Struct google_ml1::GoogleCloudMlV1__TrainingInput

pub struct GoogleCloudMlV1__TrainingInput {
    pub worker_type: Option<String>,
    pub runtime_version: Option<String>,
    pub scale_tier: Option<String>,
    pub master_type: Option<String>,
    pub hyperparameters: Option<GoogleCloudMlV1__HyperparameterSpec>,
    pub region: Option<String>,
    pub args: Option<Vec<String>>,
    pub python_module: Option<String>,
    pub python_version: Option<String>,
    pub job_dir: 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.

runtime_version: Option<String>

Optional. The Google Cloud ML runtime version to use for training. If not set, Google Cloud ML will choose the latest stable version.

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.

standard_gpu

A machine equivalent to standard that also includes a single NVIDIA Tesla K80 GPU. See more about using GPUs for training your model.

complex_model_m_gpu

A machine equivalent to complex_model_m that also includes four NVIDIA Tesla K80 GPUs.

complex_model_l_gpu

A machine equivalent to complex_model_l that also includes eight NVIDIA Tesla K80 GPUs.

standard_p100

A machine equivalent to standard that also includes a single NVIDIA Tesla P100 GPU. The availability of these GPUs is in the Alpha launch stage.

complex_model_m_p100

A machine equivalent to complex_model_m that also includes four NVIDIA Tesla P100 GPUs. The availability of these GPUs is in the Alpha launch stage.

You must set this value when scaleTier is set to CUSTOM.

hyperparameters: Option<GoogleCloudMlV1__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.

python_version: Option<String>

Optional. The version of Python used in training. If not set, the default version is '2.7'.

job_dir: Option<String>

Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the 'job_dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.

package_uris: Option<Vec<String>>

Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.

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 Default for GoogleCloudMlV1__TrainingInput

fn default() -> GoogleCloudMlV1__TrainingInput

Returns the "default value" for a type.

impl Clone for GoogleCloudMlV1__TrainingInput

fn clone(&self) -> GoogleCloudMlV1__TrainingInput

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for GoogleCloudMlV1__TrainingInput

fn fmt(&self, f: &mut Formatter) -> Result

Formats the value using the given formatter.

impl Part for GoogleCloudMlV1__TrainingInput