=tzutc()),
)'>

Experiment properties​

An experiment has the following main properties:

  • uid: The unique identifier of the experiment.
  • name: The name of the experiment.
  • description: A description of the experiment.
  • creator: The user who created the experiment.
  • created_time: The timestamp when the experiment was created.
  • last_modified_time: The timestamp of the last modification.
  • is_registered: If the experiment is registered or not.

Metadata​

Each experiment includes metadata you can retrieve using the following method:

Input:

experiment.metadata

Output:

   | key                 | value
---+---------------------+---------------------------------------------
 0 | h2o3/algo           | glm
 1 | h2o3/algo_full_name | Generalized Linear Modeling
 2 | h2o3/category       | Regression
 3 | h2o3/columns        | ['Origin', 'Dest', 'fDayofMonth', 'fYear...
 4 | h2o3/created_time   | 2020-08-24 07:21:50.137000+00:00
 5 | input_schema        | [{'name': 'Origin', 'type': 'STR'}, {'na...
 6 | model_type          | h2o3/mojo
 7 | output_schema       | [{'name': 'Distance', 'type': 'FLOAT64'}...
 8 | tool                | h2o3

To get a specific metadata entry by index:

Input:

experiment.metadata[3]

Output:

{'h2o3/columns': ['Origin',
 'Dest',
 'fDayofMonth',
 'fYear',
 'UniqueCarrier',
 'fDayOfWeek',
 'fMonth',
 'IsDepDelayed']}

Parameters​

To access parameters related to the dataset used in the experiment:

Input:

experiment.parameters["target_column"]

Output:

{'training_dataset_id': '',
'validation_dataset_id': '',
'test_dataset_id': '',
'target_column': 'Distance',
'weight_column': '',
'fold_column': ''}

In this example, the target column is Distance.

Statistics​

To access training statistics:

Input:

experiment.statistics

Output:

{'training_duration': None}

Input schema​

To view the schema of the input dataset:

Input:

experiment.input_schema

Output:

   | name          | type
---+---------------+--------
 0 | Origin        | STR
 1 | Dest          | STR
 2 | fDayofMonth   | STR
 3 | fYear         | STR
 4 | UniqueCarrier | STR
 5 | fDayOfWeek    | STR
 6 | fMonth        | STR
 7 | IsDepDelayed  | STR

Output schema​

To view the output schema of the experiment:

Input:

experiment.output_schema

Output:

   | name     | type
---+----------+---------
 0 | Distance | FLOAT64

Scoring runtimes​

You can list the available scoring runtimes that might be used when deploying the experiment:

Input:

scoring_runtimes = experiment.scoring_runtimes
scoring_runtimes

Output:

   | artifact_type   | runtime_uid                           | runtime_name
---+-----------------+---------------------------------------+----------------------------------------------
 0 | h2o3_mojo       | h2o3_mojo_runtime                     | H2O-3 MOJO Scorer
 1 | h2o3_mojo       | h2o3_mojo_runtime_shapley_transformed | H2O-3 MOJO Scorer (Shapley transformed only)

To view details of a specific scoring runtime:

Input:

scoring_runtimes[0]

Output:

<class 'h2o_mlops._runtimes.MLOpsScoringRuntime(
   runtime='h2o3_mojo_runtime',
   artifact_type='h2o3_mojo',
   artifact_processor='h2o3_mojo_extractor',
   model_type='h2o3_mojo',
)'>

Compute Kubernetes options​

To compute Kubernetes options for an experiment runtime:

Input:

experiment.compute_k8s_options(
   runtime_uid="h2o3_mojo_runtime", workers=1
)

Output:

KubernetesOptions(
   replicas=1,
   requests={'cpu': '500m', 'memory': '128Mi'},
   limits={},
   affinity=None,
   toleration=None
)

Update an experiment​

You can update only the name and description fields of an experiment.

Make sure to retrieve the experiment instance before updating it. See Retrieve an experiment.

Input:

experiment.update(name="new-experiment")
experiment

Output:

<class 'h2o_mlops._experiments.MLOpsExperiment(
   uid='d9a47c99-c66c-4ff9-b2b6-30faf5f413ef',
   name='new-experiment',
   description='Test experiment',
   creator_uid='4c4eb198-bcbc-4442-91f6-a27deb53e9c1',
   created_time=datetime.datetime(2025, 5, 22, 7, 2, 48, 185159, tzinfo=tzutc()),
   last_modified_time=datetime.datetime(2025, 5, 22, 7, 3, 26, 278369, tzinfo=tzutc()),
)'>

Add comments to an experiment​

You can add one or more comments to an experiment to share information with collaborators.

experiment.comments.add("Comment 01")
experiment.comments.add("Comment 02")

To list all comments:

Input:

experiment.comments.list()

Output:

   | created_time           | author_username    | message
---+------------------------+--------------------+------------
 0 | 2025-05-22 07:03:30 AM | user               | Comment 01
 1 | 2025-05-22 07:03:31 AM | user               | Comment 02

Manage experiment tags​

You can create tags and add them to experiments to group related experiments. For example, you can create a tag called Telco for telecommunication-related experiments and later retrieve them as a group.

Create a tag​

To create a new tag in a workspace:

tag1 = workspace.tags.create(label="tag1")

List tags​

To list all tags in a workspace:

Input:

workspace.tags.list()

Output:

   | label   | uid
---+---------+--------------------------------------
 0 | tag1    | 31fc1901-1134-4384-ac07-e0965f8e30c7

Get a specific tag​

To retrieve a tag by its label:

Input:

tag1 = workspace.tags.get(label="tag1")
tag1

Output:

<class 'h2o_mlops._projects.MLOpsProjectTag(
   uid='31fc1901-1134-4384-ac07-e0965f8e30c7',
   label='tag1',
   parent_workspace_uid='e37a6146-5248-4754-9d93-68a9798babb2',
   created_time=datetime.datetime(2025, 5, 22, 7, 3, 35, 596458, tzinfo=tzutc()),
)'>

Add tag​

To add an existing tag to an experiment:

experiment.tags.add(label="tag1")

To add a new tag to an experiment without creating it first:

experiment.tags.add(label="tag2")

To list and verify all tags in an experiment:

Input:

experiment.tags.list()

Output:

   | label   | uid
---+---------+--------------------------------------
 0 | tag1    | 31fc1901-1134-4384-ac07-e0965f8e30c7
 1 | tag2    | b541b0b1-6371-4106-8095-94c9c25f2f0a

Update a tag​

To update a tag’s label and view the updated list:

Input:

tag1.update(label="new-tag1")
experiment.tags.list() # or workspace.tags.list()

Output:

   | label    | uid
---+----------+--------------------------------------
 0 | tag2     | b541b0b1-6371-4106-8095-94c9c25f2f0a
 1 | new-tag1 | 31fc1901-1134-4384-ac07-e0965f8e30c7

Remove a tag​

To remove a tag from an experiment:

Input:

experiment.tags.remove(label="new-tag1")
experiment.tags.list()

Output:

   | label   | uid
---+---------+--------------------------------------
 0 | tag2    | b541b0b1-6371-4106-8095-94c9c25f2f0a

This removes the tag only from the experiment, not from the workspace.

To view all tags in a workspace after performing the above removal, use the following method:

Input:

workspace.tags.list()

This displays the list of tags currently available in the workspace.

Output:

   | label    | uid
---+----------+--------------------------------------
 0 | tag2     | b541b0b1-6371-4106-8095-94c9c25f2f0a
 1 | new-tag1 | 31fc1901-1134-4384-ac07-e0965f8e30c7

Delete a tag​

To delete a tag from a workspace:

Input:

tag1.delete()
workspace.tags.list()

Output:

   | label   | uid
---+---------+--------------------------------------
 0 | tag2    | b541b0b1-6371-4106-8095-94c9c25f2f0a
note

You cannot delete a tag from a workspace if it has already been added to an experiment.

Delete and restore experiments​

This section describes how to delete and restore experiments.

Delete using an experiment instance​

If you already have a reference to the experiment object, use the delete() method:

Input:

experiment.delete()
workspace.experiments.list()

Output:

   | name   | uid   | tags
---+--------+-------+--------

Restore using an experiment instance​

If you already have a reference to the experiment object, use the restore() method:

Input:

experiment.restore()
workspace.experiments.list()

Output:

   | name                   | uid                                  | tags
---+------------------------+--------------------------------------+--------
 0 | new-experiment         | d9a47c99-c66c-4ff9-b2b6-30faf5f413ef | tag2

Delete using experiment UIDs​

You can also delete multiple experiments at once by specifying their UIDs:

Input:

workspace.experiments.delete(uids=["d9a47c99-c66c-4ff9-b2b6-30faf5f413ef"])
note

You can also pass a list of MLOpsExperiment instances or a _utils.Table containing experiments. Example: workspace.experiments.delete(experiments=[experiment])

Output:

   | experiment_uid                       | is_deleted   | message   | workspace_uid
---+--------------------------------------+--------------+-----------+--------------------------------------
 0 | d9a47c99-c66c-4ff9-b2b6-30faf5f413ef | True         |           | e37a6146-5248-4754-9d93-68a9798babb2

Restore using experiment UIDs​

You can also restore multiple experiments at once by specifying their UIDs:

Input:

workspace.experiments.restore(uids=["d9a47c99-c66c-4ff9-b2b6-30faf5f413ef"])
note

You can also pass a list of MLOpsExperiment instances or a _utils.Table containing experiments. Example: workspace.experiments.restore(experiments=[experiment])

Output:

   | experiment_uid                       | is_restored   | message   | workspace_uid
---+--------------------------------------+---------------+-----------+--------------------------------------
 0 | d9a47c99-c66c-4ff9-b2b6-30faf5f413ef | True          |           | e37a6146-5248-4754-9d93-68a9798babb2
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