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The interfaces provided are listed below, along with usage samples.
PredictionServiceClient
Service Description: AutoML Prediction API.
On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
Sample for PredictionServiceClient:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
ExamplePayload payload = ExamplePayload.newBuilder().build();
Map<String, String> params = new HashMap<>();
PredictResponse response = predictionServiceClient.predict(name, payload, params);
}
AutoMlClient
Service Description: AutoML Server API.
The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then the id for the item
is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
Sample for AutoMlClient:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
try (AutoMlClient autoMlClient = AutoMlClient.create()) {
DatasetName name = DatasetName.of("[PROJECT]", "[LOCATION]", "[DATASET]");
Dataset response = autoMlClient.getDataset(name);
}
Classes
AnnotationPayload
Contains annotation information that is relevant to AutoML.
Protobuf type google.cloud.automl.v1.AnnotationPayload
AnnotationPayload.Builder
Contains annotation information that is relevant to AutoML.
Protobuf type google.cloud.automl.v1.AnnotationPayload
AnnotationPayloadOuterClass
AnnotationSpec
A definition of an annotation spec.
Protobuf type google.cloud.automl.v1.AnnotationSpec
AnnotationSpec.Builder
A definition of an annotation spec.
Protobuf type google.cloud.automl.v1.AnnotationSpec
AnnotationSpecName
AnnotationSpecName.Builder
Builder for projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}.
AnnotationSpecOuterClass
AutoMlClient
Service Description: AutoML Server API.
The resource names are assigned by the server. The server never reuses names that it has created after the resources with those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then the id for the item
is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
try (AutoMlClient autoMlClient = AutoMlClient.create()) {
DatasetName name = DatasetName.of("[PROJECT]", "[LOCATION]", "[DATASET]");
Dataset response = autoMlClient.getDataset(name);
}
Note: close() needs to be called on the AutoMlClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().
The surface of this class includes several types of Java methods for each of the API's methods:
- A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
- A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
- A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.
See the individual methods for example code.
Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.
This class can be customized by passing in a custom instance of AutoMlSettings to create(). For example:
To customize credentials:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings autoMlSettings =
AutoMlSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);
To customize the endpoint:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings autoMlSettings = AutoMlSettings.newBuilder().setEndpoint(myEndpoint).build();
AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);
To use REST (HTTP1.1/JSON) transport (instead of gRPC) for sending and receiving requests over the wire:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings autoMlSettings =
AutoMlSettings.newBuilder()
.setTransportChannelProvider(
AutoMlSettings.defaultHttpJsonTransportProviderBuilder().build())
.build();
AutoMlClient autoMlClient = AutoMlClient.create(autoMlSettings);
Please refer to the GitHub repository's samples for more quickstart code snippets.
AutoMlClient.ListDatasetsFixedSizeCollection
AutoMlClient.ListDatasetsPage
AutoMlClient.ListDatasetsPagedResponse
AutoMlClient.ListModelEvaluationsFixedSizeCollection
AutoMlClient.ListModelEvaluationsPage
AutoMlClient.ListModelEvaluationsPagedResponse
AutoMlClient.ListModelsFixedSizeCollection
AutoMlClient.ListModelsPage
AutoMlClient.ListModelsPagedResponse
AutoMlGrpc
AutoML Server API.
The resource names are assigned by the server.
The server never reuses names that it has created after the resources with
those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then
the id for the item is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in
snake_case or dash-case, either of those cases is accepted.
AutoMlGrpc.AutoMlBlockingStub
AutoML Server API.
The resource names are assigned by the server.
The server never reuses names that it has created after the resources with
those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then
the id for the item is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in
snake_case or dash-case, either of those cases is accepted.
AutoMlGrpc.AutoMlFutureStub
AutoML Server API.
The resource names are assigned by the server.
The server never reuses names that it has created after the resources with
those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then
the id for the item is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in
snake_case or dash-case, either of those cases is accepted.
AutoMlGrpc.AutoMlImplBase
AutoML Server API.
The resource names are assigned by the server.
The server never reuses names that it has created after the resources with
those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then
the id for the item is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in
snake_case or dash-case, either of those cases is accepted.
AutoMlGrpc.AutoMlStub
AutoML Server API.
The resource names are assigned by the server.
The server never reuses names that it has created after the resources with
those names are deleted.
An ID of a resource is the last element of the item's resource name. For
projects/{project_id}/locations/{location_id}/datasets/{dataset_id}
, then
the id for the item is {dataset_id}
.
Currently the only supported location_id
is "us-central1".
On any input that is documented to expect a string parameter in
snake_case or dash-case, either of those cases is accepted.
AutoMlProto
AutoMlSettings
Settings class to configure an instance of AutoMlClient.
The default instance has everything set to sensible defaults:
- The default service address (automl.googleapis.com) and default port (443) are used.
- Credentials are acquired automatically through Application Default Credentials.
- Retries are configured for idempotent methods but not for non-idempotent methods.
The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.
For example, to set the total timeout of getDataset to 30 seconds:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
AutoMlSettings.Builder autoMlSettingsBuilder = AutoMlSettings.newBuilder();
autoMlSettingsBuilder
.getDatasetSettings()
.setRetrySettings(
autoMlSettingsBuilder.getDatasetSettings().getRetrySettings().toBuilder()
.setTotalTimeout(Duration.ofSeconds(30))
.build());
AutoMlSettings autoMlSettings = autoMlSettingsBuilder.build();
AutoMlSettings.Builder
Builder for AutoMlSettings.
BatchPredictInputConfig
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:
<h4>AutoML Vision</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
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
</section><section><h5>Object Detection</h5>
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
</section>
</div>
<h4>AutoML Video Intelligence</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
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
</section><section><h5>Object Tracking</h5>
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
</section>
</div>
<h4>AutoML Natural Language</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
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
</section><section><h5>Sentiment Analysis</h5>
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
</section><section><h5>Entity Extraction</h5>
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" ]
}
}
}
}
</section>
</div>
<h4>AutoML Tables</h4><div class="ui-datasection-main"><section
class="selected">
See Preparing your training
data for more
information.
You can use either
gcs_source
or
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'
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.
Sample rows from a CSV file:
<pre>
"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"}]}
</pre>
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'
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.
</section>
</div>
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.
Protobuf type google.cloud.automl.v1.BatchPredictInputConfig
BatchPredictInputConfig.Builder
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:
<h4>AutoML Vision</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
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
</section><section><h5>Object Detection</h5>
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
</section>
</div>
<h4>AutoML Video Intelligence</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
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
</section><section><h5>Object Tracking</h5>
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
</section>
</div>
<h4>AutoML Natural Language</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
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
</section><section><h5>Sentiment Analysis</h5>
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
</section><section><h5>Entity Extraction</h5>
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" ]
}
}
}
}
</section>
</div>
<h4>AutoML Tables</h4><div class="ui-datasection-main"><section
class="selected">
See Preparing your training
data for more
information.
You can use either
gcs_source
or
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'
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.
Sample rows from a CSV file:
<pre>
"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"}]}
</pre>
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'
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.
</section>
</div>
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.
Protobuf type google.cloud.automl.v1.BatchPredictInputConfig
BatchPredictOperationMetadata
Details of BatchPredict operation.
Protobuf type google.cloud.automl.v1.BatchPredictOperationMetadata
BatchPredictOperationMetadata.BatchPredictOutputInfo
Further describes this batch predict's output. Supplements BatchPredictOutputConfig.
Protobuf type
google.cloud.automl.v1.BatchPredictOperationMetadata.BatchPredictOutputInfo
BatchPredictOperationMetadata.BatchPredictOutputInfo.Builder
Further describes this batch predict's output. Supplements BatchPredictOutputConfig.
Protobuf type
google.cloud.automl.v1.BatchPredictOperationMetadata.BatchPredictOutputInfo
BatchPredictOperationMetadata.Builder
Details of BatchPredict operation.
Protobuf type google.cloud.automl.v1.BatchPredictOperationMetadata
BatchPredictOutputConfig
Output configuration for BatchPredict Action. As destination the gcs_destination must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.
- For Image Classification:
In the created directory files
image_classification_1.jsonl
,image_classification_2.jsonl
,...,image_classification_N.jsonl
will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
fields. - For Image Object Detection:
In the created directory files
image_object_detection_1.jsonl
,image_object_detection_2.jsonl
,...,image_object_detection_N.jsonl
will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
fields. - For Video Classification:
In the created directory a video_classification.csv file, and a .JSON
file per each video classification requested in the input (i.e. each
line in given CSV(s)), will be created.
The format of video_classification.csv is:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
where:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
the prediction input lines (i.e. video_classification.csv has
precisely the same number of lines as the prediction input had.)
JSON_FILE_NAME = Name of .JSON file in the output directory, which
contains prediction responses for the video time segment.
STATUS = "OK" if prediction completed successfully, or an error code
with message otherwise. If STATUS is not "OK" then the .JSON file
for that line may not exist or be empty.
Each .JSON file, assuming STATUS is "OK", will contain a list of
AnnotationPayload protos in JSON format, which are the predictions
for the video time segment the file is assigned to in the
video_classification.csv. All AnnotationPayload protos will have
video_classification field set, and will be sorted by
video_classification.type field (note that the returned types are
governed by
classifaction_types
parameter in [PredictService.BatchPredictRequest.params][]). - For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set.
- For Text Classification:
In the created directory files
text_classification_1.jsonl
,text_classification_2.jsonl
,...,text_classification_N.jsonl
will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
. - For Text Sentiment:
In the created directory files
text_sentiment_1.jsonl
,text_sentiment_2.jsonl
,...,text_sentiment_N.jsonl
will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
.- For Text Extraction:
In the created directory files
text_extraction_1.jsonl
,text_extraction_2.jsonl
,...,text_extraction_N.jsonl
will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "<id_value>" (in case of inline) or the document proto (in case of document) but here followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
.
- For Text Extraction:
In the created directory files
- For Tables:
Output depends on whether
gcs_destination
or
bigquery_destination
is set (either is allowed).
Google Cloud Storage case:
In the created directory files
tables_1.csv
,tables_2.csv
,...,tables_N.csv
will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "<target_column_specs display_name><target value>_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction scores. For REGRESSION and FORECASTING prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted<target_column_specs display_name>" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additionalerrors_1.csv
,errors_2.csv
,...,errors_N.csv
will be created (N depends on total number of failed rows). These files will have analogous format astables_*.csv
, but always with a single target column havinggoogle.rpc.Status
represented as a JSON string, and containing onlycode
andmessage
. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with nameprediction_<model-display-name><timestamp-of-prediction-call>
where <model-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created,predictions
, anderrors
. Thepredictions
table's column names will be the input columns' display_name-s followed by the target column with name in the format of "predicted<target_column_specs display_name>" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation. Theerrors
table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_<target_column_specs display_name>", and as a value hasgoogle.rpc.Status
represented as a STRUCT, and containing onlycode
andmessage
.
Protobuf type google.cloud.automl.v1.BatchPredictOutputConfig
BatchPredictOutputConfig.Builder
Output configuration for BatchPredict Action. As destination the gcs_destination must be set unless specified otherwise for a domain. If gcs_destination is set then in the given directory a new directory is created. Its name will be "prediction-<model-display-name>-<timestamp-of-prediction-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. The contents of it depends on the ML problem the predictions are made for.
- For Image Classification:
In the created directory files
image_classification_1.jsonl
,image_classification_2.jsonl
,...,image_classification_N.jsonl
will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. A single image will be listed only once with all its annotations, and its annotations will never be split across files. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. If prediction for any image failed (partially or completely), then an additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
fields. - For Image Object Detection:
In the created directory files
image_object_detection_1.jsonl
,image_object_detection_2.jsonl
,...,image_object_detection_N.jsonl
will be created, where N may be 1, and depends on the total number of the successfully predicted images and annotations. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps image's "ID" : "<id_value>" followed by a list of zero or more AnnotationPayload protos (called annotations), which have image_object_detection detail populated. A single image will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any image failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps the same "ID" : "<id_value>" but here followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
fields. - For Video Classification:
In the created directory a video_classification.csv file, and a .JSON
file per each video classification requested in the input (i.e. each
line in given CSV(s)), will be created.
The format of video_classification.csv is:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS
where:
GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1
the prediction input lines (i.e. video_classification.csv has
precisely the same number of lines as the prediction input had.)
JSON_FILE_NAME = Name of .JSON file in the output directory, which
contains prediction responses for the video time segment.
STATUS = "OK" if prediction completed successfully, or an error code
with message otherwise. If STATUS is not "OK" then the .JSON file
for that line may not exist or be empty.
Each .JSON file, assuming STATUS is "OK", will contain a list of
AnnotationPayload protos in JSON format, which are the predictions
for the video time segment the file is assigned to in the
video_classification.csv. All AnnotationPayload protos will have
video_classification field set, and will be sorted by
video_classification.type field (note that the returned types are
governed by
classifaction_types
parameter in [PredictService.BatchPredictRequest.params][]). - For Video Object Tracking: In the created directory a video_object_tracking.csv file will be created, and multiple files video_object_trackinng_1.json, video_object_trackinng_2.json,..., video_object_trackinng_N.json, where N is the number of requests in the input (i.e. the number of lines in given CSV(s)). The format of video_object_tracking.csv is: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END,JSON_FILE_NAME,STATUS where: GCS_FILE_PATH,TIME_SEGMENT_START,TIME_SEGMENT_END = matches 1 to 1 the prediction input lines (i.e. video_object_tracking.csv has precisely the same number of lines as the prediction input had.) JSON_FILE_NAME = Name of .JSON file in the output directory, which contains prediction responses for the video time segment. STATUS = "OK" if prediction completed successfully, or an error code with message otherwise. If STATUS is not "OK" then the .JSON file for that line may not exist or be empty. Each .JSON file, assuming STATUS is "OK", will contain a list of AnnotationPayload protos in JSON format, which are the predictions for each frame of the video time segment the file is assigned to in video_object_tracking.csv. All AnnotationPayload protos will have video_object_tracking field set.
- For Text Classification:
In the created directory files
text_classification_1.jsonl
,text_classification_2.jsonl
,...,text_classification_N.jsonl
will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have classification detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
. - For Text Sentiment:
In the created directory files
text_sentiment_1.jsonl
,text_sentiment_2.jsonl
,...,text_sentiment_N.jsonl
will be created, where N may be 1, and depends on the total number of inputs and annotations found. Each .JSONL file will contain, per line, a JSON representation of a proto that wraps input text file (or document) in the text snippet (or document) proto and a list of zero or more AnnotationPayload protos (called annotations), which have text_sentiment detail populated. A single text file (or document) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any input file (or document) failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps input file followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
.- For Text Extraction:
In the created directory files
text_extraction_1.jsonl
,text_extraction_2.jsonl
,...,text_extraction_N.jsonl
will be created, where N may be 1, and depends on the total number of inputs and annotations found. The contents of these .JSONL file(s) depend on whether the input used inline text, or documents. If input was inline, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request text snippet's "id" (if specified), followed by input text snippet, and a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated. A single text snippet will be listed only once with all its annotations, and its annotations will never be split across files. If input used documents, then each .JSONL file will contain, per line, a JSON representation of a proto that wraps given in request document proto, followed by its OCR-ed representation in the form of a text snippet, finally followed by a list of zero or more AnnotationPayload protos (called annotations), which have text_extraction detail populated and refer, via their indices, to the OCR-ed text snippet. A single document (and its text snippet) will be listed only once with all its annotations, and its annotations will never be split across files. If prediction for any text snippet failed (partially or completely), then additionalerrors_1.jsonl
,errors_2.jsonl
,...,errors_N.jsonl
files will be created (N depends on total number of failed predictions). These files will have a JSON representation of a proto that wraps either the "id" : "<id_value>" (in case of inline) or the document proto (in case of document) but here followed by exactly onegoogle.rpc.Status
containing onlycode
andmessage
.
- For Text Extraction:
In the created directory files
- For Tables:
Output depends on whether
gcs_destination
or
bigquery_destination
is set (either is allowed).
Google Cloud Storage case:
In the created directory files
tables_1.csv
,tables_2.csv
,...,tables_N.csv
will be created, where N may be 1, and depends on the total number of the successfully predicted rows. For all CLASSIFICATION prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by M target column names in the format of "<target_column_specs display_name><target value>_score" where M is the number of distinct target values, i.e. number of distinct values in the target column of the table used to train the model. Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, columns having the corresponding prediction scores. For REGRESSION and FORECASTING prediction_type-s: Each .csv file will contain a header, listing all columns' display_name-s given on input followed by the predicted target column with name in the format of "predicted<target_column_specs display_name>" Subsequent lines will contain the respective values of successfully predicted rows, with the last, i.e. the target, column having the predicted target value. If prediction for any rows failed, then an additionalerrors_1.csv
,errors_2.csv
,...,errors_N.csv
will be created (N depends on total number of failed rows). These files will have analogous format astables_*.csv
, but always with a single target column havinggoogle.rpc.Status
represented as a JSON string, and containing onlycode
andmessage
. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with nameprediction_<model-display-name><timestamp-of-prediction-call>
where <model-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created,predictions
, anderrors
. Thepredictions
table's column names will be the input columns' display_name-s followed by the target column with name in the format of "predicted<target_column_specs display_name>" The input feature columns will contain the respective values of successfully predicted rows, with the target column having an ARRAY of AnnotationPayloads, represented as STRUCT-s, containing TablesAnnotation. Theerrors
table contains rows for which the prediction has failed, it has analogous input columns while the target column name is in the format of "errors_<target_column_specs display_name>", and as a value hasgoogle.rpc.Status
represented as a STRUCT, and containing onlycode
andmessage
.
Protobuf type google.cloud.automl.v1.BatchPredictOutputConfig
BatchPredictRequest
Request message for PredictionService.BatchPredict.
Protobuf type google.cloud.automl.v1.BatchPredictRequest
BatchPredictRequest.Builder
Request message for PredictionService.BatchPredict.
Protobuf type google.cloud.automl.v1.BatchPredictRequest
BatchPredictResult
Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.
Protobuf type google.cloud.automl.v1.BatchPredictResult
BatchPredictResult.Builder
Result of the Batch Predict. This message is returned in response of the operation returned by the PredictionService.BatchPredict.
Protobuf type google.cloud.automl.v1.BatchPredictResult
BoundingBoxMetricsEntry
Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry
BoundingBoxMetricsEntry.Builder
Bounding box matching model metrics for a single intersection-over-union threshold and multiple label match confidence thresholds.
Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry
BoundingBoxMetricsEntry.ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry.ConfidenceMetricsEntry
BoundingBoxMetricsEntry.ConfidenceMetricsEntry.Builder
Metrics for a single confidence threshold.
Protobuf type google.cloud.automl.v1.BoundingBoxMetricsEntry.ConfidenceMetricsEntry
BoundingPoly
A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.
Protobuf type google.cloud.automl.v1.BoundingPoly
BoundingPoly.Builder
A bounding polygon of a detected object on a plane. On output both vertices and normalized_vertices are provided. The polygon is formed by connecting vertices in the order they are listed.
Protobuf type google.cloud.automl.v1.BoundingPoly
ClassificationAnnotation
Contains annotation details specific to classification.
Protobuf type google.cloud.automl.v1.ClassificationAnnotation
ClassificationAnnotation.Builder
Contains annotation details specific to classification.
Protobuf type google.cloud.automl.v1.ClassificationAnnotation
ClassificationEvaluationMetrics
Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.
Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics
ClassificationEvaluationMetrics.Builder
Model evaluation metrics for classification problems. Note: For Video Classification this metrics only describe quality of the Video Classification predictions of "segment_classification" type.
Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics
ClassificationEvaluationMetrics.ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
ClassificationEvaluationMetrics.ConfidenceMetricsEntry.Builder
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry
ClassificationEvaluationMetrics.ConfusionMatrix
Confusion matrix of the model running the classification.
Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix
ClassificationEvaluationMetrics.ConfusionMatrix.Builder
Confusion matrix of the model running the classification.
Protobuf type google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix
ClassificationEvaluationMetrics.ConfusionMatrix.Row
Output only. A row in the confusion matrix.
Protobuf type
google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row
ClassificationEvaluationMetrics.ConfusionMatrix.Row.Builder
Output only. A row in the confusion matrix.
Protobuf type
google.cloud.automl.v1.ClassificationEvaluationMetrics.ConfusionMatrix.Row
ClassificationProto
CreateDatasetOperationMetadata
Details of CreateDataset operation.
Protobuf type google.cloud.automl.v1.CreateDatasetOperationMetadata
CreateDatasetOperationMetadata.Builder
Details of CreateDataset operation.
Protobuf type google.cloud.automl.v1.CreateDatasetOperationMetadata
CreateDatasetRequest
Request message for AutoMl.CreateDataset.
Protobuf type google.cloud.automl.v1.CreateDatasetRequest
CreateDatasetRequest.Builder
Request message for AutoMl.CreateDataset.
Protobuf type google.cloud.automl.v1.CreateDatasetRequest
CreateModelOperationMetadata
Details of CreateModel operation.
Protobuf type google.cloud.automl.v1.CreateModelOperationMetadata
CreateModelOperationMetadata.Builder
Details of CreateModel operation.
Protobuf type google.cloud.automl.v1.CreateModelOperationMetadata
CreateModelRequest
Request message for AutoMl.CreateModel.
Protobuf type google.cloud.automl.v1.CreateModelRequest
CreateModelRequest.Builder
Request message for AutoMl.CreateModel.
Protobuf type google.cloud.automl.v1.CreateModelRequest
DataItems
Dataset
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
Protobuf type google.cloud.automl.v1.Dataset
Dataset.Builder
A workspace for solving a single, particular machine learning (ML) problem. A workspace contains examples that may be annotated.
Protobuf type google.cloud.automl.v1.Dataset
DatasetName
DatasetName.Builder
Builder for projects/{project}/locations/{location}/datasets/{dataset}.
DatasetOuterClass
DeleteDatasetRequest
Request message for AutoMl.DeleteDataset.
Protobuf type google.cloud.automl.v1.DeleteDatasetRequest
DeleteDatasetRequest.Builder
Request message for AutoMl.DeleteDataset.
Protobuf type google.cloud.automl.v1.DeleteDatasetRequest
DeleteModelRequest
Request message for AutoMl.DeleteModel.
Protobuf type google.cloud.automl.v1.DeleteModelRequest
DeleteModelRequest.Builder
Request message for AutoMl.DeleteModel.
Protobuf type google.cloud.automl.v1.DeleteModelRequest
DeleteOperationMetadata
Details of operations that perform deletes of any entities.
Protobuf type google.cloud.automl.v1.DeleteOperationMetadata
DeleteOperationMetadata.Builder
Details of operations that perform deletes of any entities.
Protobuf type google.cloud.automl.v1.DeleteOperationMetadata
DeployModelOperationMetadata
Details of DeployModel operation.
Protobuf type google.cloud.automl.v1.DeployModelOperationMetadata
DeployModelOperationMetadata.Builder
Details of DeployModel operation.
Protobuf type google.cloud.automl.v1.DeployModelOperationMetadata
DeployModelRequest
Request message for AutoMl.DeployModel.
Protobuf type google.cloud.automl.v1.DeployModelRequest
DeployModelRequest.Builder
Request message for AutoMl.DeployModel.
Protobuf type google.cloud.automl.v1.DeployModelRequest
Detection
Document
A structured text document e.g. a PDF.
Protobuf type google.cloud.automl.v1.Document
Document.Builder
A structured text document e.g. a PDF.
Protobuf type google.cloud.automl.v1.Document
Document.Layout
Describes the layout information of a text_segment in the document.
Protobuf type google.cloud.automl.v1.Document.Layout
Document.Layout.Builder
Describes the layout information of a text_segment in the document.
Protobuf type google.cloud.automl.v1.Document.Layout
DocumentDimensions
Message that describes dimension of a document.
Protobuf type google.cloud.automl.v1.DocumentDimensions
DocumentDimensions.Builder
Message that describes dimension of a document.
Protobuf type google.cloud.automl.v1.DocumentDimensions
DocumentInputConfig
Input configuration of a Document.
Protobuf type google.cloud.automl.v1.DocumentInputConfig
DocumentInputConfig.Builder
Input configuration of a Document.
Protobuf type google.cloud.automl.v1.DocumentInputConfig
ExamplePayload
Example data used for training or prediction.
Protobuf type google.cloud.automl.v1.ExamplePayload
ExamplePayload.Builder
Example data used for training or prediction.
Protobuf type google.cloud.automl.v1.ExamplePayload
ExportDataOperationMetadata
Details of ExportData operation.
Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata
ExportDataOperationMetadata.Builder
Details of ExportData operation.
Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata
ExportDataOperationMetadata.ExportDataOutputInfo
Further describes this export data's output. Supplements OutputConfig.
Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata.ExportDataOutputInfo
ExportDataOperationMetadata.ExportDataOutputInfo.Builder
Further describes this export data's output. Supplements OutputConfig.
Protobuf type google.cloud.automl.v1.ExportDataOperationMetadata.ExportDataOutputInfo
ExportDataRequest
Request message for AutoMl.ExportData.
Protobuf type google.cloud.automl.v1.ExportDataRequest
ExportDataRequest.Builder
Request message for AutoMl.ExportData.
Protobuf type google.cloud.automl.v1.ExportDataRequest
ExportModelOperationMetadata
Details of ExportModel operation.
Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata
ExportModelOperationMetadata.Builder
Details of ExportModel operation.
Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata
ExportModelOperationMetadata.ExportModelOutputInfo
Further describes the output of model export. Supplements ModelExportOutputConfig.
Protobuf type google.cloud.automl.v1.ExportModelOperationMetadata.ExportModelOutputInfo
ExportModelOperationMetadata.ExportModelOutputInfo.Builder
Further describes the output of model export. Supplements ModelExportOutputConfig.
Protobuf type
google.cloud.automl.v1.ExportModelOperationMetadata.ExportModelOutputInfo
ExportModelRequest
Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.
Protobuf type google.cloud.automl.v1.ExportModelRequest
ExportModelRequest.Builder
Request message for AutoMl.ExportModel. Models need to be enabled for exporting, otherwise an error code will be returned.
Protobuf type google.cloud.automl.v1.ExportModelRequest
GcsDestination
The Google Cloud Storage location where the output is to be written to.
Protobuf type google.cloud.automl.v1.GcsDestination
GcsDestination.Builder
The Google Cloud Storage location where the output is to be written to.
Protobuf type google.cloud.automl.v1.GcsDestination
GcsSource
The Google Cloud Storage location for the input content.
Protobuf type google.cloud.automl.v1.GcsSource
GcsSource.Builder
The Google Cloud Storage location for the input content.
Protobuf type google.cloud.automl.v1.GcsSource
Geometry
GetAnnotationSpecRequest
Request message for AutoMl.GetAnnotationSpec.
Protobuf type google.cloud.automl.v1.GetAnnotationSpecRequest
GetAnnotationSpecRequest.Builder
Request message for AutoMl.GetAnnotationSpec.
Protobuf type google.cloud.automl.v1.GetAnnotationSpecRequest
GetDatasetRequest
Request message for AutoMl.GetDataset.
Protobuf type google.cloud.automl.v1.GetDatasetRequest
GetDatasetRequest.Builder
Request message for AutoMl.GetDataset.
Protobuf type google.cloud.automl.v1.GetDatasetRequest
GetModelEvaluationRequest
Request message for AutoMl.GetModelEvaluation.
Protobuf type google.cloud.automl.v1.GetModelEvaluationRequest
GetModelEvaluationRequest.Builder
Request message for AutoMl.GetModelEvaluation.
Protobuf type google.cloud.automl.v1.GetModelEvaluationRequest
GetModelRequest
Request message for AutoMl.GetModel.
Protobuf type google.cloud.automl.v1.GetModelRequest
GetModelRequest.Builder
Request message for AutoMl.GetModel.
Protobuf type google.cloud.automl.v1.GetModelRequest
Image
A representation of an image. Only images up to 30MB in size are supported.
Protobuf type google.cloud.automl.v1.Image
Image.Builder
A representation of an image. Only images up to 30MB in size are supported.
Protobuf type google.cloud.automl.v1.Image
ImageClassificationDatasetMetadata
Dataset metadata that is specific to image classification.
Protobuf type google.cloud.automl.v1.ImageClassificationDatasetMetadata
ImageClassificationDatasetMetadata.Builder
Dataset metadata that is specific to image classification.
Protobuf type google.cloud.automl.v1.ImageClassificationDatasetMetadata
ImageClassificationModelDeploymentMetadata
Model deployment metadata specific to Image Classification.
Protobuf type google.cloud.automl.v1.ImageClassificationModelDeploymentMetadata
ImageClassificationModelDeploymentMetadata.Builder
Model deployment metadata specific to Image Classification.
Protobuf type google.cloud.automl.v1.ImageClassificationModelDeploymentMetadata
ImageClassificationModelMetadata
Model metadata for image classification.
Protobuf type google.cloud.automl.v1.ImageClassificationModelMetadata
ImageClassificationModelMetadata.Builder
Model metadata for image classification.
Protobuf type google.cloud.automl.v1.ImageClassificationModelMetadata
ImageObjectDetectionAnnotation
Annotation details for image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionAnnotation
ImageObjectDetectionAnnotation.Builder
Annotation details for image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionAnnotation
ImageObjectDetectionDatasetMetadata
Dataset metadata specific to image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionDatasetMetadata
ImageObjectDetectionDatasetMetadata.Builder
Dataset metadata specific to image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionDatasetMetadata
ImageObjectDetectionEvaluationMetrics
Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionEvaluationMetrics
ImageObjectDetectionEvaluationMetrics.Builder
Model evaluation metrics for image object detection problems. Evaluates prediction quality of labeled bounding boxes.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionEvaluationMetrics
ImageObjectDetectionModelDeploymentMetadata
Model deployment metadata specific to Image Object Detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata
ImageObjectDetectionModelDeploymentMetadata.Builder
Model deployment metadata specific to Image Object Detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata
ImageObjectDetectionModelMetadata
Model metadata specific to image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelMetadata
ImageObjectDetectionModelMetadata.Builder
Model metadata specific to image object detection.
Protobuf type google.cloud.automl.v1.ImageObjectDetectionModelMetadata
ImageProto
ImportDataOperationMetadata
Details of ImportData operation.
Protobuf type google.cloud.automl.v1.ImportDataOperationMetadata
ImportDataOperationMetadata.Builder
Details of ImportData operation.
Protobuf type google.cloud.automl.v1.ImportDataOperationMetadata
ImportDataRequest
Request message for AutoMl.ImportData.
Protobuf type google.cloud.automl.v1.ImportDataRequest
ImportDataRequest.Builder
Request message for AutoMl.ImportData.
Protobuf type google.cloud.automl.v1.ImportDataRequest
InputConfig
Input configuration for AutoMl.ImportData action.
The format of input depends on dataset_metadata the Dataset into which
the import is happening has. As input source the
gcs_source
is expected, unless specified otherwise. Additionally any input .CSV file
by itself must be 100MB or smaller, unless specified otherwise.
If an "example" file (that is, image, video etc.) with identical content
(even if it had different GCS_FILE_PATH
) is mentioned multiple times, then
its label, bounding boxes etc. are appended. The same file should be always
provided with the same ML_USE
and GCS_FILE_PATH
, if it is not, then
these values are nondeterministically selected from the given ones.
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:
<h4>AutoML Vision</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
See Preparing your training
data for more
information.
CSV file(s) with each line in format:
ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH
- The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO.LABEL
- A label that identifies the object in the image. For theMULTICLASS
classification type, at most oneLABEL
is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with noLABEL
. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg </section><section><h5>Object Detection</h5> See Preparing your training data for more information. A CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH
- The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled.LABEL
- A label that identifies the object in the image specified by theBOUNDING_BOX
.BOUNDING BOX
- The vertices of an object in the example image. The minimum allowedBOUNDING_BOX
edge length is 0.01, and no more than 500BOUNDING_BOX
instances per image are allowed (oneBOUNDING_BOX
per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the ",,,,,,," in place of theBOUNDING_BOX
. Four sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH ForML_USE
, do not useVALIDATE
.GCS_FILE_PATH
is the path to another .csv file that describes training example for a givenML_USE
, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) HereGCS_FILE_PATH
leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.TIME_SEGMENT_START
andTIME_SEGMENT_END
must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place ofLABEL, TIME_SEGMENT_START,TIME_SEGMENT_END
. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,, </section><section><h5>Object Tracking</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH ForML_USE
, do not useVALIDATE
.GCS_FILE_PATH
is the path to another .csv file that describes training example for a givenML_USE
, using the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, HereGCS_FILE_PATH
leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. ProvidingINSTANCE_ID
s can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the sameINSTANCE_ID
is given to it.TIMESTAMP
must be within the length of the video, theBOUNDING_BOX
is assumed to be drawn on the closest video's frame to theTIMESTAMP
. Any mentioned by theTIMESTAMP
frame is expected to be exhaustively labeled and no more than 500BOUNDING_BOX
-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place ofLABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,, </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,GCS_FILE_PATHML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing..
GCS_FILE_PATH
- a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training. After the training data set has been determined from theTRAIN
andUNASSIGNED
CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation. For example: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl In-line JSONL files In-line .JSONL files contain, per line, a JSON document that wraps atext_snippet
field followed by one or moreannotations
fields, which havedisplay_name
andtext_extraction
fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n). The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal". Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 7} } } ] } JSONL files that reference documents .JSONL files contain, per line, a JSON document that wraps ainput_config
that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n). Supported document extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } In-line JSONL files with document layout information Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI orexportData
. In-line .JSONL files for documents contain, per line, a JSON document that wraps adocument
field that provides the textual content of the document and the layout information. For example: { "document": { "document_text": { "content": "dog car cat" } "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, "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}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 11} } }, ], </section><section><h5>Classification</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET
andGCS_FILE_PATH
are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as aGCS_FILE_PATH
. Otherwise, if the content is enclosed in double quotes (""), it is treated as aTEXT_SNIPPET
. ForGCS_FILE_PATH
, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. ForTEXT_SNIPPET
, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size. For theMULTICLASS
classification type, at most oneLABEL
is allowed. TheML_USE
andLABEL
columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood gs://folder/content.txt,SlowService TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood </section><section><h5>Sentiment Analysis</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENTML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET
andGCS_FILE_PATH
are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as aGCS_FILE_PATH
. Otherwise, if the content is enclosed in double quotes (""), it is treated as aTEXT_SNIPPET
. ForGCS_FILE_PATH
, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. ForTEXT_SNIPPET
, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size. TheML_USE
andSENTIMENT
columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIPSENTIMENT
- An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 gs://folder/content.txt,3 TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,2 </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See Preparing your training data for more information. You can use either gcs_source or bigquery_source. All input is concatenated into a single primary_table_spec_id For gcs_source: CSV file(s), where the first row of the first file is the header, containing unique 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. Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller. First three sample rows of a CSV file: <pre> "Id","First Name","Last Name","Dob","Addresses" "1","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"}]" "2","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"}]} </pre> For bigquery_source: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel. </section> </div> Input field definitions:ML_USE
: ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED") Describes how the given example (file) should be used for model training. "UNASSIGNED" can be used when user has no preference.GCS_FILE_PATH
: The path to a file on Google Cloud Storage. For example, "gs://folder/image1.png".LABEL
: A display name of an object on an image, video etc., e.g. "dog". Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions.INSTANCE_ID
: A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which.BOUNDING_BOX
: (VERTEX,VERTEX,VERTEX,VERTEX
|VERTEX,,,VERTEX,,
) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle.VERTEX
: (COORDINATE,COORDINATE
) First coordinate is horizontal (x), the second is vertical (y).COORDINATE
: A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left.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.TEXT_SNIPPET
: The content of a text snippet, UTF-8 encoded, enclosed within double quotes ("").DOCUMENT
: A field that provides the textual content with document and the layout information. 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 nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.
Protobuf type google.cloud.automl.v1.InputConfig
InputConfig.Builder
Input configuration for AutoMl.ImportData action.
The format of input depends on dataset_metadata the Dataset into which
the import is happening has. As input source the
gcs_source
is expected, unless specified otherwise. Additionally any input .CSV file
by itself must be 100MB or smaller, unless specified otherwise.
If an "example" file (that is, image, video etc.) with identical content
(even if it had different GCS_FILE_PATH
) is mentioned multiple times, then
its label, bounding boxes etc. are appended. The same file should be always
provided with the same ML_USE
and GCS_FILE_PATH
, if it is not, then
these values are nondeterministically selected from the given ones.
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:
<h4>AutoML Vision</h4>
<div class="ds-selector-tabs"><section><h5>Classification</h5>
See Preparing your training
data for more
information.
CSV file(s) with each line in format:
ML_USE,GCS_FILE_PATH,LABEL,LABEL,...
ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH
- The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG, .WEBP, .BMP, .TIFF, .ICO.LABEL
- A label that identifies the object in the image. For theMULTICLASS
classification type, at most oneLABEL
is allowed per image. If an image has not yet been labeled, then it should be mentioned just once with noLABEL
. Some sample rows: TRAIN,gs://folder/image1.jpg,daisy TEST,gs://folder/image2.jpg,dandelion,tulip,rose UNASSIGNED,gs://folder/image3.jpg,daisy UNASSIGNED,gs://folder/image4.jpg </section><section><h5>Object Detection</h5> See Preparing your training data for more information. A CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH,[LABEL],(BOUNDING_BOX | ,,,,,,,)ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
GCS_FILE_PATH
- The Google Cloud Storage location of an image of up to 30MB in size. Supported extensions: .JPEG, .GIF, .PNG. Each image is assumed to be exhaustively labeled.LABEL
- A label that identifies the object in the image specified by theBOUNDING_BOX
.BOUNDING BOX
- The vertices of an object in the example image. The minimum allowedBOUNDING_BOX
edge length is 0.01, and no more than 500BOUNDING_BOX
instances per image are allowed (oneBOUNDING_BOX
per line). If an image has no looked for objects then it should be mentioned just once with no LABEL and the ",,,,,,," in place of theBOUNDING_BOX
. Four sample rows: TRAIN,gs://folder/image1.png,car,0.1,0.1,,,0.3,0.3,, TRAIN,gs://folder/image1.png,bike,.7,.6,,,.8,.9,, UNASSIGNED,gs://folder/im2.png,car,0.1,0.1,0.2,0.1,0.2,0.3,0.1,0.3 TEST,gs://folder/im3.png,,,,,,,,, </section> </div> <h4>AutoML Video Intelligence</h4> <div class="ds-selector-tabs"><section><h5>Classification</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH ForML_USE
, do not useVALIDATE
.GCS_FILE_PATH
is the path to another .csv file that describes training example for a givenML_USE
, using the following row format: GCS_FILE_PATH,(LABEL,TIME_SEGMENT_START,TIME_SEGMENT_END | ,,) HereGCS_FILE_PATH
leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI.TIME_SEGMENT_START
andTIME_SEGMENT_END
must be within the length of the video, and the end time must be after the start time. Any segment of a video which has one or more labels on it, is considered a hard negative for all other labels. Any segment with no labels on it is considered to be unknown. If a whole video is unknown, then it should be mentioned just once with ",," in place ofLABEL, TIME_SEGMENT_START,TIME_SEGMENT_END
. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,120,180.000021 gs://folder/video1.avi,bike,150,180.000021 gs://folder/vid2.avi,car,0,60.5 gs://folder/vid3.avi,,, </section><section><h5>Object Tracking</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,GCS_FILE_PATH ForML_USE
, do not useVALIDATE
.GCS_FILE_PATH
is the path to another .csv file that describes training example for a givenML_USE
, using the following row format: GCS_FILE_PATH,LABEL,[INSTANCE_ID],TIMESTAMP,BOUNDING_BOX or GCS_FILE_PATH,,,,,,,,,, HereGCS_FILE_PATH
leads to a video of up to 50GB in size and up to 3h duration. Supported extensions: .MOV, .MPEG4, .MP4, .AVI. ProvidingINSTANCE_ID
s can help to obtain a better model. When a specific labeled entity leaves the video frame, and shows up afterwards it is not required, albeit preferable, that the sameINSTANCE_ID
is given to it.TIMESTAMP
must be within the length of the video, theBOUNDING_BOX
is assumed to be drawn on the closest video's frame to theTIMESTAMP
. Any mentioned by theTIMESTAMP
frame is expected to be exhaustively labeled and no more than 500BOUNDING_BOX
-es per frame are allowed. If a whole video is unknown, then it should be mentioned just once with ",,,,,,,,,," in place ofLABEL, [INSTANCE_ID],TIMESTAMP,BOUNDING_BOX
. Sample top level CSV file: TRAIN,gs://folder/train_videos.csv TEST,gs://folder/test_videos.csv UNASSIGNED,gs://folder/other_videos.csv Seven sample rows of a CSV file for a particular ML_USE: gs://folder/video1.avi,car,1,12.10,0.8,0.8,0.9,0.8,0.9,0.9,0.8,0.9 gs://folder/video1.avi,car,1,12.90,0.4,0.8,0.5,0.8,0.5,0.9,0.4,0.9 gs://folder/video1.avi,car,2,12.10,.4,.2,.5,.2,.5,.3,.4,.3 gs://folder/video1.avi,car,2,12.90,.8,.2,,,.9,.3,, gs://folder/video1.avi,bike,,12.50,.45,.45,,,.55,.55,, gs://folder/video2.avi,car,1,0,.1,.9,,,.9,.1,, gs://folder/video2.avi,,,,,,,,,,, </section> </div> <h4>AutoML Natural Language</h4> <div class="ds-selector-tabs"><section><h5>Entity Extraction</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,GCS_FILE_PATHML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing..
GCS_FILE_PATH
- a Identifies JSON Lines (.JSONL) file stored in Google Cloud Storage that contains in-line text in-line as documents for model training. After the training data set has been determined from theTRAIN
andUNASSIGNED
CSV files, the training data is divided into train and validation data sets. 70% for training and 30% for validation. For example: TRAIN,gs://folder/file1.jsonl VALIDATE,gs://folder/file2.jsonl TEST,gs://folder/file3.jsonl In-line JSONL files In-line .JSONL files contain, per line, a JSON document that wraps atext_snippet
field followed by one or moreannotations
fields, which havedisplay_name
andtext_extraction
fields to describe the entity from the text snippet. Multiple JSON documents can be separated using line breaks (\n). The supplied text must be annotated exhaustively. For example, if you include the text "horse", but do not label it as "animal", then "horse" is assumed to not be an "animal". Any given text snippet content must have 30,000 characters or less, and also be UTF-8 NFC encoded. ASCII is accepted as it is UTF-8 NFC encoded. For example: { "text_snippet": { "content": "dog car cat" }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 2} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 6} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 10} } } ] }\n { "text_snippet": { "content": "This dog is good." }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 5, "end_offset": 7} } } ] } JSONL files that reference documents .JSONL files contain, per line, a JSON document that wraps ainput_config
that contains the path to a source document. Multiple JSON documents can be separated using line breaks (\n). Supported document extensions: .PDF, .TIF, .TIFF For example: { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document1.pdf" ] } } } }\n { "document": { "input_config": { "gcs_source": { "input_uris": [ "gs://folder/document2.tif" ] } } } } In-line JSONL files with document layout information Note: You can only annotate documents using the UI. The format described below applies to annotated documents exported using the UI orexportData
. In-line .JSONL files for documents contain, per line, a JSON document that wraps adocument
field that provides the textual content of the document and the layout information. For example: { "document": { "document_text": { "content": "dog car cat" } "layout": [ { "text_segment": { "start_offset": 0, "end_offset": 11, }, "page_number": 1, "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}, ], }, "text_segment_type": TOKEN, } ], "document_dimensions": { "width": 8.27, "height": 11.69, "unit": INCH, } "page_count": 3, }, "annotations": [ { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 0, "end_offset": 3} } }, { "display_name": "vehicle", "text_extraction": { "text_segment": {"start_offset": 4, "end_offset": 7} } }, { "display_name": "animal", "text_extraction": { "text_segment": {"start_offset": 8, "end_offset": 11} } }, ], </section><section><h5>Classification</h5> See Preparing your training data for more information. One or more CSV file(s) with each line in the following format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),LABEL,LABEL,...ML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET
andGCS_FILE_PATH
are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as aGCS_FILE_PATH
. Otherwise, if the content is enclosed in double quotes (""), it is treated as aTEXT_SNIPPET
. ForGCS_FILE_PATH
, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. ForTEXT_SNIPPET
, AutoML imports the column content excluding quotes. In both cases, size of the content must be 10MB or less in size. For zip files, the size of each file inside the zip must be 10MB or less in size. For theMULTICLASS
classification type, at most oneLABEL
is allowed. TheML_USE
andLABEL
columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIP A maximum of 100 unique labels are allowed per CSV row. Sample rows: TRAIN,"They have bad food and very rude",RudeService,BadFood gs://folder/content.txt,SlowService TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,BadFood </section><section><h5>Sentiment Analysis</h5> See Preparing your training data for more information. CSV file(s) with each line in format: ML_USE,(TEXT_SNIPPET | GCS_FILE_PATH),SENTIMENTML_USE
- Identifies the data set that the current row (file) applies to. This value can be one of the following:TRAIN
- Rows in this file are used to train the model.TEST
- Rows in this file are used to test the model during training.UNASSIGNED
- Rows in this file are not categorized. They are Automatically divided into train and test data. 80% for training and 20% for testing.
TEXT_SNIPPET
andGCS_FILE_PATH
are distinguished by a pattern. If the column content is a valid Google Cloud Storage file path, that is, prefixed by "gs://", it is treated as aGCS_FILE_PATH
. Otherwise, if the content is enclosed in double quotes (""), it is treated as aTEXT_SNIPPET
. ForGCS_FILE_PATH
, the path must lead to a file with supported extension and UTF-8 encoding, for example, "gs://folder/content.txt" AutoML imports the file content as a text snippet. ForTEXT_SNIPPET
, AutoML imports the column content excluding quotes. In both cases, size of the content must be 128kB or less in size. For zip files, the size of each file inside the zip must be 128kB or less in size. TheML_USE
andSENTIMENT
columns are optional. Supported file extensions: .TXT, .PDF, .TIF, .TIFF, .ZIPSENTIMENT
- An integer between 0 and Dataset.text_sentiment_dataset_metadata.sentiment_max (inclusive). Describes the ordinal of the sentiment - higher value means a more positive sentiment. All the values are completely relative, i.e. neither 0 needs to mean a negative or neutral sentiment nor sentiment_max needs to mean a positive one - it is just required that 0 is the least positive sentiment in the data, and sentiment_max is the most positive one. The SENTIMENT shouldn't be confused with "score" or "magnitude" from the previous Natural Language Sentiment Analysis API. All SENTIMENT values between 0 and sentiment_max must be represented in the imported data. On prediction the same 0 to sentiment_max range will be used. The difference between neighboring sentiment values needs not to be uniform, e.g. 1 and 2 may be similar whereas the difference between 2 and 3 may be large. Sample rows: TRAIN,"@freewrytin this is way too good for your product",2 gs://folder/content.txt,3 TEST,gs://folder/document.pdf VALIDATE,gs://folder/text_files.zip,2 </section> </div> <h4>AutoML Tables</h4><div class="ui-datasection-main"><section class="selected"> See Preparing your training data for more information. You can use either gcs_source or bigquery_source. All input is concatenated into a single primary_table_spec_id For gcs_source: CSV file(s), where the first row of the first file is the header, containing unique 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. Each .CSV file by itself must be 10GB or smaller, and their total size must be 100GB or smaller. First three sample rows of a CSV file: <pre> "Id","First Name","Last Name","Dob","Addresses" "1","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"}]" "2","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"}]} </pre> For bigquery_source: An URI of a BigQuery table. The user data size of the BigQuery table must be 100GB or smaller. An imported table must have between 2 and 1,000 columns, inclusive, and between 1000 and 100,000,000 rows, inclusive. There are at most 5 import data running in parallel. </section> </div> Input field definitions:ML_USE
: ("TRAIN" | "VALIDATE" | "TEST" | "UNASSIGNED") Describes how the given example (file) should be used for model training. "UNASSIGNED" can be used when user has no preference.GCS_FILE_PATH
: The path to a file on Google Cloud Storage. For example, "gs://folder/image1.png".LABEL
: A display name of an object on an image, video etc., e.g. "dog". Must be up to 32 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscores(_), and ASCII digits 0-9. For each label an AnnotationSpec is created which display_name becomes the label; AnnotationSpecs are given back in predictions.INSTANCE_ID
: A positive integer that identifies a specific instance of a labeled entity on an example. Used e.g. to track two cars on a video while being able to tell apart which one is which.BOUNDING_BOX
: (VERTEX,VERTEX,VERTEX,VERTEX
|VERTEX,,,VERTEX,,
) A rectangle parallel to the frame of the example (image, video). If 4 vertices are given they are connected by edges in the order provided, if 2 are given they are recognized as diagonally opposite vertices of the rectangle.VERTEX
: (COORDINATE,COORDINATE
) First coordinate is horizontal (x), the second is vertical (y).COORDINATE
: A float in 0 to 1 range, relative to total length of image or video in given dimension. For fractions the leading non-decimal 0 can be omitted (i.e. 0.3 = .3). Point 0,0 is in top left.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.TEXT_SNIPPET
: The content of a text snippet, UTF-8 encoded, enclosed within double quotes ("").DOCUMENT
: A field that provides the textual content with document and the layout information. 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 nothing is imported. Regardless of overall success or failure the per-row failures, up to a certain count cap, is listed in Operation.metadata.partial_failures.
Protobuf type google.cloud.automl.v1.InputConfig
Io
ListDatasetsRequest
Request message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1.ListDatasetsRequest
ListDatasetsRequest.Builder
Request message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1.ListDatasetsRequest
ListDatasetsResponse
Response message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1.ListDatasetsResponse
ListDatasetsResponse.Builder
Response message for AutoMl.ListDatasets.
Protobuf type google.cloud.automl.v1.ListDatasetsResponse
ListModelEvaluationsRequest
Request message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1.ListModelEvaluationsRequest
ListModelEvaluationsRequest.Builder
Request message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1.ListModelEvaluationsRequest
ListModelEvaluationsResponse
Response message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1.ListModelEvaluationsResponse
ListModelEvaluationsResponse.Builder
Response message for AutoMl.ListModelEvaluations.
Protobuf type google.cloud.automl.v1.ListModelEvaluationsResponse
ListModelsRequest
Request message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1.ListModelsRequest
ListModelsRequest.Builder
Request message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1.ListModelsRequest
ListModelsResponse
Response message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1.ListModelsResponse
ListModelsResponse.Builder
Response message for AutoMl.ListModels.
Protobuf type google.cloud.automl.v1.ListModelsResponse
LocationName
LocationName.Builder
Builder for projects/{project}/locations/{location}.
Model
API proto representing a trained machine learning model.
Protobuf type google.cloud.automl.v1.Model
Model.Builder
API proto representing a trained machine learning model.
Protobuf type google.cloud.automl.v1.Model
ModelEvaluation
Evaluation results of a model.
Protobuf type google.cloud.automl.v1.ModelEvaluation
ModelEvaluation.Builder
Evaluation results of a model.
Protobuf type google.cloud.automl.v1.ModelEvaluation
ModelEvaluationName
ModelEvaluationName.Builder
Builder for projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}.
ModelEvaluationOuterClass
ModelExportOutputConfig
Output configuration for ModelExport Action.
Protobuf type google.cloud.automl.v1.ModelExportOutputConfig
ModelExportOutputConfig.Builder
Output configuration for ModelExport Action.
Protobuf type google.cloud.automl.v1.ModelExportOutputConfig
ModelName
ModelName.Builder
Builder for projects/{project}/locations/{location}/models/{model}.
ModelOuterClass
NormalizedVertex
A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.
Protobuf type google.cloud.automl.v1.NormalizedVertex
NormalizedVertex.Builder
A vertex represents a 2D point in the image. The normalized vertex coordinates are between 0 to 1 fractions relative to the original plane (image, video). E.g. if the plane (e.g. whole image) would have size 10 x 20 then a point with normalized coordinates (0.1, 0.3) would be at the position (1, 6) on that plane.
Protobuf type google.cloud.automl.v1.NormalizedVertex
OperationMetadata
Metadata used across all long running operations returned by AutoML API.
Protobuf type google.cloud.automl.v1.OperationMetadata
OperationMetadata.Builder
Metadata used across all long running operations returned by AutoML API.
Protobuf type google.cloud.automl.v1.OperationMetadata
Operations
OutputConfig
- For Translation:
CSV file
translation.csv
, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language)- For Tables:
Output depends on whether the dataset was imported from Google Cloud
Storage or BigQuery.
Google Cloud Storage case:
gcs_destination
must be set. Exported are CSV file(s)
tables_1.csv
,tables_2.csv
,...,tables_N.csv
with each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with nameexport_data_<automl-dataset-display-name>_<timestamp-of-export-call>
where <automl-dataset-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table calledprimary_table
will be created, and filled with precisely the same data as this obtained on import.
- For Tables:
Output depends on whether the dataset was imported from Google Cloud
Storage or BigQuery.
Google Cloud Storage case:
gcs_destination
must be set. Exported are CSV file(s)
Protobuf type google.cloud.automl.v1.OutputConfig
OutputConfig.Builder
- For Translation:
CSV file
translation.csv
, with each line in format: ML_USE,GCS_FILE_PATH GCS_FILE_PATH leads to a .TSV file which describes examples that have given ML_USE, using the following row format per line: TEXT_SNIPPET (in source language) \t TEXT_SNIPPET (in target language)- For Tables:
Output depends on whether the dataset was imported from Google Cloud
Storage or BigQuery.
Google Cloud Storage case:
gcs_destination
must be set. Exported are CSV file(s)
tables_1.csv
,tables_2.csv
,...,tables_N.csv
with each having as header line the table's column names, and all other lines contain values for the header columns. BigQuery case: bigquery_destination pointing to a BigQuery project must be set. In the given project a new dataset will be created with nameexport_data_<automl-dataset-display-name>_<timestamp-of-export-call>
where <automl-dataset-display-name> will be made BigQuery-dataset-name compatible (e.g. most special characters will become underscores), and timestamp will be in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In that dataset a new table calledprimary_table
will be created, and filled with precisely the same data as this obtained on import.
- For Tables:
Output depends on whether the dataset was imported from Google Cloud
Storage or BigQuery.
Google Cloud Storage case:
gcs_destination
must be set. Exported are CSV file(s)
Protobuf type google.cloud.automl.v1.OutputConfig
PredictRequest
Request message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1.PredictRequest
PredictRequest.Builder
Request message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1.PredictRequest
PredictResponse
Response message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1.PredictResponse
PredictResponse.Builder
Response message for PredictionService.Predict.
Protobuf type google.cloud.automl.v1.PredictResponse
PredictionServiceClient
Service Description: AutoML Prediction API.
On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
ExamplePayload payload = ExamplePayload.newBuilder().build();
Map<String, String> params = new HashMap<>();
PredictResponse response = predictionServiceClient.predict(name, payload, params);
}
Note: close() needs to be called on the PredictionServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().
The surface of this class includes several types of Java methods for each of the API's methods:
- A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
- A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
- A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.
See the individual methods for example code.
Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.
This class can be customized by passing in a custom instance of PredictionServiceSettings to create(). For example:
To customize credentials:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder()
.setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
.build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
To customize the endpoint:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
To use REST (HTTP1.1/JSON) transport (instead of gRPC) for sending and receiving requests over the wire:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.newBuilder()
.setTransportChannelProvider(
PredictionServiceSettings.defaultHttpJsonTransportProviderBuilder().build())
.build();
PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings);
Please refer to the GitHub repository's samples for more quickstart code snippets.
PredictionServiceGrpc
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceBlockingStub
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceFutureStub
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceImplBase
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
PredictionServiceGrpc.PredictionServiceStub
AutoML Prediction API. On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.
PredictionServiceProto
PredictionServiceSettings
Settings class to configure an instance of PredictionServiceClient.
The default instance has everything set to sensible defaults:
- The default service address (automl.googleapis.com) and default port (443) are used.
- Credentials are acquired automatically through Application Default Credentials.
- Retries are configured for idempotent methods but not for non-idempotent methods.
The builder of this class is recursive, so contained classes are themselves builders. When build() is called, the tree of builders is called to create the complete settings object.
For example, to set the total timeout of predict to 30 seconds:
// This snippet has been automatically generated and should be regarded as a code template only.
// It will require modifications to work:
// - It may require correct/in-range values for request initialization.
// - It may require specifying regional endpoints when creating the service client as shown in
// https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
PredictionServiceSettings.Builder predictionServiceSettingsBuilder =
PredictionServiceSettings.newBuilder();
predictionServiceSettingsBuilder
.predictSettings()
.setRetrySettings(
predictionServiceSettingsBuilder.predictSettings().getRetrySettings().toBuilder()
.setTotalTimeout(Duration.ofSeconds(30))
.build());
PredictionServiceSettings predictionServiceSettings = predictionServiceSettingsBuilder.build();
PredictionServiceSettings.Builder
Builder for PredictionServiceSettings.
TextClassificationDatasetMetadata
Dataset metadata for classification.
Protobuf type google.cloud.automl.v1.TextClassificationDatasetMetadata
TextClassificationDatasetMetadata.Builder
Dataset metadata for classification.
Protobuf type google.cloud.automl.v1.TextClassificationDatasetMetadata
TextClassificationModelMetadata
Model metadata that is specific to text classification.
Protobuf type google.cloud.automl.v1.TextClassificationModelMetadata
TextClassificationModelMetadata.Builder
Model metadata that is specific to text classification.
Protobuf type google.cloud.automl.v1.TextClassificationModelMetadata
TextExtraction
TextExtractionAnnotation
Annotation for identifying spans of text.
Protobuf type google.cloud.automl.v1.TextExtractionAnnotation
TextExtractionAnnotation.Builder
Annotation for identifying spans of text.
Protobuf type google.cloud.automl.v1.TextExtractionAnnotation
TextExtractionDatasetMetadata
Dataset metadata that is specific to text extraction
Protobuf type google.cloud.automl.v1.TextExtractionDatasetMetadata
TextExtractionDatasetMetadata.Builder
Dataset metadata that is specific to text extraction
Protobuf type google.cloud.automl.v1.TextExtractionDatasetMetadata
TextExtractionEvaluationMetrics
Model evaluation metrics for text extraction problems.
Protobuf type google.cloud.automl.v1.TextExtractionEvaluationMetrics
TextExtractionEvaluationMetrics.Builder
Model evaluation metrics for text extraction problems.
Protobuf type google.cloud.automl.v1.TextExtractionEvaluationMetrics
TextExtractionEvaluationMetrics.ConfidenceMetricsEntry
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1.TextExtractionEvaluationMetrics.ConfidenceMetricsEntry
TextExtractionEvaluationMetrics.ConfidenceMetricsEntry.Builder
Metrics for a single confidence threshold.
Protobuf type
google.cloud.automl.v1.TextExtractionEvaluationMetrics.ConfidenceMetricsEntry
TextExtractionModelMetadata
Model metadata that is specific to text extraction.
Protobuf type google.cloud.automl.v1.TextExtractionModelMetadata
TextExtractionModelMetadata.Builder
Model metadata that is specific to text extraction.
Protobuf type google.cloud.automl.v1.TextExtractionModelMetadata
TextProto
TextSegment
A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
Protobuf type google.cloud.automl.v1.TextSegment
TextSegment.Builder
A contiguous part of a text (string), assuming it has an UTF-8 NFC encoding.
Protobuf type google.cloud.automl.v1.TextSegment
TextSegmentProto
TextSentimentAnnotation
Contains annotation details specific to text sentiment.
Protobuf type google.cloud.automl.v1.TextSentimentAnnotation
TextSentimentAnnotation.Builder
Contains annotation details specific to text sentiment.
Protobuf type google.cloud.automl.v1.TextSentimentAnnotation
TextSentimentDatasetMetadata
Dataset metadata for text sentiment.
Protobuf type google.cloud.automl.v1.TextSentimentDatasetMetadata
TextSentimentDatasetMetadata.Builder
Dataset metadata for text sentiment.
Protobuf type google.cloud.automl.v1.TextSentimentDatasetMetadata
TextSentimentEvaluationMetrics
Model evaluation metrics for text sentiment problems.
Protobuf type google.cloud.automl.v1.TextSentimentEvaluationMetrics
TextSentimentEvaluationMetrics.Builder
Model evaluation metrics for text sentiment problems.
Protobuf type google.cloud.automl.v1.TextSentimentEvaluationMetrics
TextSentimentModelMetadata
Model metadata that is specific to text sentiment.
Protobuf type google.cloud.automl.v1.TextSentimentModelMetadata
TextSentimentModelMetadata.Builder
Model metadata that is specific to text sentiment.
Protobuf type google.cloud.automl.v1.TextSentimentModelMetadata
TextSentimentProto
TextSnippet
A representation of a text snippet.
Protobuf type google.cloud.automl.v1.TextSnippet
TextSnippet.Builder
A representation of a text snippet.
Protobuf type google.cloud.automl.v1.TextSnippet
TranslationAnnotation
Annotation details specific to translation.
Protobuf type google.cloud.automl.v1.TranslationAnnotation
TranslationAnnotation.Builder
Annotation details specific to translation.
Protobuf type google.cloud.automl.v1.TranslationAnnotation
TranslationDatasetMetadata
Dataset metadata that is specific to translation.
Protobuf type google.cloud.automl.v1.TranslationDatasetMetadata
TranslationDatasetMetadata.Builder
Dataset metadata that is specific to translation.
Protobuf type google.cloud.automl.v1.TranslationDatasetMetadata
TranslationEvaluationMetrics
Evaluation metrics for the dataset.
Protobuf type google.cloud.automl.v1.TranslationEvaluationMetrics
TranslationEvaluationMetrics.Builder
Evaluation metrics for the dataset.
Protobuf type google.cloud.automl.v1.TranslationEvaluationMetrics
TranslationModelMetadata
Model metadata that is specific to translation.
Protobuf type google.cloud.automl.v1.TranslationModelMetadata
TranslationModelMetadata.Builder
Model metadata that is specific to translation.
Protobuf type google.cloud.automl.v1.TranslationModelMetadata
TranslationProto
UndeployModelOperationMetadata
Details of UndeployModel operation.
Protobuf type google.cloud.automl.v1.UndeployModelOperationMetadata
UndeployModelOperationMetadata.Builder
Details of UndeployModel operation.
Protobuf type google.cloud.automl.v1.UndeployModelOperationMetadata
UndeployModelRequest
Request message for AutoMl.UndeployModel.
Protobuf type google.cloud.automl.v1.UndeployModelRequest
UndeployModelRequest.Builder
Request message for AutoMl.UndeployModel.
Protobuf type google.cloud.automl.v1.UndeployModelRequest
UpdateDatasetRequest
Request message for AutoMl.UpdateDataset
Protobuf type google.cloud.automl.v1.UpdateDatasetRequest
UpdateDatasetRequest.Builder
Request message for AutoMl.UpdateDataset
Protobuf type google.cloud.automl.v1.UpdateDatasetRequest
UpdateModelRequest
Request message for AutoMl.UpdateModel
Protobuf type google.cloud.automl.v1.UpdateModelRequest
UpdateModelRequest.Builder
Request message for AutoMl.UpdateModel
Protobuf type google.cloud.automl.v1.UpdateModelRequest
Interfaces
AnnotationPayloadOrBuilder
AnnotationSpecOrBuilder
BatchPredictInputConfigOrBuilder
BatchPredictOperationMetadata.BatchPredictOutputInfoOrBuilder
BatchPredictOperationMetadataOrBuilder
BatchPredictOutputConfigOrBuilder
BatchPredictRequestOrBuilder
BatchPredictResultOrBuilder
BoundingBoxMetricsEntry.ConfidenceMetricsEntryOrBuilder
BoundingBoxMetricsEntryOrBuilder
BoundingPolyOrBuilder
ClassificationAnnotationOrBuilder
ClassificationEvaluationMetrics.ConfidenceMetricsEntryOrBuilder
ClassificationEvaluationMetrics.ConfusionMatrix.RowOrBuilder
ClassificationEvaluationMetrics.ConfusionMatrixOrBuilder
ClassificationEvaluationMetricsOrBuilder
CreateDatasetOperationMetadataOrBuilder
CreateDatasetRequestOrBuilder
CreateModelOperationMetadataOrBuilder
CreateModelRequestOrBuilder
DatasetOrBuilder
DeleteDatasetRequestOrBuilder
DeleteModelRequestOrBuilder
DeleteOperationMetadataOrBuilder
DeployModelOperationMetadataOrBuilder
DeployModelRequestOrBuilder
Document.LayoutOrBuilder
DocumentDimensionsOrBuilder
DocumentInputConfigOrBuilder
DocumentOrBuilder
ExamplePayloadOrBuilder
ExportDataOperationMetadata.ExportDataOutputInfoOrBuilder
ExportDataOperationMetadataOrBuilder
ExportDataRequestOrBuilder
ExportModelOperationMetadata.ExportModelOutputInfoOrBuilder
ExportModelOperationMetadataOrBuilder
ExportModelRequestOrBuilder
GcsDestinationOrBuilder
GcsSourceOrBuilder
GetAnnotationSpecRequestOrBuilder
GetDatasetRequestOrBuilder
GetModelEvaluationRequestOrBuilder
GetModelRequestOrBuilder
ImageClassificationDatasetMetadataOrBuilder
ImageClassificationModelDeploymentMetadataOrBuilder
ImageClassificationModelMetadataOrBuilder
ImageObjectDetectionAnnotationOrBuilder
ImageObjectDetectionDatasetMetadataOrBuilder
ImageObjectDetectionEvaluationMetricsOrBuilder
ImageObjectDetectionModelDeploymentMetadataOrBuilder
ImageObjectDetectionModelMetadataOrBuilder
ImageOrBuilder
ImportDataOperationMetadataOrBuilder
ImportDataRequestOrBuilder
InputConfigOrBuilder
ListDatasetsRequestOrBuilder
ListDatasetsResponseOrBuilder
ListModelEvaluationsRequestOrBuilder
ListModelEvaluationsResponseOrBuilder
ListModelsRequestOrBuilder
ListModelsResponseOrBuilder
ModelEvaluationOrBuilder
ModelExportOutputConfigOrBuilder
ModelOrBuilder
NormalizedVertexOrBuilder
OperationMetadataOrBuilder
OutputConfigOrBuilder
PredictRequestOrBuilder
PredictResponseOrBuilder
TextClassificationDatasetMetadataOrBuilder
TextClassificationModelMetadataOrBuilder
TextExtractionAnnotationOrBuilder
TextExtractionDatasetMetadataOrBuilder
TextExtractionEvaluationMetrics.ConfidenceMetricsEntryOrBuilder
TextExtractionEvaluationMetricsOrBuilder
TextExtractionModelMetadataOrBuilder
TextSegmentOrBuilder
TextSentimentAnnotationOrBuilder
TextSentimentDatasetMetadataOrBuilder
TextSentimentEvaluationMetricsOrBuilder
TextSentimentModelMetadataOrBuilder
TextSnippetOrBuilder
TranslationAnnotationOrBuilder
TranslationDatasetMetadataOrBuilder
TranslationEvaluationMetricsOrBuilder
TranslationModelMetadataOrBuilder
UndeployModelOperationMetadataOrBuilder
UndeployModelRequestOrBuilder
UpdateDatasetRequestOrBuilder
UpdateModelRequestOrBuilder
Enums
AnnotationPayload.DetailCase
BatchPredictInputConfig.SourceCase
BatchPredictOperationMetadata.BatchPredictOutputInfo.OutputLocationCase
BatchPredictOutputConfig.DestinationCase
ClassificationType
Type of the classification problem.
Protobuf enum google.cloud.automl.v1.ClassificationType
Dataset.DatasetMetadataCase
DeployModelRequest.ModelDeploymentMetadataCase
Document.Layout.TextSegmentType
The type of TextSegment in the context of the original document.
Protobuf enum google.cloud.automl.v1.Document.Layout.TextSegmentType
DocumentDimensions.DocumentDimensionUnit
Unit of the document dimension.
Protobuf enum google.cloud.automl.v1.DocumentDimensions.DocumentDimensionUnit
ExamplePayload.PayloadCase
ExportDataOperationMetadata.ExportDataOutputInfo.OutputLocationCase
Image.DataCase
InputConfig.SourceCase
Model.DeploymentState
Deployment state of the model.
Protobuf enum google.cloud.automl.v1.Model.DeploymentState