Struct google_ml1_beta1::GoogleCloudMlV1beta1__TrainingInput
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pub struct GoogleCloudMlV1beta1__TrainingInput {
pub worker_type: Option<String>,
pub scale_tier: Option<String>,
pub master_type: Option<String>,
pub hyperparameters: Option<GoogleCloudMlV1beta1__HyperparameterSpec>,
pub region: Option<String>,
pub args: Option<Vec<String>>,
pub python_module: Option<String>,
pub package_uris: Option<Vec<String>>,
pub worker_count: Option<i64>,
pub parameter_server_type: Option<String>,
pub parameter_server_count: Option<i64>,
}Represents input parameters for a training job.
This type is not used in any activity, and only used as part of another schema.
Fields
worker_type: Option<String>
Optional. Specifies the type of virtual machine to use for your training job's worker nodes.
The supported values are the same as those described in the entry for
masterType.
This value must be present when scaleTier is set to CUSTOM and
workerCount is greater than zero.
scale_tier: Option<String>
Required. Specifies the machine types, the number of replicas for workers and parameter servers.
master_type: Option<String>
Optional. Specifies the type of virtual machine to use for your training job's master worker.
The following types are supported:
- standard
- A basic machine configuration suitable for training simple models with small to moderate datasets.
- large_model
- A machine with a lot of memory, specially suited for parameter servers when your model is large (having many hidden layers or layers with very large numbers of nodes).
- complex_model_s
- A machine suitable for the master and workers of the cluster when your model requires more computation than the standard machine can handle satisfactorily.
- complex_model_m
-
A machine with roughly twice the number of cores and roughly double the
memory of
complex_model_s. - complex_model_l
-
A machine with roughly twice the number of cores and roughly double the
memory of
complex_model_m.
You must set this value when scaleTier is set to CUSTOM.
hyperparameters: Option<GoogleCloudMlV1beta1__HyperparameterSpec>
Optional. The set of Hyperparameters to tune.
region: Option<String>
Required. The Google Compute Engine region to run the training job in.
args: Option<Vec<String>>
Optional. Command line arguments to pass to the program.
python_module: Option<String>
Required. The Python module name to run after installing the packages.
package_uris: Option<Vec<String>>
Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies.
worker_count: Option<i64>
Optional. The number of worker replicas to use for the training job. Each
replica in the cluster will be of the type specified in worker_type.
This value can only be used when scale_tier is set to CUSTOM. If you
set this value, you must also set worker_type.
parameter_server_type: Option<String>
Optional. Specifies the type of virtual machine to use for your training job's parameter server.
The supported values are the same as those described in the entry for
master_type.
This value must be present when scaleTier is set to CUSTOM and
parameter_server_count is greater than zero.
parameter_server_count: Option<i64>
Optional. The number of parameter server replicas to use for the training
job. Each replica in the cluster will be of the type specified in
parameter_server_type.
This value can only be used when scale_tier is set to CUSTOM.If you
set this value, you must also set parameter_server_type.
Trait Implementations
impl Debug for GoogleCloudMlV1beta1__TrainingInput[src]
impl Clone for GoogleCloudMlV1beta1__TrainingInput[src]
fn clone(&self) -> GoogleCloudMlV1beta1__TrainingInput
Returns a copy of the value. Read more
fn clone_from(&mut self, source: &Self)1.0.0
Performs copy-assignment from source. Read more
impl Default for GoogleCloudMlV1beta1__TrainingInput[src]
fn default() -> GoogleCloudMlV1beta1__TrainingInput
Returns the "default value" for a type. Read more