Struct googapis::google::cloud::ml::v1::TrainingInput [−][src]
pub struct TrainingInput {Show 13 fields
pub scale_tier: i32,
pub master_type: String,
pub worker_type: String,
pub parameter_server_type: String,
pub worker_count: i64,
pub parameter_server_count: i64,
pub package_uris: Vec<String>,
pub python_module: String,
pub args: Vec<String>,
pub hyperparameters: Option<HyperparameterSpec>,
pub region: String,
pub job_dir: String,
pub runtime_version: String,
}
Expand description
Represents input parameters for a training job.
Fields
scale_tier: i32
Required. Specifies the machine types, the number of replicas for workers and parameter servers.
master_type: 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 GPU that you can use in your trainer. - complex_model_m_gpu
-
A machine equivalent to
coplex_model_m
that also includes four GPUs.
You must set this value when scaleTier
is set to CUSTOM
.
worker_type: 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.
parameter_server_type: 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.
worker_count: 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_count: 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
.
package_uris: Vec<String>
Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies.
python_module: String
Required. The Python module name to run after installing the packages.
args: Vec<String>
Optional. Command line arguments to pass to the program.
hyperparameters: Option<HyperparameterSpec>
Optional. The set of Hyperparameters to tune.
region: String
Required. The Google Compute Engine region to run the training job in.
job_dir: 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.
runtime_version: String
Optional. The Google Cloud ML runtime version to use for training. If not set, Google Cloud ML will choose the latest stable version.
Implementations
Returns the enum value of scale_tier
, or the default if the field is set to an invalid enum value.
Sets scale_tier
to the provided enum value.
Trait Implementations
fn merge_field<B>(
&mut self,
tag: u32,
wire_type: WireType,
buf: &mut B,
ctx: DecodeContext
) -> Result<(), DecodeError> where
B: Buf,
Returns the encoded length of the message without a length delimiter.
Encodes the message to a buffer. Read more
Encodes the message to a newly allocated buffer.
Encodes the message with a length-delimiter to a buffer. Read more
Encodes the message with a length-delimiter to a newly allocated buffer.
Decodes an instance of the message from a buffer. Read more
fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
Self: Default,
B: Buf,
fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError> where
Self: Default,
B: Buf,
Decodes a length-delimited instance of the message from the buffer.
Decodes an instance of the message from a buffer, and merges it into self
. Read more
Decodes a length-delimited instance of the message from buffer, and
merges it into self
. Read more
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
This method tests for !=
.
Auto Trait Implementations
impl RefUnwindSafe for TrainingInput
impl Send for TrainingInput
impl Sync for TrainingInput
impl Unpin for TrainingInput
impl UnwindSafe for TrainingInput
Blanket Implementations
Mutably borrows from an owned value. Read more
Wrap the input message T
in a tonic::Request
pub fn vzip(self) -> V
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more