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ancestors
Type: UNORDERED_LIST_STRING
create_time
Type: TIMESTAMP
Provider name: createTime
Description: Output only. Time when the HyperparameterTuningJob was created.
encryption_spec
Type: STRUCT
Provider name: encryptionSpec
Description: Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
kms_key_name
Type: STRING
Provider name: kmsKeyName
Description: Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.
end_time
Type: TIMESTAMP
Provider name: endTime
Description: Output only. Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
error
Type: STRUCT
Provider name: error
Description: Output only. Only populated when job’s state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
code
Type: INT32
Provider name: code
Description: The status code, which should be an enum value of google.rpc.Code.
message
Type: STRING
Provider name: message
Description: A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
gcp_display_name
Type: STRING
Provider name: displayName
Description: Required. The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
labels
Type: UNORDERED_LIST_STRING
max_failed_trial_count
Type: INT32
Provider name: maxFailedTrialCount
Description: The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails.
max_trial_count
Type: INT32
Provider name: maxTrialCount
Description: Required. The desired total number of Trials.
name
Type: STRING
Provider name: name
Description: Output only. Resource name of the HyperparameterTuningJob.
organization_id
Type: STRING
parallel_trial_count
Type: INT32
Provider name: parallelTrialCount
Description: Required. The desired number of Trials to run in parallel.
parent
Type: STRING
project_id
Type: STRING
project_number
Type: STRING
resource_name
Type: STRING
satisfies_pzi
Type: BOOLEAN
Provider name: satisfiesPzi
Description: Output only. Reserved for future use.
satisfies_pzs
Type: BOOLEAN
Provider name: satisfiesPzs
Description: Output only. Reserved for future use.
start_time
Type: TIMESTAMP
Provider name: startTime
Description: Output only. Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
state
Type: STRING
Provider name: state
Description: Output only. The detailed state of the job.
Possible values:
JOB_STATE_UNSPECIFIED - The job state is unspecified.
JOB_STATE_QUEUED - The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING - The service is preparing to run the job.
JOB_STATE_RUNNING - The job is in progress.
JOB_STATE_SUCCEEDED - The job completed successfully.
JOB_STATE_FAILED - The job failed.
JOB_STATE_CANCELLING - The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED - The job has been cancelled.
JOB_STATE_PAUSED - The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED - The job has expired.
JOB_STATE_UPDATING - The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED - The job is partially succeeded, some results may be missing due to errors.
study_spec
Type: STRUCT
Provider name: studySpec
Description: Required. Study configuration of the HyperparameterTuningJob.
algorithm
Type: STRING
Provider name: algorithm
Description: The search algorithm specified for the Study.
Possible values:
ALGORITHM_UNSPECIFIED - The default algorithm used by Vertex AI for hyperparameter tuning and Vertex AI Vizier.
GRID_SEARCH - Simple grid search within the feasible space. To use grid search, all parameters must be INTEGER, CATEGORICAL, or DISCRETE.
RANDOM_SEARCH - Simple random search within the feasible space.
convex_automated_stopping_spec
Type: STRUCT
Provider name: convexAutomatedStoppingSpec
Description: The automated early stopping spec using convex stopping rule.
learning_rate_parameter_name
Type: STRING
Provider name: learningRateParameterName
Description: The hyper-parameter name used in the tuning job that stands for learning rate. Leave it blank if learning rate is not in a parameter in tuning. The learning_rate is used to estimate the objective value of the ongoing trial.
max_step_count
Type: INT64
Provider name: maxStepCount
Description: Steps used in predicting the final objective for early stopped trials. In general, it’s set to be the same as the defined steps in training / tuning. If not defined, it will learn it from the completed trials. When use_steps is false, this field is set to the maximum elapsed seconds.
min_measurement_count
Type: INT64
Provider name: minMeasurementCount
Description: The minimal number of measurements in a Trial. Early-stopping checks will not trigger if less than min_measurement_count+1 completed trials or pending trials with less than min_measurement_count measurements. If not defined, the default value is 5.
min_step_count
Type: INT64
Provider name: minStepCount
Description: Minimum number of steps for a trial to complete. Trials which do not have a measurement with step_count > min_step_count won’t be considered for early stopping. It’s ok to set it to 0, and a trial can be early stopped at any stage. By default, min_step_count is set to be one-tenth of the max_step_count. When use_elapsed_duration is true, this field is set to the minimum elapsed seconds.
update_all_stopped_trials
Type: BOOLEAN
Provider name: updateAllStoppedTrials
Description: ConvexAutomatedStoppingSpec by default only updates the trials that needs to be early stopped using a newly trained auto-regressive model. When this flag is set to True, all stopped trials from the beginning are potentially updated in terms of their final_measurement. Also, note that the training logic of autoregressive models is different in this case. Enabling this option has shown better results and this may be the default option in the future.
use_elapsed_duration
Type: BOOLEAN
Provider name: useElapsedDuration
Description: This bool determines whether or not the rule is applied based on elapsed_secs or steps. If use_elapsed_duration==false, the early stopping decision is made according to the predicted objective values according to the target steps. If use_elapsed_duration==true, elapsed_secs is used instead of steps. Also, in this case, the parameters max_num_steps and min_num_steps are overloaded to contain max_elapsed_seconds and min_elapsed_seconds.
decay_curve_stopping_spec
Type: STRUCT
Provider name: decayCurveStoppingSpec
Description: The automated early stopping spec using decay curve rule.
use_elapsed_duration
Type: BOOLEAN
Provider name: useElapsedDuration
Description: True if Measurement.elapsed_duration is used as the x-axis of each Trials Decay Curve. Otherwise, Measurement.step_count will be used as the x-axis.
measurement_selection_type
Type: STRING
Provider name: measurementSelectionType
Description: Describe which measurement selection type will be used
Possible values:
MEASUREMENT_SELECTION_TYPE_UNSPECIFIED - Will be treated as LAST_MEASUREMENT.
LAST_MEASUREMENT - Use the last measurement reported.
BEST_MEASUREMENT - Use the best measurement reported.
median_automated_stopping_spec
Type: STRUCT
Provider name: medianAutomatedStoppingSpec
Description: The automated early stopping spec using median rule.
use_elapsed_duration
Type: BOOLEAN
Provider name: useElapsedDuration
Description: True if median automated stopping rule applies on Measurement.elapsed_duration. It means that elapsed_duration field of latest measurement of current Trial is used to compute median objective value for each completed Trials.
metrics
Type: UNORDERED_LIST_STRUCT
Provider name: metrics
Description: Required. Metric specs for the Study.
goal
Type: STRING
Provider name: goal
Description: Required. The optimization goal of the metric.
Possible values:
GOAL_TYPE_UNSPECIFIED - Goal Type will default to maximize.
MAXIMIZE - Maximize the goal metric.
MINIMIZE - Minimize the goal metric.
metric_id
Type: STRING
Provider name: metricId
Description: Required. The ID of the metric. Must not contain whitespaces and must be unique amongst all MetricSpecs.
safety_config
Type: STRUCT
Provider name: safetyConfig
Description: Used for safe search. In the case, the metric will be a safety metric. You must provide a separate metric for objective metric.
desired_min_safe_trials_fraction
Type: DOUBLE
Provider name: desiredMinSafeTrialsFraction
Description: Desired minimum fraction of safe trials (over total number of trials) that should be targeted by the algorithm at any time during the study (best effort). This should be between 0.0 and 1.0 and a value of 0.0 means that there is no minimum and an algorithm proceeds without targeting any specific fraction. A value of 1.0 means that the algorithm attempts to only Suggest safe Trials.
safety_threshold
Type: DOUBLE
Provider name: safetyThreshold
Description: Safety threshold (boundary value between safe and unsafe). NOTE that if you leave SafetyMetricConfig unset, a default value of 0 will be used.
observation_noise
Type: STRING
Provider name: observationNoise
Description: The observation noise level of the study. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
Possible values:
OBSERVATION_NOISE_UNSPECIFIED - The default noise level chosen by Vertex AI.
LOW - Vertex AI assumes that the objective function is (nearly) perfectly reproducible, and will never repeat the same Trial parameters.
HIGH - Vertex AI will estimate the amount of noise in metric evaluations, it may repeat the same Trial parameters more than once.
parameters
Type: UNORDERED_LIST_STRUCT
Provider name: parameters
Description: Required. The set of parameters to tune.
categorical_value_spec
Type: STRUCT
Provider name: categoricalValueSpec
Description: The value spec for a ‘CATEGORICAL’ parameter.
default_value
Type: STRING
Provider name: defaultValue
Description: A default value for a CATEGORICAL parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
values
Type: UNORDERED_LIST_STRING
Provider name: values
Description: Required. The list of possible categories.
conditional_parameter_specs
Type: UNORDERED_LIST_STRUCT
Provider name: conditionalParameterSpecs
Description: A conditional parameter node is active if the parameter’s value matches the conditional node’s parent_value_condition. If two items in conditional_parameter_specs have the same name, they must have disjoint parent_value_condition.
parent_categorical_values
Type: STRUCT
Provider name: parentCategoricalValues
Description: The spec for matching values from a parent parameter of CATEGORICAL type.
values
Type: UNORDERED_LIST_STRING
Provider name: values
Description: Required. Matches values of the parent parameter of ‘CATEGORICAL’ type. All values must exist in categorical_value_spec of parent parameter.
parent_discrete_values
Type: STRUCT
Provider name: parentDiscreteValues
Description: The spec for matching values from a parent parameter of DISCRETE type.
values
Type: UNORDERED_LIST_DOUBLE
Provider name: values
Description: Required. Matches values of the parent parameter of ‘DISCRETE’ type. All values must exist in discrete_value_spec of parent parameter. The Epsilon of the value matching is 1e-10.
parent_int_values
Type: STRUCT
Provider name: parentIntValues
Description: The spec for matching values from a parent parameter of INTEGER type.
values
Type: UNORDERED_LIST_INT64
Provider name: values
Description: Required. Matches values of the parent parameter of ‘INTEGER’ type. All values must lie in integer_value_spec of parent parameter.
discrete_value_spec
Type: STRUCT
Provider name: discreteValueSpec
Description: The value spec for a ‘DISCRETE’ parameter.
default_value
Type: DOUBLE
Provider name: defaultValue
Description: A default value for a DISCRETE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. It automatically rounds to the nearest feasible discrete point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
values
Type: UNORDERED_LIST_DOUBLE
Provider name: values
Description: Required. A list of possible values. The list should be in increasing order and at least 1e-10 apart. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
double_value_spec
Type: STRUCT
Provider name: doubleValueSpec
Description: The value spec for a ‘DOUBLE’ parameter.
default_value
Type: DOUBLE
Provider name: defaultValue
Description: A default value for a DOUBLE parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
max_value
Type: DOUBLE
Provider name: maxValue
Description: Required. Inclusive maximum value of the parameter.
min_value
Type: DOUBLE
Provider name: minValue
Description: Required. Inclusive minimum value of the parameter.
integer_value_spec
Type: STRUCT
Provider name: integerValueSpec
Description: The value spec for an ‘INTEGER’ parameter.
default_value
Type: INT64
Provider name: defaultValue
Description: A default value for an INTEGER parameter that is assumed to be a relatively good starting point. Unset value signals that there is no offered starting point. Currently only supported by the Vertex AI Vizier service. Not supported by HyperparameterTuningJob or TrainingPipeline.
max_value
Type: INT64
Provider name: maxValue
Description: Required. Inclusive maximum value of the parameter.
min_value
Type: INT64
Provider name: minValue
Description: Required. Inclusive minimum value of the parameter.
parameter_id
Type: STRING
Provider name: parameterId
Description: Required. The ID of the parameter. Must not contain whitespaces and must be unique amongst all ParameterSpecs.
scale_type
Type: STRING
Provider name: scaleType
Description: How the parameter should be scaled. Leave unset for CATEGORICAL parameters.
Possible values:
SCALE_TYPE_UNSPECIFIED - By default, no scaling is applied.
UNIT_LINEAR_SCALE - Scales the feasible space to (0, 1) linearly.
UNIT_LOG_SCALE - Scales the feasible space logarithmically to (0, 1). The entire feasible space must be strictly positive.
UNIT_REVERSE_LOG_SCALE - Scales the feasible space ‘reverse’ logarithmically to (0, 1). The result is that values close to the top of the feasible space are spread out more than points near the bottom. The entire feasible space must be strictly positive.
study_stopping_config
Type: STRUCT
Provider name: studyStoppingConfig
Description: Conditions for automated stopping of a Study. Enable automated stopping by configuring at least one condition.
max_duration_no_progress
Type: STRING
Provider name: maxDurationNoProgress
Description: If the objective value has not improved for this much time, stop the study. WARNING: Effective only for single-objective studies.
max_num_trials
Type: INT32
Provider name: maxNumTrials
Description: If there are more than this many trials, stop the study.
max_num_trials_no_progress
Type: INT32
Provider name: maxNumTrialsNoProgress
Description: If the objective value has not improved for this many consecutive trials, stop the study. WARNING: Effective only for single-objective studies.
maximum_runtime_constraint
Type: STRUCT
Provider name: maximumRuntimeConstraint
Description: If the specified time or duration has passed, stop the study.
end_time
Type: TIMESTAMP
Provider name: endTime
Description: Compares the wallclock time to this time. Must use UTC timezone.
max_duration
Type: STRING
Provider name: maxDuration
Description: Counts the wallclock time passed since the creation of this Study.
min_num_trials
Type: INT32
Provider name: minNumTrials
Description: If there are fewer than this many COMPLETED trials, do not stop the study.
minimum_runtime_constraint
Type: STRUCT
Provider name: minimumRuntimeConstraint
Description: Each “stopping rule” in this proto specifies an “if” condition. Before Vizier would generate a new suggestion, it first checks each specified stopping rule, from top to bottom in this list. Note that the first few rules (e.g. minimum_runtime_constraint, min_num_trials) will prevent other stopping rules from being evaluated until they are met. For example, setting min_num_trials=5 and always_stop_after= 1 hour means that the Study will ONLY stop after it has 5 COMPLETED trials, even if more than an hour has passed since its creation. It follows the first applicable rule (whose “if” condition is satisfied) to make a stopping decision. If none of the specified rules are applicable, then Vizier decides that the study should not stop. If Vizier decides that the study should stop, the study enters STOPPING state (or STOPPING_ASAP if should_stop_asap = true). IMPORTANT: The automatic study state transition happens precisely as described above; that is, deleting trials or updating StudyConfig NEVER automatically moves the study state back to ACTIVE. If you want to resume a Study that was stopped, 1) change the stopping conditions if necessary, 2) activate the study, and then 3) ask for suggestions. If the specified time or duration has not passed, do not stop the study.
end_time
Type: TIMESTAMP
Provider name: endTime
Description: Compares the wallclock time to this time. Must use UTC timezone.
max_duration
Type: STRING
Provider name: maxDuration
Description: Counts the wallclock time passed since the creation of this Study.
should_stop_asap
Type: BOOLEAN
Provider name: shouldStopAsap
Description: If true, a Study enters STOPPING_ASAP whenever it would normally enters STOPPING state. The bottom line is: set to true if you want to interrupt on-going evaluations of Trials as soon as the study stopping condition is met. (Please see Study.State documentation for the source of truth).
Type: UNORDERED_LIST_STRING
trial_job_spec
Type: STRUCT
Provider name: trialJobSpec
Description: Required. The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
base_output_directory
Type: STRUCT
Provider name: baseOutputDirectory
Description: The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob’s baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/
output_uri_prefix
Type: STRING
Provider name: outputUriPrefix
Description: Required. Google Cloud Storage URI to output directory. If the uri doesn’t end with ‘/’, a ‘/’ will be automatically appended. The directory is created if it doesn’t exist.
enable_dashboard_access
Type: BOOLEAN
Provider name: enableDashboardAccess
Description: Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enable_web_access
Type: BOOLEAN
Provider name: enableWebAccess
Description: Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment
Type: STRING
Provider name: experiment
Description: Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experiment_run
Type: STRING
Provider name: experimentRun
Description: Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
models
Type: UNORDERED_LIST_STRING
Provider name: models
Description: Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: projects/{project}/locations/{location}/models/{model} In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the “default” version will be returned. The “default” version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.
network
Type: STRING
Provider name: network
Description: Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.
persistent_resource_id
Type: STRING
Provider name: persistentResourceId
Description: Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
protected_artifact_location_id
Type: STRING
Provider name: protectedArtifactLocationId
Description: The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
reserved_ip_ranges
Type: UNORDERED_LIST_STRING
Provider name: reservedIpRanges
Description: Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: [‘vertex-ai-ip-range’].
scheduling
Type: STRUCT
Provider name: scheduling
Description: Scheduling options for a CustomJob.
disable_retries
Type: BOOLEAN
Provider name: disableRetries
Description: Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.
max_wait_duration
Type: STRING
Provider name: maxWaitDuration
Description: Optional. This is the maximum duration that a job will wait for the requested resources to be provisioned if the scheduling strategy is set to [Strategy.DWS_FLEX_START]. If set to 0, the job will wait indefinitely. The default is 24 hours.
restart_job_on_worker_restart
Type: BOOLEAN
Provider name: restartJobOnWorkerRestart
Description: Optional. Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.
strategy
Type: STRING
Provider name: strategy
Description: Optional. This determines which type of scheduling strategy to use.
Possible values:
STRATEGY_UNSPECIFIED - Strategy will default to STANDARD.
ON_DEMAND - Deprecated. Regular on-demand provisioning strategy.
LOW_COST - Deprecated. Low cost by making potential use of spot resources.
STANDARD - Standard provisioning strategy uses regular on-demand resources.
SPOT - Spot provisioning strategy uses spot resources.
FLEX_START - Flex Start strategy uses DWS to queue for resources.
timeout
Type: STRING
Provider name: timeout
Description: Optional. The maximum job running time. The default is 7 days.
service_account
Type: STRING
Provider name: serviceAccount
Description: Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob’s project is used.
tensorboard
Type: STRING
Provider name: tensorboard
Description: Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
worker_pool_specs
Type: UNORDERED_LIST_STRUCT
Provider name: workerPoolSpecs
Description: Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
container_spec
Type: STRUCT
Provider name: containerSpec
Description: The custom container task.
args
Type: UNORDERED_LIST_STRING
Provider name: args
Description: The arguments to be passed when starting the container.
command
Type: UNORDERED_LIST_STRING
Provider name: command
Description: The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
env
Type: UNORDERED_LIST_STRUCT
Provider name: env
Description: Environment variables to be passed to the container. Maximum limit is 100.
name
Type: STRING
Provider name: name
Description: Required. Name of the environment variable. Must be a valid C identifier.
value
Type: STRING
Provider name: value
Description: Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
image_uri
Type: STRING
Provider name: imageUri
Description: Required. The URI of a container image in the Container Registry that is to be run on each worker replica.
disk_spec
Type: STRUCT
Provider name: diskSpec
Description: Disk spec.
boot_disk_size_gb
Type: INT32
Provider name: bootDiskSizeGb
Description: Size in GB of the boot disk (default is 100GB).
boot_disk_type
Type: STRING
Provider name: bootDiskType
Description: Type of the boot disk. For non-A3U machines, the default value is “pd-ssd”, for A3U machines, the default value is “hyperdisk-balanced”. Valid values: “pd-ssd” (Persistent Disk Solid State Drive), “pd-standard” (Persistent Disk Hard Disk Drive) or “hyperdisk-balanced”.
machine_spec
Type: STRUCT
Provider name: machineSpec
Description: Optional. Immutable. The specification of a single machine.
accelerator_count
Type: INT32
Provider name: acceleratorCount
Description: The number of accelerators to attach to the machine.
accelerator_type
Type: STRING
Provider name: acceleratorType
Description: Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
Possible values:
ACCELERATOR_TYPE_UNSPECIFIED - Unspecified accelerator type, which means no accelerator.
NVIDIA_TESLA_K80 - Deprecated: Nvidia Tesla K80 GPU has reached end of support, see https://cloud.google.com/compute/docs/eol/k80-eol.
NVIDIA_TESLA_P100 - Nvidia Tesla P100 GPU.
NVIDIA_TESLA_V100 - Nvidia Tesla V100 GPU.
NVIDIA_TESLA_P4 - Nvidia Tesla P4 GPU.
NVIDIA_TESLA_T4 - Nvidia Tesla T4 GPU.
NVIDIA_TESLA_A100 - Nvidia Tesla A100 GPU.
NVIDIA_A100_80GB - Nvidia A100 80GB GPU.
NVIDIA_L4 - Nvidia L4 GPU.
NVIDIA_H100_80GB - Nvidia H100 80Gb GPU.
NVIDIA_H100_MEGA_80GB - Nvidia H100 Mega 80Gb GPU.
NVIDIA_H200_141GB - Nvidia H200 141Gb GPU.
TPU_V2 - TPU v2.
TPU_V3 - TPU v3.
TPU_V4_POD - TPU v4.
TPU_V5_LITEPOD - TPU v5.
machine_type
Type: STRING
Provider name: machineType
Description: Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
reservation_affinity
Type: STRUCT
Provider name: reservationAffinity
Description: Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
key
Type: STRING
Provider name: key
Description: Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.
reservation_affinity_type
Type: STRING
Provider name: reservationAffinityType
Description: Required. Specifies the reservation affinity type.
Possible values:
TYPE_UNSPECIFIED - Default value. This should not be used.
NO_RESERVATION - Do not consume from any reserved capacity, only use on-demand.
ANY_RESERVATION - Consume any reservation available, falling back to on-demand.
SPECIFIC_RESERVATION - Consume from a specific reservation. When chosen, the reservation must be identified via the key and values fields.
values
Type: UNORDERED_LIST_STRING
Provider name: values
Description: Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation or reservation block.
tpu_topology
Type: STRING
Provider name: tpuTopology
Description: Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: “2x2x1”).
nfs_mounts
Type: UNORDERED_LIST_STRUCT
Provider name: nfsMounts
Description: Optional. List of NFS mount spec.
mount_point
Type: STRING
Provider name: mountPoint
Description: Required. Destination mount path. The NFS will be mounted for the user under /mnt/nfs/
path
Type: STRING
Provider name: path
Description: Required. Source path exported from NFS server. Has to start with ‘/’, and combined with the ip address, it indicates the source mount path in the form of server:path
server
Type: STRING
Provider name: server
Description: Required. IP address of the NFS server.
python_package_spec
Type: STRUCT
Provider name: pythonPackageSpec
Description: The Python packaged task.
args
Type: UNORDERED_LIST_STRING
Provider name: args
Description: Command line arguments to be passed to the Python task.
env
Type: UNORDERED_LIST_STRUCT
Provider name: env
Description: Environment variables to be passed to the python module. Maximum limit is 100.
name
Type: STRING
Provider name: name
Description: Required. Name of the environment variable. Must be a valid C identifier.
value
Type: STRING
Provider name: value
Description: Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.
executor_image_uri
Type: STRING
Provider name: executorImageUri
Description: Required. The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users’ various use cases. See the list of pre-built containers for training. You must use an image from this list.
package_uris
Type: UNORDERED_LIST_STRING
Provider name: packageUris
Description: Required. The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.
python_module
Type: STRING
Provider name: pythonModule
Description: Required. The Python module name to run after installing the packages.
replica_count
Type: INT64
Provider name: replicaCount
Description: Optional. The number of worker replicas to use for this worker pool.
trials
Type: UNORDERED_LIST_STRUCT
Provider name: trials
Description: Output only. Trials of the HyperparameterTuningJob.
client_id
Type: STRING
Provider name: clientId
Description: Output only. The identifier of the client that originally requested this Trial. Each client is identified by a unique client_id. When a client asks for a suggestion, Vertex AI Vizier will assign it a Trial. The client should evaluate the Trial, complete it, and report back to Vertex AI Vizier. If suggestion is asked again by same client_id before the Trial is completed, the same Trial will be returned. Multiple clients with different client_ids can ask for suggestions simultaneously, each of them will get their own Trial.
custom_job
Type: STRING
Provider name: customJob
Description: Output only. The CustomJob name linked to the Trial. It’s set for a HyperparameterTuningJob’s Trial.
end_time
Type: TIMESTAMP
Provider name: endTime
Description: Output only. Time when the Trial’s status changed to SUCCEEDED or INFEASIBLE.
final_measurement
Type: STRUCT
Provider name: finalMeasurement
Description: Output only. The final measurement containing the objective value.
elapsed_duration
Type: STRING
Provider name: elapsedDuration
Description: Output only. Time that the Trial has been running at the point of this Measurement.
metrics
Type: UNORDERED_LIST_STRUCT
Provider name: metrics
Description: Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
metric_id
Type: STRING
Provider name: metricId
Description: Output only. The ID of the Metric. The Metric should be defined in StudySpec’s Metrics.
value
Type: DOUBLE
Provider name: value
Description: Output only. The value for this metric.
step_count
Type: INT64
Provider name: stepCount
Description: Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
id
Type: STRING
Provider name: id
Description: Output only. The identifier of the Trial assigned by the service.
infeasible_reason
Type: STRING
Provider name: infeasibleReason
Description: Output only. A human readable string describing why the Trial is infeasible. This is set only if Trial state is INFEASIBLE.
measurements
Type: UNORDERED_LIST_STRUCT
Provider name: measurements
Description: Output only. A list of measurements that are strictly lexicographically ordered by their induced tuples (steps, elapsed_duration). These are used for early stopping computations.
elapsed_duration
Type: STRING
Provider name: elapsedDuration
Description: Output only. Time that the Trial has been running at the point of this Measurement.
metrics
Type: UNORDERED_LIST_STRUCT
Provider name: metrics
Description: Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.
metric_id
Type: STRING
Provider name: metricId
Description: Output only. The ID of the Metric. The Metric should be defined in StudySpec’s Metrics.
value
Type: DOUBLE
Provider name: value
Description: Output only. The value for this metric.
step_count
Type: INT64
Provider name: stepCount
Description: Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
name
Type: STRING
Provider name: name
Description: Output only. Resource name of the Trial assigned by the service.
parameters
Type: UNORDERED_LIST_STRUCT
Provider name: parameters
Description: Output only. The parameters of the Trial.
parameter_id
Type: STRING
Provider name: parameterId
Description: Output only. The ID of the parameter. The parameter should be defined in StudySpec’s Parameters.
start_time
Type: TIMESTAMP
Provider name: startTime
Description: Output only. Time when the Trial was started.
state
Type: STRING
Provider name: state
Description: Output only. The detailed state of the Trial.
Possible values:
STATE_UNSPECIFIED - The Trial state is unspecified.
REQUESTED - Indicates that a specific Trial has been requested, but it has not yet been suggested by the service.
ACTIVE - Indicates that the Trial has been suggested.
STOPPING - Indicates that the Trial should stop according to the service.
SUCCEEDED - Indicates that the Trial is completed successfully.
INFEASIBLE - Indicates that the Trial should not be attempted again. The service will set a Trial to INFEASIBLE when it’s done but missing the final_measurement.
update_time
Type: TIMESTAMP
Provider name: updateTime
Description: Output only. Time when the HyperparameterTuningJob was most recently updated.
