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    gcloud alpha mldiagnostics machine-learning-run create

    NAME
    gcloud alpha mldiagnostics machine-learning-run create - create a machine learning run
    SYNOPSIS
    gcloud alpha mldiagnostics machine-learning-run create ( MACHINE_LEARNING_RUN : --location = LOCATION ) [ --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) Create a machine learning run.
    EXAMPLES
    To create the new machine learning run, run: 
     gcloud  
alpha  
mldiagnostics  
machine-learning-run  
create  
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 
  
ACTIVE 

    Sample  
output:  
Create  
request  
issued  
 for 
:  
 [ 
gcloud-cli-run-id-01 ] 
  
Waiting  
 for 
  
operation  
 [ 
projects/PROJECT_ID/locations/us-central1/operations/operation-1770308728284-64a161ee4640d-38e79884-425e56e2 ] 
  
to  
complete…done.  
Created  
machine_learning_run  
 [ 
gcloud-cli-run-id-01 ] 
.
     
    If  
user  
want  
to  
list  
all  
the  
profiler  
session  
capture  
 in 
  
Google  
Cloud  
Storage  
bucket  
 ( 
recursively ) 
,  
create  
a  
special  
machine  
learning  
run  
with  
a  
special  
lable  
 ` 
list_existing_sessions_only ` 
  
as  
shown  
below:
     
     gcloud  
alpha  
mldiagnostics  
machine-learning-run  
create  
gcloud-cli-run-id-02  
 --run-group 
  
group-gcloud-cli  
 --gcs-path 
  
gs://diagon-prod-data-bucket/my-parent-directory  
 --display-name 
  
gcloud-cli-run-02  
 --labels 
  
 "created_by" 
 = 
 "cli" 
  
 --labels 
  
 "list_existing_sessions_only" 
 = 
 "true" 
 

    This  
will  
create  
a  
machine  
learning  
run  
 in 
  
COMPLETED  
state  
without  
any  
workload
details.  
User  
can  
navigate  
to  
the  
profiler  
list  
page  
to  
see  
all  
the
profiler  
sessions  
captured  
under  
gs://diagon-prod-data-bucket/my-parent-directory  
 ( 
recursively ) 
.
User  
can  
visualize  
any  
profiler  
session  
by  
passing  
the  
link  
and  
share  
it  
with  
others.
No  
further  
update  
is  
possible  
 for 
  
this  
machine  
learning  
run  
as  
it  
is  
marked  
as  
COMPLETED.
    POSITIONAL ARGUMENTS
    Machine learning run resource - Identifier. The name of the Machine Learning run. If not provided, a random UUID will be generated. 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 .
    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 created. 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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