puttrainedmodel

Documentation

Overview

Creates an inference trained model.

Index

• Variables
• type NewPutTrainedModel
  • func NewPutTrainedModelFunc(tp elastictransport.Interface) NewPutTrainedModel
• type PutTrainedModel
  • func New(tp elastictransport.Interface) *PutTrainedModel
  • func (r *PutTrainedModel) DeferDefinitionDecompression(b bool) *PutTrainedModel
  • func (r PutTrainedModel) Do(ctx context.Context) (*Response, error)
  • func (r *PutTrainedModel) Header(key, value string) *PutTrainedModel
  • func (r *PutTrainedModel) HttpRequest(ctx context.Context) (*http.Request, error)
  • func (r *PutTrainedModel) ModelId(v string) *PutTrainedModel
  • func (r PutTrainedModel) Perform(ctx context.Context) (*http.Response, error)
  • func (r *PutTrainedModel) Raw(raw io.Reader) *PutTrainedModel
  • func (r *PutTrainedModel) Request(req *Request) *PutTrainedModel
• type Request
  • func NewRequest() *Request
  • func (r *Request) FromJSON(data string) (*Request, error)
• type Response
  • func NewResponse() *Response

Variables

var ErrBuildPath = errors.New("cannot build path, check for missing path parameters")

ErrBuildPath is returned in case of missing parameters within the build of the request.

Types

type NewPutTrainedModel

type NewPutTrainedModel func(modelid string) *PutTrainedModel

NewPutTrainedModel type alias for index.

func NewPutTrainedModelFunc

func NewPutTrainedModelFunc(tp elastictransport.Interface) NewPutTrainedModel

NewPutTrainedModelFunc returns a new instance of PutTrainedModel with the provided transport. Used in the index of the library this allows to retrieve every apis in once place.

type PutTrainedModel

type PutTrainedModel struct {
	// contains filtered or unexported fields
}

func New

func New(tp elastictransport.Interface) *PutTrainedModel

Creates an inference trained model.

https://www.elastic.co/guide/en/elasticsearch/reference/current/put-trained-models.html

func (*PutTrainedModel) DeferDefinitionDecompression

func (r *PutTrainedModel) DeferDefinitionDecompression(b bool) *PutTrainedModel

DeferDefinitionDecompression If set to `true` and a `compressed_definition` is provided, the request defers definition decompression and skips relevant validations. API name: defer_definition_decompression

func (PutTrainedModel) Do

func (r PutTrainedModel) Do(ctx context.Context) (*Response, error)

Do runs the request through the transport, handle the response and returns a puttrainedmodel.Response

func (*PutTrainedModel) Header

func (r *PutTrainedModel) Header(key, value string) *PutTrainedModel

Header set a key, value pair in the PutTrainedModel headers map.

func (*PutTrainedModel) HttpRequest

func (r *PutTrainedModel) HttpRequest(ctx context.Context) (*http.Request, error)

HttpRequest returns the http.Request object built from the given parameters.

func (*PutTrainedModel) ModelId

func (r *PutTrainedModel) ModelId(v string) *PutTrainedModel

ModelId The unique identifier of the trained model. API Name: modelid

func (PutTrainedModel) Perform

func (r PutTrainedModel) Perform(ctx context.Context) (*http.Response, error)

Perform runs the http.Request through the provided transport and returns an http.Response.

func (*PutTrainedModel) Raw

func (r *PutTrainedModel) Raw(raw io.Reader) *PutTrainedModel

Raw takes a json payload as input which is then passed to the http.Request If specified Raw takes precedence on Request method.

func (*PutTrainedModel) Request

func (r *PutTrainedModel) Request(req *Request) *PutTrainedModel

Request allows to set the request property with the appropriate payload.

type Request

type Request struct {

	// CompressedDefinition The compressed (GZipped and Base64 encoded) inference definition of the
	// model. If compressed_definition is specified, then definition cannot be
	// specified.
	CompressedDefinition *string `json:"compressed_definition,omitempty"`
	// Definition The inference definition for the model. If definition is specified, then
	// compressed_definition cannot be specified.
	Definition *types.Definition `json:"definition,omitempty"`
	// Description A human-readable description of the inference trained model.
	Description *string `json:"description,omitempty"`
	// InferenceConfig The default configuration for inference. This can be either a regression
	// or classification configuration. It must match the underlying
	// definition.trained_model's target_type.
	InferenceConfig types.InferenceConfigCreateContainer `json:"inference_config"`
	// Input The input field names for the model definition.
	Input *types.Input `json:"input,omitempty"`
	// Metadata An object map that contains metadata about the model.
	Metadata json.RawMessage `json:"metadata,omitempty"`
	// ModelSizeBytes The estimated memory usage in bytes to keep the trained model in memory.
	// This property is supported only if defer_definition_decompression is true
	// or the model definition is not supplied.
	ModelSizeBytes *int64 `json:"model_size_bytes,omitempty"`
	// ModelType The model type.
	ModelType *trainedmodeltype.TrainedModelType `json:"model_type,omitempty"`
	// Tags An array of tags to organize the model.
	Tags []string `json:"tags,omitempty"`
}

Request holds the request body struct for the package puttrainedmodel

https://github.com/elastic/elasticsearch-specification/blob/1ad7fe36297b3a8e187b2259dedaf68a47bc236e/specification/ml/put_trained_model/MlPutTrainedModelRequest.ts#L28-L94

func NewRequest

func NewRequest() *Request

NewRequest returns a Request

func (*Request) FromJSON

func (r *Request) FromJSON(data string) (*Request, error)

FromJSON allows to load an arbitrary json into the request structure

type Response

type Response struct {
	CompressedDefinition *string `json:"compressed_definition,omitempty"`
	// CreateTime The time when the trained model was created.
	CreateTime types.DateTime `json:"create_time,omitempty"`
	// CreatedBy Information on the creator of the trained model.
	CreatedBy *string `json:"created_by,omitempty"`
	// DefaultFieldMap Any field map described in the inference configuration takes precedence.
	DefaultFieldMap map[string]string `json:"default_field_map,omitempty"`
	// Description The free-text description of the trained model.
	Description *string `json:"description,omitempty"`
	// EstimatedHeapMemoryUsageBytes The estimated heap usage in bytes to keep the trained model in memory.
	EstimatedHeapMemoryUsageBytes *int `json:"estimated_heap_memory_usage_bytes,omitempty"`
	// EstimatedOperations The estimated number of operations to use the trained model.
	EstimatedOperations *int `json:"estimated_operations,omitempty"`
	// InferenceConfig The default configuration for inference. This can be either a regression,
	// classification, or one of the many NLP focused configurations. It must match
	// the underlying definition.trained_model's target_type.
	InferenceConfig types.InferenceConfigCreateContainer `json:"inference_config"`
	// Input The input field names for the model definition.
	Input types.TrainedModelConfigInput `json:"input"`
	// LicenseLevel The license level of the trained model.
	LicenseLevel *string                     `json:"license_level,omitempty"`
	Location     *types.TrainedModelLocation `json:"location,omitempty"`
	// Metadata An object containing metadata about the trained model. For example, models
	// created by data frame analytics contain analysis_config and input objects.
	Metadata *types.TrainedModelConfigMetadata `json:"metadata,omitempty"`
	// ModelId Identifier for the trained model.
	ModelId        string         `json:"model_id"`
	ModelSizeBytes types.ByteSize `json:"model_size_bytes,omitempty"`
	// ModelType The model type
	ModelType *trainedmodeltype.TrainedModelType `json:"model_type,omitempty"`
	// Tags A comma delimited string of tags. A trained model can have many tags, or
	// none.
	Tags []string `json:"tags"`
	// Version The Elasticsearch version number in which the trained model was created.
	Version *string `json:"version,omitempty"`
}

func NewResponse

func NewResponse() *Response

NewResponse returns a Response