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
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 or
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'display_name-s
(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:
all columns having
TIME_SERIES_AVAILABLE_PAST_ONLY
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'display_name-s
(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:
all columns having
TIME_SERIES_AVAILABLE_PAST_ONLY
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.
Protobuf type google.cloud.automl.v1beta1.BatchPredictInputConfig
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-11-19 UTC."],[],[]]
