Package 'sagemaker.common'

(str): The name of the file to save to.

(bool): Set to True to fetch the latest data from SageMaker API.

Whether to make a deep clone.

(str, default=None): The name of the data catalog used in Athena query execution.

(str, default=None): The name of the database used in the Athena query execution.

(str, default=None): The SQL query statements, to be executed.

(str, default=None): The location in Amazon S3 where Athena query results are stored.

(str, default=None): The name of the workgroup in which the Athena query is being started.

(str, default=None): The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.

(str, default=None): The data storage format for Athena query results. Valid options are "PARQUET", "ORC", "AVRO", "JSON", "TEXTFILE"

(str, default=None): The compression used for Athena query results. Valid options are "GZIP", "SNAPPY", "ZLIB"

Whether to make a deep clone.

(Any): List of label values or threshold to indicate positive outcome used for bias metrics.

(str): Sensitive attribute in the input data for which we like to compare metrics.

(list): Optional list of values to form a sensitive group or threshold for a numeric facet column that defines the lower bound of a sensitive group. Defaults to considering each possible value as sensitive group and computing metrics vs all the other examples.

(str): Optional column name or index to indicate a group column to be used for the bias metric 'Conditional Demographic Disparity in Labels - CDDL' or 'Conditional Demographic Disparity in Predicted Labels - CDDPL'.

Whether to make a deep clone.

(str): Required. The name of the collection configuration.

(dict): Optional. The parameters for the collection configuration.

Whether to make a deep clone.

(str): Dataset S3 prefix/object URI.

(str): S3 prefix to store the output.

(str): S3 prefix to store the analysis_config output If this field is None, then the s3_output_path will be used to store the analysis_config output

(str): Target attribute of the model required by bias metrics (optional for SHAP) Specified as column name or index for CSV dataset, or as JSONPath for JSONLines.

(list[str]): A list of column names in the input dataset.

(str): JSONPath for locating the feature columns for bias metrics if the dataset format is JSONLines.

(str): Format of the dataset. Valid values are "text/csv" for CSV and "application/jsonlines" for JSONLines.

(str): Valid options are "FullyReplicated" or "ShardedByS3Key".

(str): Valid options are "None" or "Gzip".

(str): The name or index of the column in the dataset that acts as an identifier column (for instance, while performing a join). This column is only used as an identifier, and not used for any other computations. This is an optional field in all cases except when the dataset contains more than one file, and 'save_local_shap_values' is set to true in SHAPConfig.

Whether to make a deep clone.

(int): The step to start profiling. The default is step 7.

(int): The number of steps to profile. The default is for 1 step.

(int): The Unix time to start profiling. The default is for 1 step.

(float): The duration in seconds to profile.

(bool): Indicates whether the default config should be used.

(str): Regex pattern

Whether to make a deep clone.

(str, default="ShardedByS3Key"): Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).

(str, default="File"): 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.

(str, default=None): 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).

(:class:sagemaker.common::RedshiftDatasetDefinition, default=None): Configuration for Redshift Dataset Definition input.

(:class:sagemaker.common::AthenaDatasetDefinition, default=None): Configuration for Athena Dataset Definition input.

Whether to make a deep clone.

(str): Optional. The location in Amazon S3 to store the output tensors. The default Debugger output path is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.

(str): Optional. The local path in the container.

(dict): Optional. A dictionary of parameters.

([sagemaker.debugger.CollectionConfig]): Required. A list of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved at the s3_output_path.

Whether to make a deep clone.

(int): The step to start profiling. The default is step 5.

(int): The number of steps to profile. The default is for 1 step.

(int): The Unix time to start profiling.

(float): The duration in seconds to profile.

(bool): Indicates whether the default config should be used.

Whether to make a deep clone.

(MetricsSource): A metric source object that represents

(MetricsSource): A metric source object that represents

(MetricsSource): A metric source object that represents

(MetricsSource): A metric source object that represents

(FileSource): A file source object that represents bias config

(MetricsSource):

(MetricsSource):

(MetricsSource):

(FileSource): A file source object that represents

Whether to make a deep clone.

(str, optional): Name of the experiment if you want to constrain the search to only trial components belonging to an experiment.

(dict, optional): The search query to find the set of trial components to use to populate the data frame.

(str, optional): The name of the resource property used to sort the set of trial components.

(str optional): How trial components are ordered, valid values are Ascending and Descending. The default is Descending.

(list, optional): string names of all the metrics to be shown in the data frame. If not specified, all metrics will be shown of all trials.

(list, optional): string names of the parameters to be shown in the data frame. If not specified, all parameters will be shown of all trials.

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.

Whether to make a deep clone.

Whether to make a deep clone.

Whether to make a deep clone.

(type): A subclass of the :class:'~sagemaker.estimator.Framework' estimator

(str): The version of the framework. Value is ignored when “image_uri“ is provided.

(str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses this role to access AWS resources, such as data stored in Amazon S3.

(int): The number of instances to run a processing job with.

(str): The type of EC2 instance to use for processing, for example, 'ml.c4.xlarge'.

(str): Python version you want to use for executing your model training code. One of 'py2' or 'py3'. Defaults to 'py3'. Value is ignored when “image_uri“ is provided.

(str): The URI of the Docker image to use for the processing jobs (default: None).

([str]): The command to run, along with any command-line flags to *precede* the “'code script“'. Example: ["python3", "-v"]. If not provided, ["python"] will be chosen (default: None).

(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).

(str): A KMS key for the processing volume (default: None).

(str): The KMS key ID for processing job outputs (default: None).

(str): The S3 prefix URI where custom code will be uploaded (default: None). The code file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'. If not specified, the default “code location“ is 's3://sagemaker-default-bucket'

(int): Timeout in seconds (default: None). After this amount of time, Amazon SageMaker terminates the job, regardless of its current status. If 'max_runtime_in_seconds' is not specified, the default value is 24 hours.

(str): Prefix for processing name. If not specified, the processor generates a default job name, based on the processing image name and current timestamp (default: None).

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain (default: None).

(dict[str, str]): Environment variables to be passed to the processing jobs (default: None).

(list[dict]): List of tags to be passed to the processing job (default: None). For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(:class:'~sagemaker.network.NetworkConfig'): A :class:'~sagemaker.network.NetworkConfig' object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets (default: None).

(str): This can be an S3 URI or a local path to a file with the framework script to run. See the “code“ argument in 'sagemaker.processing.FrameworkProcessor.run()'.

(str): Path (absolute, relative, or an S3 URI) to a directory wit any other processing source code dependencies aside from the entrypoint file (default: None). See the “source_dir“ argument in 'sagemaker.processing.FrameworkProcessor.run()'

(list[str]): A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: []). See the “dependencies“ argument in 'sagemaker.processing.FrameworkProcessor.run()'.

(dict[str, str]): Git configurations used for cloning files. See the 'git_config' argument in 'sagemaker.processing.FrameworkProcessor.run()'.

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: None).

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: None).

(list[str]): A list of string arguments to be passed to a processing job (default: None).

(str): Processing job name. If not specified, the processor generates a default job name, based on the base job name and current timestamp.

(str): This can be an S3 URI or a local path to a file with the framework script to run.Path (absolute or relative) to the local Python source file which should be executed as the entry point to training. When 'code' is an S3 URI, ignore 'source_dir', 'dependencies, and 'git_config'. If “source_dir“ is specified, then “code“ must point to a file located at the root of “source_dir“.

(str): Path (absolute, relative or an S3 URI) to a directory with any other processing source code dependencies aside from the entry point file (default: None). If “source_dir“ is an S3 URI, it must point to a tar.gz file. Structure within this directory are preserved when processing on Amazon SageMaker (default: None).

(list[str]): A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container (default: []). The library folders will be copied to SageMaker in the same folder where the entrypoint is copied. If 'git_config' is provided, 'dependencies' should be a list of relative locations to directories with any additional libraries needed in the Git repo (default: None).

(dict[str, str]): Git configurations used for cloning files, including “repo“, “branch“, “commit“, “2FA_enabled“, “username“, “password“ and “token“. The “repo“ field is required. All other fields are optional. “repo“ specifies the Git repository where your training script is stored. If you don't provide “branch“, the default value 'master' is used. If you don't provide “commit“, the latest commit in the specified branch is used. results in cloning the repo specified in 'repo', then checkout the 'master' branch, and checkout the specified commit. “2FA_enabled“, “username“, “password“ and “token“ are used for authentication. For GitHub (or other Git) accounts, set “2FA_enabled“ to 'True' if two-factor authentication is enabled for the account, otherwise set it to 'False'. If you do not provide a value for “2FA_enabled“, a default value of 'False' is used. CodeCommit does not support two-factor authentication, so do not provide "2FA_enabled" with CodeCommit repositories. For GitHub and other Git repos, when SSH URLs are provided, it doesn't matter whether 2FA is enabled or disabled; you should either have no passphrase for the SSH key pairs, or have the ssh-agent configured so that you will not be prompted for SSH passphrase when you do 'git clone' command with SSH URLs. When HTTPS URLs are provided: if 2FA is disabled, then either token or username+password will be used for authentication if provided (token prioritized); if 2FA is enabled, only token will be used for authentication if provided. If required authentication info is not provided, python SDK will try to use local credentials storage to authenticate. If that fails either, an error message will be thrown. For CodeCommit repos, 2FA is not supported, so '2FA_enabled' should not be provided. There is no token in CodeCommit, so 'token' should not be provided too. When 'repo' is an SSH URL, the requirements are the same as GitHub-like repos. When 'repo' is an HTTPS URL, username+password will be used for authentication if they are provided; otherwise, python SDK will try to use either CodeCommit credential helper or local credential storage for authentication.

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: None).

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: None).

(list[str]): A list of string arguments to be passed to a processing job (default: None).

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when wait is True (default: True).

(str): Processing job name. If not specified, the processor generates a default job name, based on the base job name and current timestamp.

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

(str): The ARN of the KMS key that is used to encrypt the user code file (default: None).

Whether to make a deep clone.

(str):

(int):

(int):

(int):

(DetailedProfilingConfig): The configuration for detailed profiling. Configure it using the :class:'~sagemaker.debugger.metrics_config.DetailedProfilingConfig' class. Pass “DetailedProfilingConfig()“ to use the default configuration.

(DataloaderProfilingConfig): The configuration for dataloader metrics profiling. Configure it using the :class:'~sagemaker.debugger.metrics_config.DataloaderProfilingConfig' class. Pass “DataloaderProfilingConfig()“ to use the default configuration.

(PythonProfilingConfig): The configuration for stats collected by the Python profiler (cProfile or Pyinstrument). Configure it using the :class:'~sagemaker.debugger.metrics_config.PythonProfilingConfig' class. Pass “PythonProfilingConfig()“ to use the default configuration.

:

:

(int): The step at which to start profiling.

(int): The number of steps to profile.

(int): The Unix time at which to start profiling.

(float): The duration in seconds to profile.

Whether to make a deep clone.

(int): The step to start profiling. The default is step 13.

(int): The number of steps to profile. The default is for 1 steps.

(int): The Unix time to start profiling.

(float): The duration in seconds to profile.

(bool): Indicates whether the default config should be used.

Whether to make a deep clone.

(str): name of the HyperparameterTuningJob to analyze.

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.

(bool): Set to True to fetch the latest data from SageMaker API.

(bool): Set to True to fetch the latest data from SageMaker API.

Whether to make a deep clone.

(str): Specifies the type of CV model. Options: (IMAGE_CLASSIFICATION | OBJECT_DETECTION).

(None or int): Clarify uses SKLearn's SLIC method for image segmentation to generate features/superpixels. num_segments specifies approximate number of segments to be generated. Default is None. SLIC will default to 100 segments.

(NULL or str): method used for extracting features from the image.ex. "segmentation". Default is segmentation.

(NULL or float): Balances color proximity and space proximity. Higher values give more weight to space proximity, making superpixel shapes more square/cubic. We recommend exploring possible values on a log scale, e.g., 0.01, 0.1, 1, 10, 100, before refining around a chosen value.

(NULL or int): maximum number of objects displayed. Object detection algorithm may detect more than max_objects number of objects in a single image. The top max_objects number of objects according to confidence score will be displayed.

(NULL or float): minimum intersection over union for the object bounding box to consider its confidence score for computing SHAP values [0.0, 1.0]. This parameter is used for the object detection case.

(NULL or float): refers to the portion of the image outside of the bounding box. Scale is [0.0, 1.0]. If set to 1.0, whole image is considered, if set to 0.0 only the image inside bounding box is considered.

Whether to make a deep clone.

(str): The arn of the Lambda function.

(str): The name of the Lambda function. Function name must be provided to create a Lambda function.

(str): The role to be attached to Lambda function.

(str): The path of the zipped code package of the Lambda function.

(str): The bucket where zipped code is uploaded. If not provided, default session bucket is used to upload zipped_code_dir.

(str): The path of Lambda function script for direct zipped upload

(str): The Lambda handler. The format for handler should be file_name.function_name. For ex: if the name of the Lambda script is hello_world.py and Lambda function definition in that script is

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, new session is created.

(int): Timeout of the Lambda function in seconds. Default is 120 seconds.

(int): Memory of the Lambda function in megabytes. Default is 128 MB.

(str): Runtime of the Lambda function. Default is set to python3.8.

context), the handler should be hello_world.lambda_handler

Whether to make a deep clone.

(str): The name of the metrics config.

(int): The step to start profiling.

(int): The number of steps to profile.

(int): The Unix time to start profiling.

(float): The duration in seconds to profile.

Whether to make a deep clone.

(str): Model name (as created by 'CreateModel').

(int): The number of instances of a new endpoint for model inference.

(str): The type of EC2 instance to use for model inference, for example, 'ml.c5.xlarge'.

(str): The model output format to be used for getting inferences with the shadow endpoint. Valid values are "text/csv" for CSV and "application/jsonlines". Default is the same as content_type.

(str): The model input format to be used for getting inferences with the shadow endpoint. Valid values are "text/csv" for CSV and "application/jsonlines". Default is the same as dataset format.

(str): A template string to be used to construct the model input from dataset instances. It is only used when "model_content_type" is "application/jsonlines". The template should have one and only one placeholder $features which will be replaced by a features list for to form the model inference input.

(str): Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The information is an opaque value that is forwarded verbatim. You could use this value, for example, to provide an ID that you can use to track a request or to provide other metadata that a service endpoint was programmed to process. The value must consist of no more than 1024 visible US-ASCII characters as specified in Section 3.3.6. Field Value Components ( https://tools.ietf.org/html/rfc7230#section-3.2.6) of the Hypertext Transfer Protocol (HTTP/1.1).

(str): The Elastic Inference accelerator type to deploy to the model endpoint instance for making inferences to the model, see https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html.

(str): The endpoint name prefix of a new endpoint. Must follow pattern "^[a-zA-Z0-9](-\*[a-zA-Z0-9]".

Whether to make a deep clone.

(str or [integer] or list[integer]): Optional index or JSONPath location in the model output for the prediction. In case, this is a predicted label of the same type as the label in the dataset no further arguments need to be specified.

(str or [integer] or list[integer]): Optional index or JSONPath location in the model output for the predicted scores.

(float): An optional value for binary prediction tasks in which the model returns a probability, to indicate the threshold to convert the prediction to a boolean value. Default is 0.5.

(list): List of label values - one for each score of the “probability“.

Whether to make a deep clone.

(bool): Boolean that determines whether to enable network isolation.

([str]): A list of strings representing security group IDs.

([str]): A list of strings representing subnets.

(bool): Boolean that determines whether to encrypt inter-container traffic. Default value is None.

Whether to make a deep clone.

(None or list): List of features names or indices for which partial dependence plots must be computed and plotted. When ShapConfig is provided, this parameter is optional as Clarify will try to compute the partial dependence plots for top feature based on SHAP attributions. When ShapConfig is not provided, 'features' must be provided.

(int): In case of numerical features, this number represents that number of buckets that range of values must be divided into. This decides the granularity of the grid in which the PDP are plotted.

(int): Set the number of top SHAP attributes to be selected to compute partial dependence plots.

Whether to make a deep clone.

(str): The source for the input. If a local path is provided, it will automatically be uploaded to S3 under: "s3://<default-bucket-name>/<job-name>/input/<input-name>".

(str): The destination of the input.

(str): The name for the input. If a name is not provided, one will be generated (eg. "input-1").

(str): Valid options are "ManifestFile" or "S3Prefix".

(str): Valid options are "Pipe" or "File".

(str): Valid options are "FullyReplicated" or "ShardedByS3Key".

(str): Valid options are "None" or "Gzip".

(:class:'~sagemaker.dataset_definition.S3Input') Metadata of data objects stored in S3

(:class:'~sagemaker.dataset_definition.DatasetDefinition') DatasetDefinition input

(bool): Whether the input are managed by SageMaker or application

Whether to make a deep clone.

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.

(str): Name of the Processing job.

(list[:class:'~sagemaker.processing.ProcessingInput']): A list of :class:'~sagemaker.processing.ProcessingInput' objects.

(list[:class:'~sagemaker.processing.ProcessingOutput']): A list of :class:'~sagemaker.processing.ProcessingOutput' objects.

(str): The output KMS key associated with the job (default: None).

(:class:'~sagemaker.processing.Processor'): The “Processor“ instance that started the job.

(list[:class:'~sagemaker.processing.ProcessingInput']): A list of :class:'~sagemaker.processing.ProcessingInput' objects.

(list[:class:'~sagemaker.processing.ProcessingOutput']): A list of :class:'~sagemaker.processing.ProcessingOutput' objects.

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.

(str): Name of the processing job.

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.

(str): ARN of the processing job.

(bool): Whether to show the logs produced by the job (default: True).

(list[str]): The arguments for a container used to run a processing job.

(list[str]): The entrypoint for a container used to run a processing job.

(str): The container image to be run by the processing job.

(str): The AWS Key Management Service (AWS 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.

(list[dict]): Output configuration information for a processing job.

(int): 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.

(str): The ML compute instance type for the processing job.

(str): The AWS Key Management Service (AWS 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.

(int): The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.

(int): Specifies the maximum runtime in seconds.

Whether to make a deep clone.

(str): The source for the output.

(str): The destination of the output. If a destination is not provided, one will be generated: "s3://<default-bucket-name>/<job-name>/output/<output-name>".

(str): The name of the output. If a name is not provided, one will be generated (eg. "output-1").

(str): Valid options are "EndOfJob" or "Continuous". s3_upload_mode (str): Valid options are "EndOfJob" or "Continuous".

(bool): Whether the input are managed by SageMaker or application

(:class:'~sagemaker.processing.FeatureStoreOutput') Configuration for processing job outputs of FeatureStore.

Whether to make a deep clone.

(str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses this role to access AWS resources, such as data stored in Amazon S3.

(str): The URI of the Docker image to use for the processing jobs.

(int): The number of instances to run a processing job with.

(str): The type of EC2 instance to use for processing, for example, 'ml.c4.xlarge'.

(list[str]): The entrypoint for the processing job (default: NULL). This is in the form of a list of strings that make a command.

(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).

(str): A KMS key for the processing volume (default: NULL).

(str): The KMS key ID for processing job outputs (default: NULL).

(int): Timeout in seconds (default: NULL). After this amount of time, Amazon SageMaker terminates the job, regardless of its current status. If 'max_runtime_in_seconds' is not specified, the default value is 24 hours.

(str): Prefix for processing job name. If not specified, the processor generates a default job name, based on the processing image name and current timestamp.

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.

(dict[str, str]): Environment variables to be passed to the processing jobs (default: NULL).

(list[dict]): List of tags to be passed to the processing job (default: NULL). For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(:class:'~sagemaker.network.NetworkConfig'): A :class:'~sagemaker.network.NetworkConfig' object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: NULL).

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: NULL).

(list[str]): A list of string arguments to be passed to a processing job (default: NULL).

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when “wait“ is True (default: True).

(str): Processing job name. If not specified, the processor generates a default job name, based on the base job name and current timestamp.

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

Whether to make a deep clone.

(str): The location in Amazon S3 to store the output. The default Debugger output path for profiling data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/profiler-output/.

(int): The time interval in milliseconds to collect system metrics. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default is 500 milliseconds.

(:class:'~sagemaker.debugger.FrameworkProfile'): A parameter object for framework metrics profiling. Configure it using the :class:'~sagemaker.debugger.FrameworkProfile' class. To use the default framework profile parameters, pass “FrameworkProfile()“. For more information about the default values, see :class:'~sagemaker.debugger.FrameworkProfile'.

Whether to make a deep clone.

(sagemaker.debugger::ProfilerReport): The base rule configuration object returned from the sagemaker.debugger method. For example, sagemaker.debugger::ProfilerReport$new(). For a full list of built-in rules for debugging, see 'List of Debugger Built-in Rules' https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html.

(str): The name of the profiler rule. If one is not provided, the name of the base_config will be used.

(str): The path in the container.

(str): The location in Amazon S3 to store the profiling output data. The default Debugger output path for profiling data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/profiler-output/.

(str): The name of the profiler rule.

(str): The URI of the image to be used by the proflier rule.

(str): Type of EC2 instance to use, for example, 'ml.c4.xlarge'.

(int): Size in GB of the EBS volume to use for storing data.

(str): A source file containing a rule to invoke. If provided, you must also provide rule_to_invoke. This can either be an S3 uri or a local path.

(str): The name of the rule to invoke within the source. If provided, you must also provide the source.

(str): The path in the container.

(str): The location in Amazon S3 to store the output. The default Debugger output path for profiling data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/profiler-output/.

(dict): A dictionary of parameters for the rule.

Whether to make a deep clone.

(int): The step to start profiling. The default is step 9.

(int): The number of steps to profile. The default is for 3 steps.

(int): The Unix time to start profiling.

(float): The duration in seconds to profile.

(bool): Indicates whether the default configuration should be used. If set to 'True', Python profiling will be done at step 9, 10, and 11 of training, using cProfiler and collecting metrics based on the total time, cpu time, and off cpu time for these three steps respectively. The default is “True“.

(PythonProfiler): The Python profiler to use to collect python profiling stats. Available options are “"cProfile"“ and “"Pyinstrument"“. The default is “"cProfile"“. Instead of passing the string values, you can also use the enumerator util, :class:'~sagemaker.debugger.utils.PythonProfiler', to choose one of the available options.

(cProfileTimer): The timer to be used by cProfile when collecting python profiling stats. Available options are “"total_time"“, “"cpu_time"“, and “"off_cpu_time"“. The default is “"total_time"“. If you choose Pyinstrument, this parameter is ignored. Instead of passing the string values, you can also use the enumerator util, :class:'~sagemaker.debugger.utils.cProfileTimer', to choose one of the available options.

Whether to make a deep clone.

(str, default=None): The Redshift cluster Identifier. database (str, default=None): The name of the Redshift database used in Redshift query execution.

(str, default=None): The name of the Redshift database used in Redshift query execution.

(str, default=None): The database user name used in Redshift query execution.

(str, default=None): The SQL query statements to be executed.

(str, default=None): The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.

(str, default=None): The location in Amazon S3 where the Redshift query results are stored.

(str, default=None): The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.

(str, default=None): The data storage format for Redshift query results. Valid options are "PARQUET", "CSV"

(str, default=None): The compression used for Redshift query results. Valid options are "None", "GZIP", "SNAPPY", "ZSTD", "BZIP2"

Whether to make a deep clone.

(str): The name of the rule.

(str): The image URI to use the rule.

(str): Type of EC2 instance to use. For example, 'ml.c4.xlarge'.

(str): The local path to store the Rule output.

(str): The location in S3 to store the output.

(int): Size in GB of the EBS volume to use for storing data.

(dict): A dictionary of parameters for the rule.

([sagemaker.debugger.CollectionConfig]): Optional. A list of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved.

:

(dict): Required. This is the base rule config dictionary returned from the :class:sagemaker.debugger method. For example, sagemaker.debugger::dead_relu(). For a full list of built-in rules for debugging, see 'List of Debugger Built-in Rules' https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html.

(str): Optional. The name of the debugger rule. If one is not provided, the name of the base_config will be used.

(str): Optional. The local path in the rule processing container.

(str): Optional. The location in Amazon S3 to store the output tensors. The default Debugger output path for debugging data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.

([str]): Optional. The Amazon S3 input paths of other trials to use the SimilarAcrossRuns rule.

(dict): Optional. A dictionary of parameters for the rule.

(:class:sagemaker.debugger::CollectionConfig): Optional. A list of :class:sagemaker.debugger::CollectionConfig objects to be saved.

:

(str): Required. The name of the debugger rule.

(str): Required. The URI of the image to be used by the debugger rule.

(str): Required. Type of EC2 instance to use, for example, 'ml.c4.xlarge'.

(int): Required. Size in GB of the EBS volume to use for storing data.

(str): Optional. A source file containing a rule to invoke. If provided, you must also provide rule_to_invoke. This can either be an S3 uri or a local path.

(str): Optional. The name of the rule to invoke within the source. If provided, you must also provide source.

(str): Optional. The local path in the container.

(str): Optional. The location in Amazon S3 to store the output tensors. The default Debugger output path for debugging data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.

([str]): Optional. The Amazon S3 input paths of other trials to use the SimilarAcrossRuns rule.

(dict): Optional. A dictionary of parameters for the rule.

([sagemaker.debugger.CollectionConfig]): Optional. A list of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved.

:

(str): The training job name. To be set as the default training job prefix for the StopTraining action if it is specified.

Whether to make a deep clone.

(str): The name of the rule.

(str): The image URI to use the rule.

(str): Type of EC2 instance to use. For example, 'ml.c4.xlarge'.

(str): The local path to store the Rule output.

(str): The location in S3 to store the output.

(int): Size in GB of the EBS volume to use for storing data.

(dict): A dictionary of parameters for the rule.

Whether to make a deep clone.

(str): This can be an S3 URI or a local path to a file with the framework script to run.

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: None).

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: None).

(list[str]): A list of string arguments to be passed to a processing job (default: None).

Whether to make a deep clone.

(str, default=None): the path to a specific S3 object or a S3 prefix

(str, default=None): the path to a local directory. If not provided, skips data download by SageMaker platform.

(str, default="S3Prefix"): Valid options are "ManifestFile" or "S3Prefix".

(str, default="File"): Valid options are "Pipe" or "File".

(str, default="FullyReplicated"): Valid options are "FullyReplicated" or "ShardedByS3Key".

(str, default=None): Valid options are "None" or "Gzip"

Whether to make a deep clone.

(str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses this role to access AWS resources, such as data stored in Amazon S3.

(int): The number of instances to run a processing job with.

(str): The type of EC2 instance to use for processing, for example, 'ml.c4.xlarge'.

(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).

(str): A KMS key for the processing volume (default: None).

(str): The KMS key ID for processing job outputs (default: None).

(int): Timeout in seconds (default: None). After this amount of time, Amazon SageMaker terminates the job, regardless of its current status. If 'max_runtime_in_seconds' is not specified, the default value is 24 hours.

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.

(dict[str, str]): Environment variables to be passed to the processing jobs (default: None).

(list[dict]): List of tags to be passed to the processing job (default: None). For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(:class:'~sagemaker.network.NetworkConfig'): A :class:'~sagemaker.network.NetworkConfig' object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.

(str): Processing job name prefix.

(str): Clarify version want to be used.

(:class:'~sagemaker.clarify.DataConfig'): Config of the input/output data.

(:class:'~sagemaker.clarify.BiasConfig'): Config of sensitive groups.

(str or list[str]): Selector of a subset of potential metrics:

• 'CI' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ci.html

• 'DPL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dpl.html

• 'KL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-kl.html

• 'JS' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-js.html

• 'LP' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-lp.html

• 'TVD' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-tvd.html

• 'KS' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ks.html

• 'CDDL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cdd.html

Defaults to computing all.

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when “wait“ is True (default: True).

(str): Processing job name. If not specified, a name is composed of "Clarify-Pretraining-Bias" and current timestamp.

(str): The ARN of the KMS key that is used to encrypt the user code file (default: None).

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

(:class:'~sagemaker.clarify.DataConfig'): Config of the input/output data.

(:class:'~sagemaker.clarify.BiasConfig'): Config of sensitive groups.

(:class:'~sagemaker.clarify.ModelConfig'): Config of the model and its endpoint to be created.

(:class:'~sagemaker.clarify.ModelPredictedLabelConfig'): Config of how to extract the predicted label from the model output.

(str or list[str]): Selector of a subset of potential metrics:

• 'CI' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ci.html

• 'DPL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dpl.html

• 'KL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-kl.html

• 'JS' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-js.html

• 'LP' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-lp.html

• 'TVD' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-tvd.html

• 'KS' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ks.html

• 'CDDL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cdd.html

Defaults to computing all.

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when “wait“ is True (default: True).

(str): Processing job name. If not specified, a name is composed of "Clarify-Posttraining-Bias" and current timestamp.

(str): The ARN of the KMS key that is used to encrypt the user code file (default: None).

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

(:class:'~sagemaker.clarify.DataConfig'): Config of the input/output data.

(:class:'~sagemaker.clarify.BiasConfig'): Config of sensitive groups.

(:class:'~sagemaker.clarify.ModelConfig'): Config of the model and its endpoint to be created.

(:class:'~sagemaker.clarify.ModelPredictedLabelConfig'): Config of how to extract the predicted label from the model output.

(str or list[str]): Selector of a subset of potential metrics:

• 'CI' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ci.html

• 'DPL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dpl.html

• 'KL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-kl.html

• 'JS' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-js.html

• 'LP' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-lp.html

• 'TVD' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-tvd.html

• 'KS' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ks.html

• 'CDDL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cdd.html

Defaults to computing all.

(str or list[str]): Selector of a subset of potential metrics:

• 'DPPL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dppl.html

• 'DI' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-di.html

• 'DCA' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dca.html

• 'DCR' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dcr.html

• 'RD' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-rd.html

• 'DAR' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-dar.html

• 'DRR' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-drr.html

• 'AD' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ad.html

• 'CDDPL' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-cddpl.html

• 'TE' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-te.html

• 'FT' https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-post-training-bias-metric-ft.html

Defaults to computing all.

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when “wait“ is True (default: True).

(str): Processing job name. If not specified, a name is composed of "Clarify-Bias" and current timestamp.

(str): The ARN of the KMS key that is used to encrypt the user code file (default: None).

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

(:class:'~sagemaker.clarify.DataConfig'): Config of the input/output data.

(:class:'~sagemaker.clarify.ModelConfig'): Config of the model and its endpoint to be created.

(:class:'~sagemaker.clarify.ExplainabilityConfig'): Config of the specific explainability method. Currently, only SHAP is supported.

: Index or JSONPath location in the model output for the predicted scores to be explained. This is not required if the model output is a single score.

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when “wait“ is True (default: True).

(str): Processing job name. If not specified, a name is composed of "Clarify-Explainability" and current timestamp.

(str): The ARN of the KMS key that is used to encrypt the user code file (default: None).

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

Whether to make a deep clone.

(str): An AWS IAM role name or ARN. Amazon SageMaker Processing uses this role to access AWS resources, such as data stored in Amazon S3.

(str): The URI of the Docker image to use for the processing jobs.

([str]): The command to run, along with any command-line flags. Example: ["python3", "-v"].

(int): The number of instances to run a processing job with.

(str): The type of EC2 instance to use for processing, for example, 'ml.c4.xlarge'.

(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).

(str): A KMS key for the processing volume (default: NULL).

(str): The KMS key ID for processing job outputs (default: NULL).

(int): Timeout in seconds (default: NULL). After this amount of time, Amazon SageMaker terminates the job, regardless of its current status. If 'max_runtime_in_seconds' is not specified, the default value is 24 hours.

(str): Prefix for processing name. If not specified, the processor generates a default job name, based on the processing image name and current timestamp.

(:class:'~sagemaker.session.Session'): Session object which manages interactions with Amazon SageMaker and any other AWS services needed. If not specified, the processor creates one using the default AWS configuration chain.

(dict[str, str]): Environment variables to be passed to the processing jobs (default: NULL).

(list[dict]): List of tags to be passed to the processing job (default: NULL). For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(:class:'~sagemaker.network.NetworkConfig'): A :class:'~sagemaker.network.NetworkConfig' object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.

(str): This can be an S3 URI or a local path to a file with the framework script to run.

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: None).

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: None).

(list[str]): A list of string arguments to be passed to a processing job (default: None).

(str): This can be an S3 URI or a local path to a file with the framework script to run.

(list[:class:'~sagemaker.processing.ProcessingInput']): Input files for the processing job. These must be provided as :class:'~sagemaker.processing.ProcessingInput' objects (default: NULL).

(list[:class:'~sagemaker.processing.ProcessingOutput']): Outputs for the processing job. These can be specified as either path strings or :class:'~sagemaker.processing.ProcessingOutput' objects (default: NULL).

(list[str]): A list of string arguments to be passed to a processing job (default: NULL).

(bool): Whether the call should wait until the job completes (default: True).

(bool): Whether to show the logs produced by the job. Only meaningful when wait is True (default: True).

(str): Processing job name. If not specified, the processor generates a default job name, based on the base job name and current timestamp.

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys: 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'.

(str): The ARN of the KMS key that is used to encrypt the user code file (default: None).

Whether to make a deep clone.

(int): Optional. The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB. If no value is provided, Amazon SageMaker will choose the default value for you. (Default: 2048)

(int): Optional. The maximum number of concurrent invocations your serverless endpoint can process. If no value is provided, Amazon SageMaker will choose the default value for you. (Default: 5)

Whether to make a deep clone.

(str or list): A list of rows (at least one) or S3 object URI to be used as the baseline dataset in the Kernel SHAP algorithm. The format should be the same as the dataset format. Each row should contain only the feature columns/values and omit the label column/values.

(int): Number of samples to be used in the Kernel SHAP algorithm. This number determines the size of the generated synthetic dataset to compute the SHAP values.

(str): Aggregation method for global SHAP values. Valid values are "mean_abs" (mean of absolute SHAP values for all instances), "median" (median of SHAP values for all instances) and "mean_sq" (mean of squared SHAP values for all instances).

(bool): Indicator of whether the logit function is to be applied to the model predictions. Default is False. If "use_logit" is true then the SHAP values will have log-odds units.

(bool): Indicator of whether to save the local SHAP values in the output location. Default is True.

(int): seed value to get deterministic SHAP values. Default is NULL.

(NULL or int): If a baseline is not provided, Clarify automatically computes a baseline dataset via a clustering algorithm (K-means/K-prototypes). num_clusters is a parameter for this algorithm. num_clusters will be the resulting size of the baseline dataset. If not provided, Clarify job will use a default value.

(:class:'~sagemaker.clarify.TextConfig'): Config to handle text features. Default is NULL

(:class:'~sagemaker.clarify.ImageConfig'): Config to handle image features. Default is NULL

Whether to make a deep clone.

(int): The step to start profiling. The default is step 15.

(int): The number of steps to profile. The default is for 1 steps.

(int): The Unix time to start profiling.

(float): The duration in seconds to profile.

(bool): Indicates whether the default configuration should be used.

Whether to make a deep clone.

(int): The step to start profiling.

(int): The number of steps to profile.

Whether to make a deep clone.

(str): Optional. The location in Amazon S3 to store the output.

(str): Optional. The local path in the container.

Whether to make a deep clone.

(str): Determines the granularity in which text features are broken down to, can be "token", "sentence", or "paragraph". Shap values are computed for these units.

(str): Specifies the language of the text features, can be "chinese", "danish", "dutch", "english", "french", "german", "greek", "italian", "japanese", "lithuanian", "multi-language", "norwegian bokmal", "polish", "portuguese", "romanian", "russian", "spanish", "afrikaans", "albanian", "arabic", "armenian", "basque", "bengali", "bulgarian", "catalan", "croatian", "czech", "estonian", "finnish", "gujarati", "hebrew", "hindi", "hungarian", "icelandic", "indonesian", "irish", "kannada", "kyrgyz", "latvian", "ligurian", "luxembourgish", "macedonian", "malayalam", "marathi", "nepali", "persian", "sanskrit", "serbian", "setswana", "sinhala", "slovak", "slovenian", "swedish", "tagalog", "tamil", "tatar", "telugu", "thai", "turkish", "ukrainian", "urdu", "vietnamese", "yoruba". Use "multi-language" for a mix of mulitple languages.

Whether to make a deep clone.

(int): The Unix time to start profiling.

(float): The duration in seconds to profile.

(ignored).

Whether to make a deep clone.

(str): name of the TrainingJob to analyze.

(list, optional): string names of all the metrics to collect for this training job. If not specified, then it will use all metric names configured for this job.

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is specified using the default AWS configuration chain.

:

:

:

Whether to make a deep clone.

(str): Name of the SageMaker model being used for the transform job.

(int): Number of EC2 instances to use.

(str): Type of EC2 instance to use, for example, 'ml.c4.xlarge'.

(str): The strategy used to decide how to batch records in a single request (default: None). Valid values: 'MultiRecord' and 'SingleRecord'.

(str): How the output is assembled (default: None). Valid values: 'Line' or 'None'.

(str): S3 location for saving the transform result. If not specified, results are stored to a default bucket.

(str): Optional. KMS key ID for encrypting the transform output (default: None).

(str): The accept header passed by the client to the inference endpoint. If it is supported by the endpoint, it will be the format of the batch transform output.

(int): The maximum number of HTTP requests to be made to each individual transform container at one time.

(int): Maximum size of the payload in a single HTTP request to the container in MB.

(list[dict]): List of tags for labeling a transform job (default: None). For more, see the SageMaker API documentation for 'Tag <https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html>'_.

(dict): Environment variables to be set for use during the transform job (default: None).

(str): Prefix for the transform job when the :meth:'~sagemaker.transformer.Transformer.transform' method launches. If not specified, a default prefix will be generated based on the training image name that was used to train the model associated with the transform job.

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one using the default AWS configuration chain.

(str): Optional. KMS key ID for encrypting the volume attached to the ML compute instance (default: None).

(str): Input data location in S3.

(str): What the S3 location defines (default: 'S3Prefix'). Valid values:

• 'S3Prefix' - the S3 URI defines a key name prefix. All objects with this prefix will be used as inputs for the transform job.

• 'ManifestFile' - the S3 URI points to a single manifest file listing each S3 object to use as an input for the transform job.

(str): MIME type of the input data (default: None).

(str): Compression type of the input data, if compressed (default: None). Valid values: 'Gzip', None.

(str): The record delimiter for the input object (default: 'None'). Valid values: 'None', 'Line', 'RecordIO', and 'TFRecord'.

(str): job name (default: None). If not specified, one will be generated.

(str): A JSONPath to select a portion of the input to pass to the algorithm container for inference. If you omit the field, it gets the value '$', representing the entire input. For CSV data, each row is taken as a JSON array, so only index-based JSONPaths can be applied, e.g. $[0], $[1:]. CSV data should follow the 'RFC format <https://tools.ietf.org/html/rfc4180>'_. See 'Supported JSONPath Operators <https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform-data-processing.html#data-processing-operators>'_ for a table of supported JSONPath operators. For more information, see the SageMaker API documentation for 'CreateTransformJob <https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTransformJob.html>'_. Some examples: "$[1:]", "$.features" (default: None).

(str): A JSONPath to select a portion of the joined/original output to return as the output. For more information, see the SageMaker API documentation for 'CreateTransformJob <https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTransformJob.html>'_. Some examples: "$[1:]", "$.prediction" (default: None).

(str): The source of data to be joined to the transform output. It can be set to 'Input' meaning the entire input record will be joined to the inference result. You can use OutputFilter to select the useful portion before uploading to S3. (default: None). Valid values: Input, None.

(dict[str, str]): Experiment management configuration. Dictionary contains three optional keys, 'ExperimentName', 'TrialName', and 'TrialComponentDisplayName'. (default: “None“).

(dict[str, str]): Model configuration. Dictionary contains two optional keys, 'InvocationsTimeoutInSeconds', and 'InvocationsMaxRetries'. (default: “None“).

(bool): Whether the call should wait until the job completes (default: TRUE).

(bool): Whether to show the logs produced by the job. Only meaningful when wait is True (default: TRUE).

Other parameters (currently not used)

return logs

wait for transform job

(str): Name for the transform job to be attached.

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one will be created using the default AWS configuration chain.

Whether to make a deep clone.