gcloud alpha mldiagnostics machine-learning-run update

INFORMATION
gcloud alpha mldiagnostics machine-learning-run update is not available in universe domain universe.
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; default="gke"] [--run-group=RUN_GROUP] [--run-phase=RUN_PHASE; default="active"] [--tools=[xprof=XPROF]; default="xprof"] [--configs-hardware=[CONFIGS_HARDWARE,…] --configs-software=[CONFIGS_SOFTWARE,…] --configs-user=[CONFIGS_USER,…]] [--gke-cluster-name=GKE_CLUSTER_NAME --gke-kind=GKE_KIND --gke-namespace=GKE_NAMESPACE --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 my-run --location=us-central1 --gcs-path=gs://my-bucket/my-run --gke-cluster-name=projects/my-project/locations/us-central1/clusters/my-cluster --gke-workload-name=my-workload --gke-kind=Job --gke-namespace=default
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; default="gke"
The orchestrator used for the run. If not specified, gke will be used by default. 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)
--configs-user=[CONFIGS_USER,…]
User defined configs.
KEY
Sets KEY value.
VALUE
Sets VALUE value.
Shorthand Example: 
--configs-user=string=string

JSON Example: 

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

File Example: 

--configs-user=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>

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

--gke-kind=GKE_KIND
The kind of the workload. Example - JobSet

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

--gke-namespace=GKE_NAMESPACE
The namespace of the workload. Example - default

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

--gke-workload-name=GKE_WORKLOAD_NAME
The identifier of the workload. Example - jobset-abcd

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

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.