gcloud alpha mldiagnostics machine-learning-run update

NAME: gcloud alpha mldiagnostics machine-learning-run update - update a machine learning run
SYNOPSIS: gcloud alpha mldiagnostics machine-learning-run update ( MACHINE_LEARNING_RUN : --location = LOCATION ) --etag = ETAG [ --async ] [ --display-name = DISPLAY_NAME ] [ --gcs-path = GCS_PATH ] [ --labels =[ LABELS , …]] [ --orchestrator = ORCHESTRATOR ] [ --run-group = RUN_GROUP ] [ --run-phase = RUN_PHASE ; default="active"] [ --tools =[ xprof = XPROF ]; default="xprof"] [ --configs-hardware =[ CONFIGS_HARDWARE , …] --configs-software =[ CONFIGS_SOFTWARE , …]] [ --gke-cluster-name = GKE_CLUSTER_NAME --gke-kind = GKE_KIND --gke-namespace = GKE_NAMESPACE --gke-workload-create-time = GKE_WORKLOAD_CREATE_TIME --gke-workload-name = GKE_WORKLOAD_NAME ] [ GCLOUD_WIDE_FLAG … ]
DESCRIPTION: (ALPHA) Update a machine learning run.
EXAMPLES: To update the machine learning run, run:
gcloud alpha mldiagnostics machine-learning-run update gcloud-cli-run-id-01 --orchestrator gke --run-group group-gcloud-cli --tools xprof --gcs-path gs://diagon-prod-data-bucket --display-name gcloud-cli-run --gke-cluster-name projects/PROJECT_ID/locations/us-central1/clusters/mldiag-prod-gke-cluster projects/PROJECT_ID/locations/us-central1/clusters/mldiag-prod-gke-cluster --gke-namespace diagon --gke-workload-name mldiag-prod-demo-jobset --gke-kind JobSet --gke-workload-create-time 2026 -02-20T06:00:00Z --labels "created_by" = "cli" --run-phase COMPLETED --etag "1f54a7f4-bd25-4f98-a91c-97bfa1c5b7a6"

Update command support only full update ( partial update is not supported ) . Hence, user should get the existing metadata of the ML run using get/list command and modify required fields. User need to pass latest ` etag ` for ML Run to ensure latest ML Run details were read before the update operation.

Sample output: Request issued for : [ my-run ] Waiting for operation [ projects/PROJECT_ID/locations/us-central1/operations/operation-1770309155834-64a1638604a06-34b8ba32-966d4a8f ] to complete…done. Updated machine_learning_run [ my-run ] . artifacts: gcsPath: gs://diagon-prod-data createTime: '2026-02-05T16:25:28.367865234Z' displayName: updated-gcloud-cli-run endTime: '0001-01-01T00:00:00Z' etag: 2a9dad60-a18b-4680-b63d-d0e16214713c labels: on_demand_xprof: enabled name: projects/PROJECT_ID/locations/us-central1/machineLearningRuns/my-run orchestrator: GKE runPhase: COMPLETED runSet: group-gcloud-cli tools: - xprof: {} updateTime: '2026-02-05T16:32:35.885684490Z' workloadDetails: gke: cluster: projects/PROJECT_ID/locations/us-central1/clusters/mldiag-prod-cluster id: mldiag-prod-demo-jobset kind: JobSet namespace: diagon
POSITIONAL ARGUMENTS: Machine learning run resource - Identifier. The name of the Machine Learning run. The arguments in this group can be used to specify the attributes of this resource. (NOTE) Some attributes are not given arguments in this group but can be set in other ways.
To set the project attribute:

provide the argument machine_learning_run on the command line with a fully specified name;

provide the argument --project on the command line;

set the property core/project .

This must be specified.

MACHINE_LEARNING_RUN

ID of the machine_learning_run or fully qualified identifier for the machine_learning_run.
To set the machine_learning_run attribute:

provide the argument machine_learning_run on the command line.

This positional argument must be specified if any of the other arguments in this group are specified.

--location = LOCATION

The location id of the machine_learning_run resource.
To set the location attribute:

provide the argument machine_learning_run on the command line with a fully specified name;

provide the argument --location on the command line;

set the property compute/region .
REQUIRED FLAGS: --etag = ETAG

ETag for the run. It must be provided for update/delete operations and must match the server's etag.
OPTIONAL FLAGS: --async

Return immediately, without waiting for the operation in progress to complete.

--display-name = DISPLAY_NAME

Display name for the run.

Represents information about the artifacts of the Machine Learning Run.

--gcs-path = GCS_PATH

The Cloud Storage path where the artifacts of the run are stored. Example: gs://my-bucket/my-run-directory .

--labels =[ LABELS ,…]

Any custom labels for this run Example: type:workload, type:simulation etc.

KEY

Keys must start with a lowercase character and contain only hyphens ( - ), underscores ( _ ), lowercase characters, and numbers.

VALUE

Values must contain only hyphens ( - ), underscores ( _ ), lowercase characters, and numbers.

Shorthand Example:
--labels = string = string

JSON Example:

--labels = '{"string": "string"}'

File Example:

--labels = path_to_file. ( yaml | json )

--orchestrator = ORCHESTRATOR

The orchestrator used for the run. ORCHESTRATOR must be one of:

gce

Google Compute Engine orchestrator.

gke

Google Kubernetes Engine orchestrator.

slurm

Slurm cluster orchestrator.

--run-group = RUN_GROUP

Allows grouping of similar runs.

Helps improve UI rendering performance.

Allows comparing similar runs via fast filters.

--run-phase = RUN_PHASE ; default="active"

RunPhase defines the phase of the run. This should be used only if non standard machine learning run needs to be updated. If not specified, run phase will be set to active by default. RUN_PHASE must be one of:

active

Run is active.

completed

Run is completed.

failed

Run is failed.

--tools =[ xprof = XPROF ]; default="xprof"

List of tools enabled for this run. This is a repeated argument, and each instance configures one tool. If no tools are specified, XProf will be used by default by the service.
To enable XProf without a specific session ID: --tools=xprof To enable XProf with a specific session ID: --tools=xprof:sessionId=my-session-id To enable multiple tools, repeat the argument: --tools=xprof:sessionId=123 --tools=nsys .

xprof

Configuration for the XProf tool.

sessionId

The session ID for XProf. Example: my-session-id .

Shorthand Example:
--tools = xprof ={ sessionId = string } --tools = xprof ={ sessionId = string }

JSON Example:

--tools = '[{"xprof": {"sessionId": "string"}}]'

File Example:

--tools = path_to_file. ( yaml | json )

Configuration for a Machine Learning run.

--configs-hardware =[ CONFIGS_HARDWARE ,…]

Hardware configs.

KEY

Sets KEY value.

VALUE

Sets VALUE value.

Shorthand Example:
--configs-hardware = string = string

JSON Example:

--configs-hardware = '{"string": "string"}'

File Example:

--configs-hardware = path_to_file. ( yaml | json )

--configs-software =[ CONFIGS_SOFTWARE ,…]

Software configs.

KEY

Sets KEY value.

VALUE

Sets VALUE value.

Shorthand Example:
--configs-software = string = string

JSON Example:

--configs-software = '{"string": "string"}'

File Example:

--configs-software = path_to_file. ( yaml | json )

Workload details associated for the Machine Learning Run. Workload have different metadata based on the orchestrator like GKE cluster, Slurm cluster, Google Compute Engine instance etc.

Arguments for the metadata.

Workload details for the GKE orchestrator.

--gke-cluster-name = GKE_CLUSTER_NAME

The cluster of the workload. Example - /projects/<project id>/locations/<location>/clusters/<cluster name>

--gke-kind = GKE_KIND

The kind of the workload. Example - JobSet

--gke-namespace = GKE_NAMESPACE

The namespace of the workload. Example - default

--gke-workload-create-time = GKE_WORKLOAD_CREATE_TIME

The create timestamp of the workload. Example - 2026-02-20T06:00:00Z

--gke-workload-name = GKE_WORKLOAD_NAME

The identifier of the workload. Example - jobset-abcd
GCLOUD WIDE FLAGS: These flags are available to all commands: --access-token-file , --account , --billing-project , --configuration , --flags-file , --flatten , --format , --help , --impersonate-service-account , --log-http , --project , --quiet , --trace-token , --user-output-enabled , --verbosity .
Run $ gcloud help for details.
API REFERENCE: This command uses the hypercomputecluster/v1alpha API. The full documentation for this API can be found at: https://docs.cloud.google.com/cluster-director/docs
NOTES: This command is currently in alpha and might change without notice. If this command fails with API permission errors despite specifying the correct project, you might be trying to access an API with an invitation-only early access allowlist.

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gcloud alpha mldiagnostics machine-learning-run update