Analyze Vertex ML Metadata

You can use Vertex ML Metadata to track and analyze the metadata produced by your machine learning (ML) systems. By tracking this metadata, it becomes easier to analyze the behavior of your ML system. This can help you understand changes in your system's performance, or it can help you compare the artifacts that your ML system produced.

If you are new to Vertex ML Metadata, read the introduction to Vertex ML Metadata to learn more about tracking and analyzing your ML workflow's metadata.

Learn how to query for the Vertex ML Metadata that you want to analyze in the following ways:

  • Query for all artifacts, executions, or context that match your filtering criteria.
  • Query for an execution's input and output artifacts along with the events, used to connect the artifacts to the execution.
  • Query for a context's lineage subgraph. This query returns a context's artifacts and executions, along with the events that connect artifacts to executions.

Query for artifacts, executions, and contexts

You can use the Vertex AI SDK for Python or the REST API to query for artifacts, executions, and contexts records using filters to create queries like the following:

  • Which versions of a trained model achieved a certain quality threshold?
  • Which dataset is used in a given pipeline?

The following sections demonstrate how to create filters and how to query for artifacts, executions, and contexts.

Overview of filter syntax

The following sections describe how to use filters to query for artifacts, executions, and contexts.

Fields

The following fields are supported when filtering artifacts, executions, and contexts.

Artifact Execution Context
name
display_name
schema_title
create_time
update_time
metadata
state
uri

Your filter must be wrapped in quotation marks. Any quotes that are a part of your filter must be escaped with a backslash.

Comparison operators

You can use the following comparison operators in your filters: =, !=, <, >, >=, <=.

For example, the following filters to find all artifacts where the display name is my_artifact.

REST 

display_name=\"my_artifact\"

Python 

"display_name=\"my_artifact\""

For string fields, you can use wildcard filtering with the * character.

For timestamp fields such as create_time and update_time, you must format the date using RFC 3339 format — for example:

REST 

create_time=\"2021-05-11T12:30:00-08:00\"

Python 

"create_time=\"2021-05-11T12:30:00-08:00\""

Logical Operators

You can use AND and OR logical operators to combine filters to create a complex query.

The following example demonstrates how to query for artifacts of type ai_platform.model and a metadata field precision with a numeric value greater than 0.9.

REST 

schema_title=\"ai_platform.Model\"+AND+metadata.precision.number_value>0.9

Python 

"create_time=\"schema_title=\"ai_platform.Model\" AND metadata.precision.number_value>0.9"

Filter on metadata using the traversal operator

The metadata field is an instance of google.protobuf.Struct whose format is defined in the schema specified in the schema_title field. google.protobuf.Struct is a data structure that maps keys to google.protobuf.Value instances. The google.protobuf.Value data structure stores values in different fields depending on their data type. For example:

  • strings are stored as metadata.FIELD_NAME.string_value,
  • numbers are stored as metadata.FIELD_NAME.number_value,
  • booleans are stored as metadata.FIELD_NAME.bool_value.

To filter on metadata, you must use the traversal operator to traverse to the field that you want to filter on. The traversal operator uses the following format.

REST 

metadata.FIELD_NAME.TYPE_NAME=\"FILTER_VALUE\"

Python 

"metadata.FIELD_NAME.TYPE_NAME=\"FILTER_VALUE\""

For example, consider a metadata structure like the following:

{
   "field_1": 5,
   "field_2": "example",
   "field_3": {
     ...
   },
   "field_4": [],
   "field_5": true,
}

The following queries illustrate how to use the traversal operator to filter on this example metadata.

  • Filter for records that have metadata.field_1 with a value less than 5.

REST 

metadata.field_1.number_value<5

Python 

"metadata.field_1.number_value<5"

  • Filter for records that have metadata.field_2 with a value equal to example.

REST 

metadata.field_2.string_value=\"example\"

Python 

"metadata.field_2.string_value=\"example\""

  • Filter for records that have metadata.field_5 with a value equal to true.

REST 

metadata.field_5.bool_value=true

Python 

"metadata.field_5.bool_value=true"

Filter contexts by their parent and child relationships

You can use the has operator to find contexts that are the parent or child of a specified context.

The has operator uses the following format:

  • "parent_contexts:\"CONTEXT_RESOURCE_NAME\""
  • "child_contexts:\"CONTEXT_RESOURCE_NAME\""

The context name must be the context's full resource name, like the following: project/PROJECT/locations/LOCATION/metadataStores/METADATA-STORE/contexts/CONTEXT.

The following filters demonstrate how to use the has operator:

  • Filter for all contexts that are children of the specified pipeline.

REST 

parent_contexts:\"project/12345/locations/us-central1/metadataStores/default/contexts/pipeline_1\"

Python 

"parent_contexts:\"project/12345/locations/us-central1/metadataStores/default/contexts/pipeline_1\""

  • Filter for all contexts that are a parent of the specified pipeline.

REST 

child_contexts:\"project/12345/locations/us-central1/metadataStores/default/contexts/pipeline_1\"

Python 

"child_contexts:\"project/12345/locations/us-central1/metadataStores/default/contexts/pipeline_1\""

Filter contexts, executions, and artifacts by association and attribution

You can use the in_context() function to filter for artifacts or executions that are associated with a context. You can use the with_execution() function to filter for specific artifacts or contexts that are associated with an execution. Similarly you can use the with_artifact() function to filter for specific executions or contexts that are associated with an artifact.

The filter functions are used in the following format.

  • "in_context(\"CONTEXT_RESOURCE_NAME\")"
  • "with_execution(\"EXECUTION_RESOURCE_NAME\")"
  • "with_artifact(\"ARTIFACT_RESOURCE_NAME\")"

The context, the execution, and the artifact names must be the full resource name, like the following.

  • project/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA-STORE/contexts/CONTEXT
  • project/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA-STORE/executions/EXECUTION
  • project/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA-STORE/artifacts/ARTIFACT

The following example demonstrates how to filter for objects that are in the specified pipeline.

REST 

in_context(\"project/12345/locations/us-central1/metadataStores/default/contexts/pipeline_1\")

Python 

"in_context(\"project/12345/locations/us-central1/metadataStores/default/contexts/pipeline_1\")"

You can use a wildcard * in the filter functions to filter on parameters of each resource. For example you can use the following to filter all executions that act on a system.model artifact type.

REST 

with_artifact(\"*\",\"schema_title='name.model'\")

Python 

"with_artifact(\"*\",\"schema_title='name.model'\")"

Other supported parameters that you can filter on are as follows

  • input=true/false: Filter input or output artifact types.
  • event_time: Filter executions or artifacts event times.
  • All other supported filter fields

You can combine the fields with logical operands to construct complex filter queries. Note that the maximum supported nested function depth is 5.

Query for artifacts

Artifacts, such as datasets and models, represent data used or produced by your ML workflow. Use the following instructions to query for artifacts.

REST

Before using any of the request data, make the following replacements:

  • LOCATION_ID: Your region.
  • PROJECT_ID: Your project ID.
  • METADATA_STORE: The metadata store ID where the artifact is created. The default metadata store is named default.
  • PAGE_SIZE: (Optional) The maximum number of artifacts to return. If this value is not specified, the service returns a maximum of 100 records.
  • PAGE_TOKEN: (Optional) A page token from a previous MetadataService.ListArtifacts call. Specify this token to get the next page of results.

  • FILTER: Specifies the conditions required to include an artifact in the result set.

HTTP method and URL: 

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/artifacts?pageSize=PAGE_SIZE&pageToken=PAGE_TOKEN&filter=FILTER

To send your request, expand one of these options:

You should see output similar to the following. ARTIFACT_ID is the ID of the artifact record.

{
  "artifacts": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/default/artifacts/ARTIFACT_ID",
      "displayName": "Example artifact",
      "uri": "gs://your_bucket_name/artifacts/dataset.csv",
      "etag": "67891011",
      "createTime": "2021-05-18T00:33:13.833Z",
      "updateTime": "2021-05-18T00:33:13.833Z",
      "state": "LIVE",
      "schemaTitle": "system.Dataset",
      "schemaVersion": "0.0.1",
      "metadata": {
        "payload_format": "CSV"
      },
      "description": "Description of the example artifact."
    },
    {
      "name": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "displayName": "Another example artifact",
      "uri": "gs://your_bucket_name/artifacts/dataset-2.csv",
      "etag": "67891012",
      "createTime": "2021-05-18T00:29:24.344Z",
      "updateTime": "2021-05-18T00:29:24.344Z",
      "state": "LIVE",
      "schemaTitle": "system.Dataset",
      "schemaVersion": "0.0.1",
      "metadata": {
        "payload_format": "CSV"
      },
      "description": "Description of the other example artifact."
    }
  ]
}

Python

Python

from typing import Optional

from google.cloud import aiplatform


def list_artifact_sample(
    project: str,
    location: str,
    display_name_filter: Optional[str] = "display_name=\"my_model_*\"",
    create_date_filter: Optional[str] = "create_time>\"2022-06-11\"",
    order_by: Optional[str] = None,
):
    aiplatform.init(project=project, location=location)

    combined_filters = f"{display_name_filter} AND {create_date_filter}"
    return aiplatform.Artifact.list(
        filter=combined_filters,
        order_by=order_by,
    )
  • project: Your project ID. You can find these IDs in the Google Cloud console welcome page.
  • location: See List of available locations.
  • display_name_filter: Filter to be applied to display name while listing the resources with the format "display_name=\"my_filter\"" .
  • create_date_filter: Filter to be applied to create_date name while listing the resources with the format "create_time>\"2022-06-11T12:30:00-08:00\"",.

Query for executions

Executions represent a step in your ML workflow, such as preprocessing data or training a model. Use the following instructions to query for executions.

REST

Before using any of the request data, make the following replacements:

  • LOCATION_ID: Your region.
  • PROJECT_ID: Your project ID.
  • METADATA_STORE: The metadata store ID where the execution is created. The default metadata store is named default.
  • PAGE_SIZE: (Optional) The maximum number of artifacts to return. If this value is not specified, the service returns a maximum of 100 records.
  • PAGE_TOKEN: (Optional) A page token from a previous MetadataService.ListArtifacts call. Specify this token to get the next page of results.

  • FILTER: Specifies the conditions required to include an execution in the result set.

HTTP method and URL: 

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/executions?pageSize=PAGE_SIZE&pageToken=PAGE_TOKEN&filter=FILTER

To send your request, expand one of these options:

You should see output similar to the following. EXECUTION_ID is the ID of the execution record.

{
  "executions": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "displayName": "Example execution 1",
      "etag": "67891011",
      "createTime": "2021-05-18T00:06:56.177Z",
      "updateTime": "2021-05-18T00:06:56.177Z",
      "schemaTitle": "system.Run",
      "schemaVersion": "0.0.1",
      "metadata": {},
      "description": "Description of the example execution."
    },
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "displayName": "Example execution 2",
      "etag": "67891011",
      "createTime": "2021-05-18T00:04:49.659Z",
      "updateTime": "2021-05-18T00:04:49.659Z",
      "schemaTitle": "system.Run",
      "schemaVersion": "0.0.1",
      "metadata": {},
      "description": "Description of the example execution."
    }
  ]
}

Python

Python

from typing import Optional

from google.cloud import aiplatform


def list_execution_sample(
    project: str,
    location: str,
    display_name_filter: Optional[str] = "display_name=\"my_execution_*\"",
    create_date_filter:  Optional[str] = "create_time>\"2022-06-11T12:30:00-08:00\"",
):
    aiplatform.init(
        project=project,
        location=location)

    combined_filters = f"{display_name_filter} AND {create_date_filter}"

    return aiplatform.Execution.list(filter=combined_filters)
  • project: Your project ID. You can find these IDs in the Google Cloud console welcome page.
  • location: See List of available locations.
  • display_name_filter: Filter to be applied to display name while listing the resources with the format "display_name=\"my_filter\"" .
  • create_date_filter: Filter to be applied to create_date name while listing the resources with the format "create_time>\"2022-06-11T12:30:00-08:00\"",.

Query for contexts

Contexts let you group sets of executions, artifacts, and other contexts. Use the following instructions to query for contexts.

REST

Before using any of the request data, make the following replacements:

  • LOCATION_ID: Your region.
  • PROJECT_ID: Your project ID.
  • METADATA_STORE: The metadata store ID where the context is created. The default metadata store is named default.
  • PAGE_SIZE: (Optional) The maximum number of artifacts to return. If this value is not specified, the service returns a maximum of 100 records.
  • PAGE_TOKEN: (Optional) A page token from a previous MetadataService.ListArtifacts call. Specify this token to get the next page of results.

  • FILTER: Specifies the conditions required to include a context in the result set.

HTTP method and URL: 

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/contexts?pageSize=PAGE_SIZE&pageToken=PAGE_TOKEN&filter=FILTER

To send your request, expand one of these options:

You should see output similar to the following. CONTEXT_ID is the ID of the context record.

{
  "contexts": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/contexts/CONTEXT_ID",
      "displayName": "Experiment 1",
      "etag": "67891011",
      "createTime": "2021-05-18T22:36:02.153Z",
      "updateTime": "2021-05-18T22:36:02.153Z",
      "parentContexts": [],
      "schemaTitle": "system.Experiment",
      "schemaVersion": "0.0.1",
      "metadata": {}
    },
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/contexts/CONTEXT_ID",
      "displayName": "Pipeline run 1",
      "etag": "67891011",
      "createTime": "2021-05-18T22:35:02.600Z",
      "updateTime": "2021-05-18T22:35:02.600Z",
      "parentContexts": [],
      "schemaTitle": "system.PipelineRun",
      "schemaVersion": "0.0.1",
      "metadata": {}
    }
  ]
}

Query for an execution's input and output artifacts

Use the following instructions to query for the artifacts and executions in the specified context, along with the events that connect artifacts to executions.

Vertex AI SDK for Python

Input artifacts

This Python SDK sample involves querying for an execution's input artifacts.

Python

from google.cloud import aiplatform


def get_execution_input_artifacts_sample(
    execution: aiplatform.Execution
):
    return execution.get_input_artifacts()

Output artifacts

This Python SDK sample involves querying for an execution's output artifacts.

Python

from google.cloud import aiplatform


def get_execution_output_artifacts_sample(
    execution: aiplatform.Execution
):
    return execution.get_output_artifacts()

REST

This REST sample includes querying for both an execution's input and output artifacts.

REST

Before using any of the request data, make the following replacements:

  • LOCATION_ID: Your region.
  • PROJECT_ID: Your project ID.
  • METADATA_STORE: The metadata store ID where the execution is created. The default metadata store is named default.
  • EXECUTION_ID: The ID of the execution record.

HTTP method and URL: 

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/executions/EXECUTION_ID:queryExecutionInputsAndOutputs

To send your request, expand one of these options:

You should see output similar to the following. EXECUTION_ID is The ID of the execution record. If the execution ID is not specified, Vertex ML Metadata created a unique identifier for this execution. ARTIFACT_ID is the ID of the artifact record.

{
  "artifacts": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "displayName": "Example artifact",
      "uri": "gs://your_bucket_name/artifacts/dataset.csv",
      "etag": "678901011",
      "createTime": "2021-05-18T00:29:24.344Z",
      "updateTime": "2021-05-18T00:29:24.344Z",
      "state": "LIVE",
      "schemaTitle": "system.Dataset",
      "schemaVersion": "0.0.1",
      "metadata": {
        "payload_format": "CSV"
      },
      "description": "Description of the example artifact."
    },
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "displayName": "Example artifact 2",
      "uri": "gs://your_bucket_name/artifacts/dataset.csv",
      "etag": "678901011",
      "createTime": "2021-05-18T00:33:13.833Z",
      "updateTime": "2021-05-18T00:33:13.833Z",
      "state": "LIVE",
      "schemaTitle": "system.Dataset",
      "schemaVersion": "0.0.1",
      "metadata": {
        "payload_format": "CSV"
      },
      "description": "Description of the example artifact."
    }
  ],
  "executions": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "displayName": "Example execution 1",
      "etag": "678901011",
      "createTime": "2021-05-18T00:04:49.659Z",
      "updateTime": "2021-05-18T00:04:49.659Z",
      "schemaTitle": "system.Run",
      "schemaVersion": "0.0.1",
      "metadata": {},
      "description": "Description of the example execution."
    }
  ],
  "events": [
    {
      "artifact": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "execution": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "eventTime": "2021-05-18T00:04:49.659Z",,
      "type": "INPUT",
    },
    {
      "artifact": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "execution": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "eventTime": "2021-05-18T00:04:49.659Z",,
      "type": "OUTPUT",
    }
  ]
}

Query for a context's lineage subgraph

Use the following instructions to query for the artifacts and executions in the specified context, along with the events that connect artifacts to executions.

REST

Before using any of the request data, make the following replacements:

  • LOCATION_ID: Your region.
  • PROJECT_ID: Your project ID.
  • METADATA_STORE: The metadata store ID where the execution is created. The default metadata store is named default.
  • CONTEXT_ID: (Optional) The ID of the context record.

HTTP method and URL: 

GET https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/metadataStores/METADATA_STORE/contexts/CONTEXT_ID:queryContextLineageSubgraph

To send your request, expand one of these options:

You should see output similar to the following. EXECUTION_ID is The ID of the execution record. If the execution ID is not specified, Vertex ML Metadata created a unique identifier for this execution. ARTIFACT_ID is the ID of the artifact record.

{
  "artifacts": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "displayName": "Example artifact",
      "uri": "gs://your_bucket_name/artifacts/dataset.csv",
      "etag": "678901011",
      "createTime": "2021-05-18T00:29:24.344Z",
      "updateTime": "2021-05-18T00:29:24.344Z",
      "state": "LIVE",
      "schemaTitle": "system.Dataset",
      "schemaVersion": "0.0.1",
      "metadata": {
        "payload_format": "CSV"
      },
      "description": "Description of the example artifact."
    },
    {
      "name": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "displayName": "Example artifact 2",
      "uri": "gs://your_bucket_name/artifacts/dataset.csv",
      "etag": "678901011",
      "createTime": "2021-05-18T00:33:13.833Z",
      "updateTime": "2021-05-18T00:33:13.833Z",
      "state": "LIVE",
      "schemaTitle": "system.Dataset",
      "schemaVersion": "0.0.1",
      "metadata": {
        "payload_format": "CSV"
      },
      "description": "Description of the example artifact."
    }
  ],
  "executions": [
    {
      "name": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "displayName": "Example execution 1",
      "etag": "678901011",
      "createTime": "2021-05-18T00:04:49.659Z",
      "updateTime": "2021-05-18T00:04:49.659Z",
      "schemaTitle": "system.Run",
      "schemaVersion": "0.0.1",
      "metadata": {},
      "description": "Description of the example execution."
    }
  ],
  "events": [
    {
      "artifact": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "execution": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "eventTime": "2021-05-18T00:04:49.659Z",,
      "type": "INPUT",
    },
    {
      "artifact": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/artifacts/ARTIFACT_ID",
      "execution": "projects/PROJECT_ID/locations/LOCATION/metadataStores/METADATA_STORE/executions/EXECUTION_ID",
      "eventTime": "2021-05-18T00:04:49.659Z",,
      "type": "OUTPUT",
    }
  ]
}

What's next