ClientToken is an idempotency token that ensures a call to CreateDataset completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from CreateDataset. In this case, safely retry your call to CreateDataset by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple dataset creation requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to CreateDataset. An idempotency token is active for 8 hours.

The location of the manifest file that Amazon Lookout for Vision uses to create the dataset.

If you don't specify DatasetSource, an empty dataset is created and the operation synchronously returns. Later, you can add JSON Lines by calling UpdateDatasetEntries.

If you specify a value for DataSource, the manifest at the S3 location is validated and used to create the dataset. The call to CreateDataset is asynchronous and might take a while to complete. To find out the current status, Check the value of Status returned in a call to DescribeDataset.

The name of the project in which you want to create a dataset.

The type of the dataset. Specify train for a training dataset. Specify test for a test dataset.

Information about the dataset.

The response's http status code.

ClientToken is an idempotency token that ensures a call to CreateModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from CreateModel. In this case, safely retry your call to CreateModel by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from starting multiple training jobs. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to CreateModel. An idempotency token is active for 8 hours.

A description for the version of the model.

The identifier for your AWS KMS key. The key is used to encrypt training and test images copied into the service for model training. Your source images are unaffected. If this parameter is not specified, the copied images are encrypted by a key that AWS owns and manages.

A set of tags (key-value pairs) that you want to attach to the model.

The name of the project in which you want to create a model version.

The location where Amazon Lookout for Vision saves the training results.

The response from a call to CreateModel.

The response's http status code.

ClientToken is an idempotency token that ensures a call to CreateProject completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from CreateProject. In this case, safely retry your call to CreateProject by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple project creation requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to CreateProject. An idempotency token is active for 8 hours.

The name for the project.

Information about the project.

The response's http status code.

ClientToken is an idempotency token that ensures a call to DeleteDataset completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from DeleteDataset. In this case, safely retry your call to DeleteDataset by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple deletetion requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to DeleteDataset. An idempotency token is active for 8 hours.

The name of the project that contains the dataset that you want to delete.

The type of the dataset to delete. Specify train to delete the training dataset. Specify test to delete the test dataset. To delete the dataset in a single dataset project, specify train.

The response's http status code.

ClientToken is an idempotency token that ensures a call to DeleteModel completes only once. You choose the value to pass. For example, an issue might prevent you from getting a response from DeleteModel. In this case, safely retry your call to DeleteModel by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple model deletion requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to DeleteModel. An idempotency token is active for 8 hours.

The name of the project that contains the model that you want to delete.

The version of the model that you want to delete.

The Amazon Resource Name (ARN) of the model that was deleted.

The response's http status code.

ClientToken is an idempotency token that ensures a call to DeleteProject completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from DeleteProject. In this case, safely retry your call to DeleteProject by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple project deletion requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to DeleteProject. An idempotency token is active for 8 hours.

The name of the project to delete.

The Amazon Resource Name (ARN) of the project that was deleted.

The response's http status code.

The name of the project that contains the dataset that you want to describe.

The type of the dataset to describe. Specify train to describe the training dataset. Specify test to describe the test dataset. If you have a single dataset project, specify train

The description of the requested dataset.

The response's http status code.

The project that contains the version of a model that you want to describe.

The version of the model that you want to describe.

Contains the description of the model.

The response's http status code.

The name of the project that contains the model packaging job that you want to describe.

The job name for the model packaging job.

The description of the model packaging job.

The response's http status code.

The name of the project that you want to describe.

The description of the project.

The response's http status code.

The name of the project that contains the model version that you want to use.

The version of the model that you want to use.

The type of the image passed in Body. Valid values are image/png (PNG format images) and image/jpeg (JPG format images).

The unencrypted image bytes that you want to analyze.

The results of the DetectAnomalies operation.

The response's http status code.

Only includes entries after the specified date in the response. For example, 2020-06-23T00:00:00.

Specify normal to include only normal images. Specify anomaly to only include anomalous entries. If you don't specify a value, Amazon Lookout for Vision returns normal and anomalous images.

Only includes entries before the specified date in the response. For example, 2020-06-23T00:00:00.

Specify true to include labeled entries, otherwise specify false. If you don't specify a value, Lookout for Vision returns all entries.

The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

If the previous response was incomplete (because there is more data to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of dataset entries.

Perform a "contains" search on the values of the source-ref key within the dataset. For example a value of "IMG_17" returns all JSON Lines where the source-ref key value matches *IMG_17*.

The name of the project that contains the dataset that you want to list.

The type of the dataset that you want to list. Specify train to list the training dataset. Specify test to list the test dataset. If you have a single dataset project, specify train.

A list of the entries (JSON Lines) within the dataset.

If the response is truncated, Amazon Lookout for Vision returns this token that you can use in the subsequent request to retrieve the next set ofdataset entries.

The response's http status code.

The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

If the previous response was incomplete (because there is more results to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

The name of the project for which you want to list the model packaging jobs.

A list of the model packaging jobs created for the specified Amazon Lookout for Vision project.

If the previous response was incomplete (because there is more results to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.

The response's http status code.

The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

If the previous response was incomplete (because there is more data to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of models.

The name of the project that contains the model versions that you want to list.

A list of model versions in the specified project.

If the response is truncated, Amazon Lookout for Vision returns this token that you can use in the subsequent request to retrieve the next set of models.

The response's http status code.

The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.

If the previous response was incomplete (because there is more data to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of projects.

If the response is truncated, Amazon Lookout for Vision returns this token that you can use in the subsequent request to retrieve the next set of projects.

A list of projects in your AWS account.

The response's http status code.

The Amazon Resource Name (ARN) of the model for which you want to list tags.

A map of tag keys and values attached to the specified model.

The response's http status code.

ClientToken is an idempotency token that ensures a call to StartModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StartModel. In this case, safely retry your call to StartModel by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StartModel. An idempotency token is active for 8 hours.

The maximum number of inference units to use for auto-scaling the model. If you don't specify a value, Amazon Lookout for Vision doesn't auto-scale the model.

The name of the project that contains the model that you want to start.

The version of the model that you want to start.

The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.

The current running status of the model.

The response's http status code.

ClientToken is an idempotency token that ensures a call to StartModelPackagingJob completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StartModelPackagingJob. In this case, safely retry your call to StartModelPackagingJob by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple dataset creation requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StartModelPackagingJob. An idempotency token is active for 8 hours.

A description for the model packaging job.

A name for the model packaging job. If you don't supply a value, the service creates a job name for you.

The name of the project which contains the version of the model that you want to package.

The version of the model within the project that you want to package.

The configuration for the model packaging job.

The job name for the model packaging job. If you don't supply a job name in the JobName input parameter, the service creates a job name for you.

The response's http status code.

ClientToken is an idempotency token that ensures a call to StopModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StopModel. In this case, safely retry your call to StopModel by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple stop requests. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StopModel. An idempotency token is active for 8 hours.

The name of the project that contains the model that you want to stop.

The version of the model that you want to stop.

The status of the model.

The response's http status code.

The Amazon Resource Name (ARN) of the model to assign the tags.

The key-value tags to assign to the model.

The response's http status code.

The Amazon Resource Name (ARN) of the model from which you want to remove tags.

A list of the keys of the tags that you want to remove.

The response's http status code.

ClientToken is an idempotency token that ensures a call to UpdateDatasetEntries completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from UpdateDatasetEntries. In this case, safely retry your call to UpdateDatasetEntries by using the same ClientToken parameter value.

If you don't supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple updates with the same dataset entries. You'll need to provide your own value for other use cases.

An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to UpdateDatasetEntries. An idempotency token is active for 8 hours.

The name of the project that contains the dataset that you want to update.

The type of the dataset that you want to update. Specify train to update the training dataset. Specify test to update the test dataset. If you have a single dataset project, specify train.

The entries to add to the dataset.-- -- Note: This Lens automatically encodes and decodes Base64 data. -- The underlying isomorphism will encode to Base64 representation during -- serialisation, and decode from Base64 representation during deserialisation. -- This Lens accepts and returns only raw unencoded data.

The status of the dataset update.

The response's http status code.

The name of an anomaly type found in an image. Name maps to an anomaly type in the training dataset, apart from the anomaly type background. The service automatically inserts the background anomaly type into the response from DetectAnomalies.

Information about the pixel mask that covers an anomaly type.

The Unix timestamp for the time and date that the dataset was created.

The type of the dataset. The value train represents a training dataset or single dataset project. The value test represents a test dataset.

Statistics about the images in a dataset.

The Unix timestamp for the date and time that the dataset was last updated.

The name of the project that contains the dataset.

The status of the dataset.

The status message for the dataset.

The S3 bucket location for the manifest file.

the total number of images labeled as an anomaly.

The total number of labeled images.

The total number of images labeled as normal.

The total number of images in the dataset.

The Unix timestamp for the date and time that the dataset was created.

The type of the dataset.

The status for the dataset.

The status message for the dataset.

Location information for the manifest file.

If the model is an image segmentation model, Anomalies contains a list of anomaly types found in the image. There is one entry for each type of anomaly found (even if multiple instances of an anomaly type exist on the image). The first element in the list is always an anomaly type representing the image background ('background') and shouldn't be considered an anomaly. Amazon Lookout for Vision automatically add the background anomaly type to the response, and you don't need to declare a background anomaly type in your dataset.

If the list has one entry ('background'), no anomalies were found on the image.

An image classification model doesn't return an Anomalies list.

If the model is an image segmentation model, AnomalyMask contains pixel masks that covers all anomaly types found on the image. Each anomaly type has a different mask color. To map a color to an anomaly type, see the color field of the PixelAnomaly object.

An image classification model doesn't return an Anomalies list.-- -- Note: This Lens automatically encodes and decodes Base64 data. -- The underlying isomorphism will encode to Base64 representation during -- serialisation, and decode from Base64 representation during deserialisation. -- This Lens accepts and returns only raw unencoded data.

The confidence that Lookout for Vision has in the accuracy of the classification in IsAnomalous.

True if Amazon Lookout for Vision classifies the image as containing an anomaly, otherwise false.

The source of the image that was analyzed. direct means that the images was supplied from the local computer. No other values are supported.

Additional compiler options for the Greengrass component. Currently, only NVIDIA Graphics Processing Units (GPU) and CPU accelerators are supported. If you specify TargetDevice, don't specify CompilerOptions.

For more information, see Compiler options in the Amazon Lookout for Vision Developer Guide.

A description for the AWS IoT Greengrass component.

A Version for the AWS IoT Greengrass component. If you don't provide a value, a default value of Model Version.0.0 is used.

A set of tags (key-value pairs) that you want to attach to the AWS IoT Greengrass component.

The target device for the model. Currently the only supported value is jetson_xavier. If you specify TargetDevice, you can't specify TargetPlatform.

The target platform for the model. If you specify TargetPlatform, you can't specify TargetDevice.

An S3 location in which Lookout for Vision stores the component artifacts.

A name for the AWS IoT Greengrass component.

The name of the component.

The version of the component.

The Amazon Resource Name (ARN) of the component.

The type of the image.

The version ID of the bucket.

The Amazon S3 bucket that contains the manifest.

The name and location of the manifest file withiin the bucket.

The unix timestamp for the date and time that the model was created.

The description for the model.

The unix timestamp for the date and time that the evaluation ended.

The S3 location where Amazon Lookout for Vision saves the manifest file that was used to test the trained model and generate the performance scores.

The S3 location where Amazon Lookout for Vision saves the performance metrics.

The identifer for the AWS Key Management Service (AWS KMS) key that was used to encrypt the model during training.

The maximum number of inference units Amazon Lookout for Vision uses to auto-scale the model. For more information, see StartModel.

The minimum number of inference units used by the model. For more information, see StartModel

The Amazon Resource Name (ARN) of the model.

The version of the model

The S3 location where Amazon Lookout for Vision saves model training files.

Performance metrics for the model. Created during training.

The status of the model.

The status message for the model.

The unix timestamp for the date and time that the model was created.

The description for the model.

The Amazon Resource Name (ARN) of the model.

The version of the model.

Performance metrics for the model. Not available until training has successfully completed.

The status of the model.

The status message for the model.

Configuration information for the AWS IoT Greengrass component in a model packaging job.

The Unix timestamp for the time and date that the model packaging job was created.

The name of the model packaging job.

The Unix timestamp for the time and date that the model packaging job was last updated.

The configuration information used in the model packaging job.

The description for the model packaging job.

The AWS service used to package the job. Currently Lookout for Vision can package jobs with AWS IoT Greengrass.

Information about the output of the model packaging job. For more information, see DescribeModelPackagingJob.

The version of the model used in the model packaging job.

The name of the project that's associated with a model that's in the model package.

The status of the model packaging job.

The status message for the model packaging job.

The Unix timestamp for the time and date that the model packaging job was created.

The name of the model packaging job.

The Unix timestamp for the time and date that the model packaging job was last updated.

The description for the model packaging job.

The AWS service used to package the job. Currently Lookout for Vision can package jobs with AWS IoT Greengrass.

The version of the model that is in the model package.

The project that contains the model that is in the model package.

The status of the model packaging job.

The status message for the model packaging job.

Information about the AWS IoT Greengrass component in a model packaging job.

The overall F1 score metric for the trained model.

The overall precision metric value for the trained model.

The overall recall metric value for the trained model.

The S3 location for the output.

The bucket that contains the training output.

The location of the training output in the bucket.

A hex color value for the mask that covers an anomaly type. Each anomaly type has a different mask color. The color maps to the color of the anomaly type used in the training dataset.

The percentage area of the image that the anomaly type covers.

The unix timestamp for the date and time that the project was created.

A list of datasets in the project.

The Amazon Resource Name (ARN) of the project.

The name of the project.

The unix timestamp for the date and time that the project was created.

The Amazon Resource Name (ARN) of the project.

The name of the project.

The path of the folder, within the S3 bucket, that contains the output.

The S3 bucket that contains the training or model packaging job output. If you are training a model, the bucket must in your AWS account. If you use an S3 bucket for a model packaging job, the S3 bucket must be in the same AWS Region and AWS account in which you use AWS IoT Greengrass.

The key of the tag that is attached to the specified model.

The value of the tag that is attached to the specified model.

The target accelerator for the model. Currently, Amazon Lookout for Vision only supports NVIDIA (Nvidia graphics processing unit) and CPU accelerators. If you specify NVIDIA as an accelerator, you must also specify the gpu-code, trt-ver, and cuda-ver compiler options. If you don't specify an accelerator, Lookout for Vision uses the CPU for compilation and we highly recommend that you use the GreengrassConfiguration$CompilerOptions field. For example, you can use the following compiler options for CPU:

• mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
• mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

The target operating system for the model. Linux is the only operating system that is currently supported.

The target architecture for the model. The currently supported architectures are X86_64 (64-bit version of the x86 instruction set) and ARM_64 (ARMv8 64-bit CPU).