Struct google_api_proto::google::cloud::automl::v1::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.v1.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:

AutoML Vision

Classification

One or more CSV files where each line is a single column:

 GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

 gs://folder/image1.jpeg
 gs://folder/image2.gif
 gs://folder/image3.png

Object Detection

One or more CSV files where each line is a single column:

 GCS_FILE_PATH

The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. This path is treated as the ID in the batch predict output.

Sample rows:

 gs://folder/image1.jpeg
 gs://folder/image2.gif
 gs://folder/image3.png

AutoML Video Intelligence

Classification

One or more CSV files where each line is a single column:

 GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time.

Sample rows:

 gs://folder/video1.mp4,10,40
 gs://folder/video1.mp4,20,60
 gs://folder/vid2.mov,0,inf

Object Tracking

One or more CSV files where each line is a single column:

 GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END

GCS_FILE_PATH is the Google Cloud Storage location of video up to 50GB in size and up to 3h in duration duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.

TIME_SEGMENT_START and TIME_SEGMENT_END must be within the length of the video, and the end time must be after the start time.

Sample rows:

 gs://folder/video1.mp4,10,40
 gs://folder/video1.mp4,20,60
 gs://folder/vid2.mov,0,inf

AutoML Natural Language

Classification

One or more CSV files where each line is a single column:

 GCS_FILE_PATH

GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 10MB in size.

Sample rows:

 gs://folder/text1.txt
 gs://folder/text2.pdf
 gs://folder/text3.tif

Sentiment Analysis

One or more CSV files where each line is a single column:

 GCS_FILE_PATH

GCS_FILE_PATH is the Google Cloud Storage location of a text file. Supported file extensions: .TXT, .PDF, .TIF, .TIFF

Text files can be no larger than 128kB in size.

Sample rows:

 gs://folder/text1.txt
 gs://folder/text2.pdf
 gs://folder/text3.tif

Entity Extraction

One or more JSONL (JSON Lines) files that either provide inline text or documents. You can only use one format, either inline text or documents, for a single call to [AutoMl.BatchPredict].

Each JSONL file contains a 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.

Each document JSONL file contains, per line, a proto that wraps a Document proto with input_config set. Each document cannot exceed 2MB in size.

Supported document extensions: .PDF, .TIF, .TIFF

Each JSONL file must not exceed 100MB in size, and no more than 20 JSONL files may be passed.

Sample inline JSONL file (Shown with artificial line breaks. Actual line breaks are 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": "Extended sample content",
      "mime_type": "text/plain"
    }
  }

Sample document JSONL file (Shown with artificial line breaks. Actual line breaks are denoted by “\n”.):

  {
    "document": {
      "input_config": {
        "gcs_source": { "input_uris": \[ "gs://folder/document1.pdf" \]
        }
      }
    }
  }\n
  {
    "document": {
      "input_config": {
        "gcs_source": { "input_uris": \[ "gs://folder/document2.tif" \]
        }
      }
    }
  }

AutoML Tables

See Preparing your training data for more information.

You can use either [gcs_source][google.cloud.automl.v1.BatchPredictInputConfig.gcs_source] or [bigquery_source][BatchPredictInputConfig.bigquery_source].

For gcs_source:

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.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.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.

Sample rows from 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","\[{"status":"current","address":"789_Any_Avenue","city":"Albany","state":"NY","zip":"33333","numberOfYears":"2"},{"status":"previous","address":"321_Main_Street","city":"Hoboken","state":"NJ","zip":"44444","numberOfYears":"3"}\]}

For bigquery_source:

The 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.v1.TablesModelMetadata.input_feature_column_specs] [display_name-s][google.cloud.automl.v1.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.

Input field definitions:

GCS_FILE_PATH : The path to a file on Google Cloud Storage. For example, “gs://folder/video.avi”.

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 n 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>

The source of the input.

Trait Implementations

impl Clone for BatchPredictInputConfig

fn clone(&self) -> BatchPredictInputConfig

Returns a copy of the value.

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() -> Self

Returns the “default value” for a type.

impl Message for BatchPredictInputConfig

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.

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

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

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

where Self: Sized,

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

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

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

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

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

where Self: Sized,

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

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

where Self: Sized,

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

impl PartialEq 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: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for BatchPredictInputConfig