Struct gapi_grpc::google::cloud::automl::v1beta1::BatchPredictInputConfig

pub struct BatchPredictInputConfig {
    pub source: Option<Source>,
}

Input configuration for BatchPredict Action.

The format of input depends on the ML problem of the model used for prediction. As input source the [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] is expected, unless specified otherwise.

The formats are represented in EBNF with commas being literal and with non-terminal symbols defined near the end of this comment. The formats are:

• For Image Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

• For Image Object Detection: CSV file(s) with each line having just a single column: GCS_FILE_PATH which leads to image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the Batch predict output. Three sample rows: gs://folder/image1.jpeg gs://folder/image2.gif gs://folder/image3.png

• For Video Classification: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,40 gs://folder/video1.mp4,20,60 gs://folder/vid2.mov,0,inf

• For Video Object Tracking: CSV file(s) with each line in format: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END GCS_FILE_PATH leads to video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and end has to be after the start. Three sample rows: gs://folder/video1.mp4,10,240 gs://folder/video1.mp4,300,360 gs://folder/vid2.mov,0,inf

• For Text Classification: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 60,000 characters or less. Three sample rows: gs://folder/text1.txt “Some text content to predict” gs://folder/text3.pdf Supported file extensions: .txt, .pdf

• For Text Sentiment: CSV file(s) with each line having just a single column: GCS_FILE_PATH | TEXT_SNIPPET Any given text file can have size upto 128kB. Any given text snippet content must have 500 characters or less. Three sample rows: gs://folder/text1.txt “Some text content to predict” gs://folder/text3.pdf Supported file extensions: .txt, .pdf

• For Text Extraction .JSONL (i.e. JSON Lines) file(s) which either provide text in-line or as documents (for a single BatchPredict call only one of the these formats may be used). The in-line .JSONL file(s) contain per line a proto that wraps a temporary user-assigned TextSnippet ID (string up to 2000 characters long) called “id”, a TextSnippet proto (in json representation) and zero or more TextFeature protos. Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded (ASCII already is). The IDs provided should be unique. The document .JSONL file(s) contain, per line, a proto that wraps a Document proto with input_config set. Only PDF documents are supported now, and each document must be up to 2MB large. Any given .JSONL file must be 100MB or smaller, and no more than 20 files may be given. Sample in-line JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n): { “id”: “my_first_id”, “text_snippet”: { “content”: “dog car cat”}, “text_features”: [ { “text_segment”: {“start_offset”: 4, “end_offset”: 6}, “structural_type”: PARAGRAPH, “bounding_poly”: { “normalized_vertices”: [ {“x”: 0.1, “y”: 0.1}, {“x”: 0.1, “y”: 0.3}, {“x”: 0.3, “y”: 0.3}, {“x”: 0.3, “y”: 0.1}, ] }, } ], }\n { “id”: “2”, “text_snippet”: { “content”: “An elaborate content”, “mime_type”: “text/plain” } } Sample document JSON Lines file (presented here with artificial line breaks, but the only actual line break is denoted by \n).: { “document”: { “input_config”: { “gcs_source”: { “input_uris”: [ “gs://folder/document1.pdf” ] } } } }\n { “document”: { “input_config”: { “gcs_source”: { “input_uris”: [ “gs://folder/document2.pdf” ] } } } }

• For Tables: Either [gcs_source][google.cloud.automl.v1beta1.InputConfig.gcs_source] or

[bigquery_source][google.cloud.automl.v1beta1.InputConfig.bigquery_source]. GCS case: CSV file(s), each by itself 10GB or smaller and total size must be 100GB or smaller, where first file must have a header containing column names. If the first row of a subsequent file is the same as the header, then it is also treated as a header. All other rows contain values for the corresponding columns. The column names must contain the model’s

[input_feature_column_specs’][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] (order doesn’t matter). The columns corresponding to the model’s input feature column specs must contain values compatible with the column spec’s data types. Prediction on all the rows, i.e. the CSV lines, will be attempted. For FORECASTING

[prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: all columns having

[TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] type will be ignored. First three sample rows of a CSV file: “First Name”,“Last Name”,“Dob”,“Addresses”

“John”,“Doe”,“1968-01-22”,“[{“status”:“current”,“address”:“123_First_Avenue”,“city”:“Seattle”,“state”:“WA”,“zip”:“11111”,“numberOfYears”:“1”},{“status”:“previous”,“address”:“456_Main_Street”,“city”:“Portland”,“state”:“OR”,“zip”:“22222”,“numberOfYears”:“5”}]“

“Jane”,“Doe”,“1980-10-16”,“[{“status”:“current”,“address”:“789_Any_Avenue”,“city”:“Albany”,“state”:“NY”,“zip”:“33333”,“numberOfYears”:“2”},{“status”:“previous”,“address”:“321_Main_Street”,“city”:“Hoboken”,“state”:“NJ”,“zip”:“44444”,“numberOfYears”:“3”}]} BigQuery case: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. The column names must contain the model’s

[input_feature_column_specs’][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]

[display_name-s][google.cloud.automl.v1beta1.ColumnSpec.display_name] (order doesn’t matter). The columns corresponding to the model’s input feature column specs must contain values compatible with the column spec’s data types. Prediction on all the rows of the table will be attempted. For FORECASTING

[prediction_type][google.cloud.automl.v1beta1.TablesModelMetadata.prediction_type]: all columns having

[TIME_SERIES_AVAILABLE_PAST_ONLY][google.cloud.automl.v1beta1.ColumnSpec.ForecastingMetadata.ColumnType] type will be ignored.

Definitions: GCS_FILE_PATH = A path to file on GCS, e.g. “gs://folder/video.avi”. TEXT_SNIPPET = A content of a text snippet, UTF-8 encoded, enclosed within double quotes (“”) TIME_SEGMENT_START = TIME_OFFSET Expresses a beginning, inclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_SEGMENT_END = TIME_OFFSET Expresses an end, exclusive, of a time segment within an example that has a time dimension (e.g. video). TIME_OFFSET = A number of seconds as measured from the start of an example (e.g. video). Fractions are allowed, up to a microsecond precision. “inf” is allowed and it means the end of the example.

Errors: If any of the provided CSV files can’t be parsed or if more than certain percent of CSV rows cannot be processed then the operation fails and prediction does not happen. Regardless of overall success or failure the per-row failures, up to a certain count cap, will be listed in Operation.metadata.partial_failures.

Fields

source: Option<Source>

Required. The source of the input.

Trait Implementations

impl Clone for BatchPredictInputConfig

fn clone(&self) -> BatchPredictInputConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for BatchPredictInputConfig

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

Formats the value using the given formatter.

impl Default for BatchPredictInputConfig

fn default() -> BatchPredictInputConfig

Returns the “default value” for a type.

impl Message for BatchPredictInputConfig

fn encode_raw<B>(&self, buf: &mut B) where
    B: BufMut, 

fn merge_field<B>(
    &mut self,
    tag: u32,
    wire_type: WireType,
    buf: &mut B,
    ctx: DecodeContext
) -> Result<(), DecodeError> where
    B: Buf, 

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

pub fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message to a buffer.

pub fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError> where
    B: BufMut, 

Encodes the message with a length-delimiter to a buffer.

pub fn decode<B>(buf: B) -> Result<Self, DecodeError> where
    Self: Default,
    B: Buf, 

Decodes an instance of the message from a buffer.

pub 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.

pub fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes an instance of the message from a buffer, and merges it into self.

pub fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError> where
    B: Buf, 

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq<BatchPredictInputConfig> for BatchPredictInputConfig

fn eq(&self, other: &BatchPredictInputConfig) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &BatchPredictInputConfig) -> bool

This method tests for !=.

impl StructuralPartialEq for BatchPredictInputConfig