| Column Name | Column Data Type |
|---|
processing_job_name Required Input Column
The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account. | VARCHAR |
_aws_profile Input Column
The AWS profile defines the AWS identity used. It can be defined via credentials or by assuming a IAM role. | STRUCT(
"type" VARCHAR,
"name" VARCHAR,
"account_id" VARCHAR,
"via_profile_name" VARCHAR,
"assumed_role_arn" VARCHAR,
"organization" STRUCT(
"account_name" VARCHAR,
"id" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[],
"master_account" STRUCT(
"id" VARCHAR,
"email" VARCHAR
),
"parents" STRUCT(
"type" VARCHAR,
"id" VARCHAR,
"name" VARCHAR,
"tags" STRUCT(
"key" VARCHAR,
"value" VARCHAR
)[]
)[]
)
) |
Show child fields- _aws_profile.account_id
The AWS account id - _aws_profile.assumed_role_arn
The ARN of the assumed role - _aws_profile.name
The unique name of the profile. - _aws_profile.organization
Information about this profile's membership in the AWS organization. Show child fields- _aws_profile.organization.account_name
The name of account speciifed by the organization - _aws_profile.organization.id
The organization id - _aws_profile.organization.master_account
Show child fields- _aws_profile.organization.master_account.email
The organization master account email address - _aws_profile.organization.master_account.id
The organization master account id
- _aws_profile.organization.parents[]
Show child fields- _aws_profile.organization.parents[].id
The id of the parent - _aws_profile.organization.parents[].name
The name of the parent - _aws_profile.organization.parents[].tags[]
Show child fields- _aws_profile.organization.parents[].tags[].key
- _aws_profile.organization.parents[].tags[].value
- _aws_profile.organization.parents[].type
The type of parent can be an organization unit or a root
- _aws_profile.organization.tags[]
Show child fields- _aws_profile.organization.tags[].key
- _aws_profile.organization.tags[].value
- _aws_profile.type
The type of profile, either 'credentials' or 'assumed_role' - _aws_profile.via_profile_name
This IAM role for this profile is assumed by first utilizing another profile with this name to obtain credentials.
|
app_specification
Configures the processing job to run a specified container image. | STRUCT(
"image_uri" VARCHAR,
"container_entrypoint" VARCHAR[],
"container_arguments" VARCHAR[]
) |
Show child fields- app_specification.container_arguments[]
- app_specification.container_entrypoint[]
- app_specification.image_uri
The container image to be run by the processing job.
|
auto_ml_job_arn
The ARN of an AutoML job associated with this processing job. | VARCHAR |
creation_time
The time at which the processing job was created. | TIMESTAMP_S |
environment
The environment variables set in the Docker container. | MAP(VARCHAR, VARCHAR) |
exit_message
An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits. | VARCHAR |
experiment_config
The configuration information used to create an experiment. | STRUCT(
"experiment_name" VARCHAR,
"trial_name" VARCHAR,
"trial_component_display_name" VARCHAR,
"run_name" VARCHAR
) |
Show child fields- experiment_config.experiment_name
The name of an existing experiment to associate with the trial component. - experiment_config.run_name
The name of the experiment run to associate with the trial component. - experiment_config.trial_component_display_name
The display name for the trial component. If this key isn't specified, the display name is the trial component name. - experiment_config.trial_name
The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
|
failure_reason
A string, up to one KB in size, that contains the reason a processing job failed, if it failed. | VARCHAR |
last_modified_time
The time at which the processing job was last modified. | TIMESTAMP_S |
monitoring_schedule_arn
The ARN of a monitoring schedule for an endpoint associated with this processing job. | VARCHAR |
network_config
Networking options for a processing job. | STRUCT(
"enable_inter_container_traffic_encryption" BOOLEAN,
"enable_network_isolation" BOOLEAN,
"vpc_config" STRUCT(
"security_group_ids" VARCHAR[],
"subnets" VARCHAR[]
)
) |
Show child fields- network_config.enable_inter_container_traffic_encryption
Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer. - network_config.enable_network_isolation
Whether to allow inbound and outbound network calls to and from the containers used for the processing job. - network_config.vpc_config
Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC. Show child fields- network_config.vpc_config.security_group_ids[]
- network_config.vpc_config.subnets[]
|
processing_end_time
The time at which the processing job completed. | TIMESTAMP_S |
processing_inputs
The inputs for a processing job. | STRUCT(
"input_name" VARCHAR,
"app_managed" BOOLEAN,
"s3_input" STRUCT(
"s3_uri" VARCHAR,
"local_path" VARCHAR,
"s3_data_type" VARCHAR,
"s3_input_mode" VARCHAR,
"s3_data_distribution_type" VARCHAR,
"s3_compression_type" VARCHAR
),
"dataset_definition" STRUCT(
"athena_dataset_definition" STRUCT(
"catalog" VARCHAR,
"database" VARCHAR,
"query_string" VARCHAR,
"work_group" VARCHAR,
"output_s3_uri" VARCHAR,
"kms_key_id" VARCHAR,
"output_format" VARCHAR,
"output_compression" VARCHAR
),
"redshift_dataset_definition" STRUCT(
"cluster_id" VARCHAR,
"database" VARCHAR,
"db_user" VARCHAR,
"query_string" VARCHAR,
"cluster_role_arn" VARCHAR,
"output_s3_uri" VARCHAR,
"kms_key_id" VARCHAR,
"output_format" VARCHAR,
"output_compression" VARCHAR
),
"local_path" VARCHAR,
"data_distribution_type" VARCHAR,
"input_mode" VARCHAR
)
)[] |
Show child fields- processing_inputs[]
Show child fields- processing_inputs[].app_managed
When True, input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker. - processing_inputs[].dataset_definition
Configuration for a Dataset Definition input. Show child fields- processing_inputs[].dataset_definition.athena_dataset_definition
Configuration for Athena Dataset Definition input. Show child fields- processing_inputs[].dataset_definition.athena_dataset_definition.catalog
The name of the data catalog used in Athena query execution. - processing_inputs[].dataset_definition.athena_dataset_definition.database
The name of the database used in the Athena query execution. - processing_inputs[].dataset_definition.athena_dataset_definition.kms_key_id
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution. - processing_inputs[].dataset_definition.athena_dataset_definition.output_compression
The compression used for Athena query results. - processing_inputs[].dataset_definition.athena_dataset_definition.output_format
The data storage format for Athena query results. - processing_inputs[].dataset_definition.athena_dataset_definition.output_s3_uri
The location in Amazon S3 where Athena query results are stored. - processing_inputs[].dataset_definition.athena_dataset_definition.query_string
The SQL query statements, to be executed. - processing_inputs[].dataset_definition.athena_dataset_definition.work_group
The name of the workgroup in which the Athena query is being started.
- processing_inputs[].dataset_definition.data_distribution_type
Whether the generated dataset is FullyReplicated or ShardedByS3Key (default). - processing_inputs[].dataset_definition.input_mode
Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume. - processing_inputs[].dataset_definition.local_path
The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default). - processing_inputs[].dataset_definition.redshift_dataset_definition
Configuration for Redshift Dataset Definition input. Show child fields- processing_inputs[].dataset_definition.redshift_dataset_definition.cluster_id
The Redshift cluster Identifier. - processing_inputs[].dataset_definition.redshift_dataset_definition.cluster_role_arn
The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets. - processing_inputs[].dataset_definition.redshift_dataset_definition.database
The name of the Redshift database used in Redshift query execution. - processing_inputs[].dataset_definition.redshift_dataset_definition.db_user
The database user name used in Redshift query execution. - processing_inputs[].dataset_definition.redshift_dataset_definition.kms_key_id
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution. - processing_inputs[].dataset_definition.redshift_dataset_definition.output_compression
The compression used for Redshift query results. - processing_inputs[].dataset_definition.redshift_dataset_definition.output_format
The data storage format for Redshift query results. - processing_inputs[].dataset_definition.redshift_dataset_definition.output_s3_uri
The location in Amazon S3 where the Redshift query results are stored. - processing_inputs[].dataset_definition.redshift_dataset_definition.query_string
The SQL query statements to be executed.
- processing_inputs[].input_name
The name for the processing job input. - processing_inputs[].s3_input
Configuration for downloading input data from Amazon S3 into the processing container. Show child fields- processing_inputs[].s3_input.local_path
The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/. LocalPath is a required parameter when AppManaged is False (default). - processing_inputs[].s3_input.s3_compression_type
Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume. - processing_inputs[].s3_input.s3_data_distribution_type
Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance. - processing_inputs[].s3_input.s3_data_type
Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job. - processing_inputs[].s3_input.s3_input_mode
Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume. - processing_inputs[].s3_input.s3_uri
The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
|
processing_job_arn
The Amazon Resource Name (ARN) of the processing job. | VARCHAR |
processing_job_status
Provides the status of a processing job. | VARCHAR |
processing_output_config
Output configuration for the processing job. | STRUCT(
"outputs" STRUCT(
"output_name" VARCHAR,
"s3_output" STRUCT(
"s3_uri" VARCHAR,
"local_path" VARCHAR,
"s3_upload_mode" VARCHAR
),
"feature_store_output" STRUCT(
"feature_group_name" VARCHAR
),
"app_managed" BOOLEAN
)[],
"kms_key_id" VARCHAR
) |
Show child fields- processing_output_config.kms_key_id
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs. - processing_output_config.outputs[]
Show child fields- processing_output_config.outputs[].app_managed
When True, output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker. - processing_output_config.outputs[].feature_store_output
Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified. Show child fields- processing_output_config.outputs[].feature_store_output.feature_group_name
The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
- processing_output_config.outputs[].output_name
The name for the processing job output. - processing_output_config.outputs[].s3_output
Configuration for processing job outputs in Amazon S3. Show child fields- processing_output_config.outputs[].s3_output.local_path
The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked. - processing_output_config.outputs[].s3_output.s3_upload_mode
Whether to upload the results of the processing job continuously or after the job completes. - processing_output_config.outputs[].s3_output.s3_uri
A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
|
processing_resources
Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance. | STRUCT(
"cluster_config" STRUCT(
"instance_count" BIGINT,
"instance_type" VARCHAR,
"volume_size_in_gb" BIGINT,
"volume_kms_key_id" VARCHAR
)
) |
Show child fields- processing_resources.cluster_config
The configuration for the resources in a cluster used to run the processing job. Show child fields- processing_resources.cluster_config.instance_count
The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1. - processing_resources.cluster_config.instance_type
The ML compute instance type for the processing job. - processing_resources.cluster_config.volume_kms_key_id
The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job. Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes. - processing_resources.cluster_config.volume_size_in_gb
The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario. Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes.
|
processing_start_time
The time at which the processing job started. | TIMESTAMP_S |
role_arn
The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf. | VARCHAR |
stopping_condition
The time limit for how long the processing job is allowed to run. | STRUCT(
"max_runtime_in_seconds" BIGINT
) |
Show child fields- stopping_condition.max_runtime_in_seconds
Specifies the maximum runtime in seconds.
|
training_job_arn
The ARN of a training job associated with this processing job. | VARCHAR |