Package 'sagemaker.mlcore'

(str): algorithm arn used for training. Can be just the name if your account owns the algorithm.

(str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

(int): Number of Amazon EC2 instances to use for training.

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

(int): Size in GB of the EBS volume to use for storing input data during training (default: 30). Must be large enough to store training data if File Mode is used (which is the default).

(str): Optional. KMS key ID for encrypting EBS volume attached to the training instance (default: NULL).

(int): Timeout in seconds for training (default: 24 * 60 * 60). After this amount of time Amazon SageMaker terminates the job regardless of its current status.

(str): The input mode that the algorithm supports (default: 'File'). Valid modes: * 'File' - Amazon SageMaker copies the training dataset from the S3 location to a local directory. * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe. This argument can be overriden on a per-channel basis using “TrainingInput.input_mode“.

(str): S3 location for saving the training result (model artifacts and output files). If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the :meth:'~sagemaker.estimator.EstimatorBase.fit' method execution.

(str): Optional. KMS key ID for encrypting the training output (default: NULL).

(str): Prefix for training job name when the :meth:'~sagemaker.estimator.EstimatorBase.fit' method launches. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

(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.

(dict): Dictionary containing the hyperparameters to initialize this estimator with.

(list[dict]): List of tags for labeling a training job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(list[str]): List of subnet ids. If not specified training job will be created without VPC config.

(list[str]): List of security group ids. If not specified training job will be created without VPC config.

(str): URI where a pre-trained model is stored, either locally or in S3 (default: NULL). If specified, the estimator will create a channel pointing to the model so the training job can download it. This model can be a 'model.tar.gz' from a previous training job, or other artifacts coming from a different source. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html#td-deserialization

(str): Name of the channel where 'model_uri' will be downloaded (default: 'model').

(list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs.

(bool): Specifies whether traffic between training containers is encrypted for the training job (default: “False“).

: Additional kwargs. This is unused. It's only added for AlgorithmEstimator to ignore the irrelevant arguments.

model hyperparameters

(str): The “ExecutionRoleArn“ IAM Role ARN for the “Model“, which is also used during transform jobs. If not specified, the role from the Estimator will be used.

(RealTimePredictor): The predictor class to use when deploying the model.

(callable): Should accept a single argument, the input data, and return a sequence of bytes. May provide a content_type attribute that defines the endpoint request content type

(callable): Should accept two arguments, the result data and the response content type, and return a sequence of bytes. May provide a content_type attribute that defines the endpoint response Accept content type.

(dict[str, list[str]]): Optional override for VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids.

: Additional arguments for creating a :class:'~sagemaker.model.ModelPackage'. .. tip:: You can find additional parameters for using this method at :class:'~sagemaker.model.ModelPackage' and :class:'~sagemaker.model.Model'.

(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.

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

(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. If none specified, then the tags used for the training job are used for the transform job.

(str): The “ExecutionRoleArn“ IAM Role ARN for the “Model“, which is also used during transform jobs. If not specified, the role from the Estimator will be used.

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

(str or dict or TrainingInput): Information about the training data. This can be one of three types:

• (str) the S3 location where training data is saved, or a file:// path in local mode.

• (dict[str, str] or dict[str, TrainingInput]) If using multiple channels for training data, you can specify a dict mapping channel names to strings or :func:'~TrainingInput' objects.

• (TrainingInput) - channel configuration for S3 data sources that can provide additional information as well as the path to the training dataset. See :func:'TrainingInput' for full details.

• (sagemaker.session.FileSystemInput) - channel configuration for a file system data source that can provide additional information as well as the path to the training dataset.

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

([str]): A list of strings specifying which logs to print. Acceptable strings are "All", "NULL", "Training", or "Rules". To maintain backwards compatibility, boolean values are also accepted and converted to strings. Only meaningful when wait is True.

(str): Training job name. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

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

(ignored).

Whether to make a deep clone.

(str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

(int): Number of Amazon EC2 instances to use for training.

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

(str or None): The s3 prefix to upload RecordSet objects to, expressed as an S3 url. For example "s3://example-bucket/some-key-prefix/". Objects will be saved in a unique sub-directory of the specified location. If None, a default data location will be used.

(bool): Specifies whether container will run in network isolation mode. Network isolation mode restricts the container access to outside networks (such as the internet). Also known as internet-free mode (default: “False“).

: Additional parameters passed to :class:'~sagemaker.estimator.EstimatorBase'.

(:class:'~RecordSet'): The records to train this “Estimator“ on.

(int or None): The size of each mini-batch to use when training. If “None“, a default value will be used.

(str): Name of the training job to be created. If not specified, one is generated, using the base name given to the constructor if applicable.

(:class:'~RecordSet'): The records to train this “Estimator“ on

(int or None): The size of each mini-batch to use when training. If “None“, a default value will be used.

(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): Training job name. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

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

(numpy.ndarray): A 2D numpy array of training data.

(numpy.ndarray): A 1D numpy array of labels. Its length must be equal to the number of rows in “train“.

(str): The SageMaker TrainingJob channel this RecordSet should be assigned to.

(bool): Specifies whether the objects uploaded to S3 are encrypted on the server side using AES-256 (default: “False“).

([str]): A list of strings specifying which logs to print. Acceptable strings are "All", "NULL", "Training", or "Rules". To maintain backwards compatibility, boolean values are also accepted and converted to strings.

Whether to make a deep clone.

(botocore.response.StreamingBody): Data to be deserialized.

(str): The MIME type of the data.

Whether to make a deep clone.

(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.

(str): Name of the Amazon SageMaker Model Monitoring Baselining Job.

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

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

(str): The output kms key associated with the job. Defaults to None if not provided.

(sagemaker.processing.ProcessingJob): The ProcessingJob used for baselining instance.

(str): The name of the json-formatted statistics file

(str): The kms key to use when retrieving the file.

(str): The name of the json-formatted constraints file

(str): The kms key to use when retrieving the file.

Whether to make a deep clone.

(object): Data to be serialized

Whether to make a deep clone.

(sagemaker.clarify.BiasConfig): Config object related to bias configurations.

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

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

Whether to make a deep clone.

(raw): A stream of bytes.

(str): The MIME type of the data.

Whether to make a deep clone.

(list or object): The possible values for the hyperparameter. This input will be converted into a list of strings.

(str): The name of the hyperparameter.

(str): The name of the hyperparameter.

(object): Value of the hyperparameter

The value to be verified.

Whether to make a deep clone.

(BiasAnalysisConfig or ExplainabilityAnalysisConfig): analysis config from configurations of the baselining job.

(str): JSONpath to locate features in predictor request payload. Only required when predictor content type is JSONlines.

(str): Index, header or JSONpath to locate predicted label in predictor response payload.

(str): Index or JSONpath location in the model output for probabilities or scores to be used for explainability.

(float): Value to indicate the threshold to select the binary label in the case of binary classification. Default is 0.5.

Whether to make a deep clone.

(sagemaker.processing.ProcessingJob): The ProcessingJob used for baselining instance.

(str): Keep this parameter to align with method signature in super class, but it will be ignored.

(str): The kms key to use when retrieving the file.

Whether to make a deep clone.

(str): An AWS IAM role. The Amazon SageMaker jobs use this role.

(int): The number of instances to run the jobs with.

(str): Type of EC2 instance to use for the job, for example, 'ml.m5.xlarge'.

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

(str): A KMS key for the job's volume.

(str): The KMS key id for the job's outputs.

(int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600

(str): Prefix for the job name. If not specified, a default name is generated based on the training image name and current timestamp.

(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.

(dict): Environment variables to be passed to the job.

([dict]): List of tags to be passed to the job.

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

: Unused argument

: Unused argument

Whether to make a deep clone.

(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.

(str): The name of the monitoring execution job.

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

(sagemaker.Processing.ProcessingOutput): The output associated with the monitoring execution.

(str): The output kms key associated with the job. Defaults to None if not provided.

Whether to make a deep clone.

(str): The body of the constraints JSON file.

(str): The uri of the constraints JSON file.

(str): The kms key to be used to decrypt the file in S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(str): The uri of the constraints JSON file.

(str): The kms key to be used to decrypt the file in S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(str): The uri of the constraints JSON file.

(str): The kms key to be used to encrypt the file in S3.

(str): The file name to use when uploading to S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(str): The path to the constraints file.

(str): The kms_key to use when encrypting the file in S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(bool): Whether to enable monitoring or not.

(str): Sets the feature-level monitoring flag if provided. Otherwise, sets the file-level override.

Whether to make a deep clone.

(str): The body of the constraint violations JSON file.

(str): The uri of the constraint violations JSON file.

(str): The kms key to be used to decrypt the file in S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(str): The uri of the constraint violations JSON file.

(str): The kms key to be used to decrypt the file in S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(str): The uri of the constraint violations JSON file.

(str): The kms key to be used to encrypt the file in S3.

(str): The file name to use when uploading to S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(str): The path to the constraint violations file.

(str): The kms_key to use when encrypting the file in S3.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

Whether to make a deep clone.

(float): The minimum value for the range.

(float): The maximum value for the range.

(str): The scale used for searching the range during tuning (default: 'Auto'). Valid values: 'Auto', 'Linear', Logarithmic' and 'ReverseLogarithmic'.

Whether to make a deep clone.

(int): The hour in HH24 format (UTC) to run the job at, on a daily schedule.

(int): The hour interval to run the job at.

(int): The hour at which to begin in HH24 format (UTC).

Whether to make a deep clone.

(str): The string encoding to use (default: "UTF-8").

(union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that is expected from the inference endpoint (default: "text/csv").

raw data to be deserialize

(str): The MIME type of the data.

Whether to make a deep clone.

(str): The MIME type to signal to the inference endpoint when sending request data (default: "text/csv").

(object): Data to be serialized. Any list of same length vectors; e.g. data.frame and data.table. If matrix, it gets internally coerced to data.table preserving col names but not row names

Whether to make a deep clone.

(bool): Required. Whether data capture should be enabled or not.

(int): Optional. Default=20. The percentage of data to sample. Must be between 0 and 100.

(str): Optional. Defaults to "s3://<default-session-bucket>/ model-monitor/data-capture".

(str): Optional. Default=None. The kms key to use when writing to S3.

([str]): Optional. Must be a list containing any combination of the following values: "REQUEST", "RESPONSE". Default=["REQUEST", "RESPONSE"]. Denotes which data to capture between request and response.

([str]): Optional. Default=["text/csv"].

([str]): Optional. Default=["application/json"].

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

Whether to make a deep clone.

(bool): Whether the csv dataset to baseline and monitor has a header. Default: True.

(str): The position of the output columns. Must be one of ("START", "END"). Default: "START".

(bool): Whether the file should be read as a json object per line. Default: True.

Whether to make a deep clone.

(str): The string encoding to use (default: "UTF-8").

(union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that is expected from the inference endpoint (default: ("text/csv","application/json")).

(botocore.response.StreamingBody): Data to be deserialized.

(str): The MIME type of the data.

Whether to make a deep clone.

(str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.

(int): The number of instances to run the jobs with.

(str): Type of EC2 instance to use for the job, for example, 'ml.m5.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.

(str): The KMS key id for the job's outputs.

(int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600

(str): Prefix for the job name. If not specified, a default name is generated based on the training image name and current timestamp.

(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.

(dict): Environment variables to be passed to the job.

([dict]): List of tags to be passed to the job.

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

(str): The path to the baseline_dataset file. This can be a local path or an S3 uri.

(dict): The format of the baseline_dataset.

(str): The path to the record preprocessor script. This can be a local path or an S3 uri.

(str): The path to the record post-analytics processor script. This can be a local path or an S3 uri.

(str): Desired S3 destination Destination of the constraint_violations and statistics json files. Default: "s3://<default_session_bucket>/<job_name>/output"

(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 image name and current timestamp.

(str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor. This can either be the endpoint name or an EndpointInput.

(str): The path to the record preprocessor script. This can be a local path or an S3 uri.

(str): The path to the record post-analytics processor script. This can be a local path or an S3 uri.

(str): Desired S3 destination of the constraint_violations and statistics json files. Default: "s3://<default_session_bucket>/<job_name>/output"

(sagemaker.model_monitor.Constraints or str): If provided alongside statistics, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an s3_uri pointing to a constraints JSON file.

(sagemaker.model_monitor.Statistic or str): If provided alongside constraints, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an s3_uri pointing to a constraints JSON file.

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

(str): The cron expression that dictates the frequency that this job run. See sagemaker.model_monitor.CronExpressionGenerator for valid expressions. Default: Daily.

(bool): Whether to publish cloudwatch metrics as part of the baselining or monitoring jobs.

(str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor. This can either be the endpoint name or an EndpointInput.

(str): The path to the record preprocessor script. This can be a local path or an S3 uri.

(str): The path to the record post-analytics processor script. This can be a local path or an S3 uri.

(str): Desired S3 destination of the constraint_violations and statistics json files.

(sagemaker.model_monitor.Statistic or str): If provided alongside constraints, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an S3 uri pointing to a constraints JSON file.

(sagemaker.model_monitor.Constraints or str): If provided alongside statistics, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an S3 uri pointing to a constraints JSON file.

(str): The cron expression that dictates the frequency that this job runs at. See sagemaker.model_monitor.CronExpressionGenerator for valid expressions.

(int): The number of instances to run the jobs with.

(str): Type of EC2 instance to use for the job, for example, 'ml.m5.xlarge'.

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

(str): A KMS key for the job's volume.

(str): The KMS key id for the job's outputs.

(int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600

(dict): Environment variables to be passed to the job.

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

(bool): Whether to publish cloudwatch metrics as part of the baselining or monitoring jobs.

(str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.

(str): The name of the schedule to attach to.

(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.

(str): The name of the endpoint.

(str): The destination of the input.

(str): The S3 input mode. Can be one of: "File", "Pipe. Default: "File".

(str): The S3 Data Distribution Type. Can be one of: "FullyReplicated", "ShardedByS3Key"

(str): Monitoring start time offset, e.g. "-PT1H"

(str): Monitoring end time offset, e.g. "-PT0H".

(str): JSONpath to locate features in JSONlines dataset. Only used for ModelBiasMonitor and ModelExplainabilityMonitor

(str): Index or JSONpath to locate predicted label(s). Only used for ModelQualityMonitor, ModelBiasMonitor, and ModelExplainabilityMonitor

(str or int): Index or JSONpath to locate probabilities. Only used for ModelQualityMonitor, ModelBiasMonitor and ModelExplainabilityMonitor

(float): threshold to convert probabilities to binaries Only used for ModelQualityMonitor, ModelBiasMonitor and ModelExplainabilityMonitor

Whether to make a deep clone.

(str): The source for the output.

(str): The destination of the output. Optional. Default: s3://<default-session-bucket/schedule_name/output

(str): The S3 upload mode.

Whether to make a deep clone.

(str): The container image to use for training.

(str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

(int): Number of Amazon EC2 instances to use for training.

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

(int): Size in GB of the EBS volume to use for storing input data during training (default: 30). Must be large enough to store training data if File Mode is used (which is the default).

(str): Optional. KMS key ID for encrypting EBS volume attached to the training instance (default: NULL).

(int): Timeout in seconds for training (default: 24 * 60 * 60). After this amount of time Amazon SageMaker terminates the job regardless of its current status.

(str): The input mode that the algorithm supports (default: 'File'). Valid modes: * 'File' - Amazon SageMaker copies the training dataset from the S3 location to a local directory. * 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe. This argument can be overriden on a per-channel basis using “TrainingInput.input_mode“.

(str): S3 location for saving the training result (model artifacts and output files). If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the :meth:'~sagemaker.estimator.EstimatorBase.fit' method execution.

(str): Optional. KMS key ID for encrypting the training output (default: NULL).

(str): Prefix for training job name when the :meth:'~sagemaker.estimator.EstimatorBase.fit' method launches. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

(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.

(dict): Dictionary containing the hyperparameters to initialize this estimator with.

(list[dict]): List of tags for labeling a training job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(list[str]): List of subnet ids. If not specified training job will be created without VPC config.

(list[str]): List of security group ids. If not specified training job will be created without VPC config.

(str): URI where a pre-trained model is stored, either locally or in S3 (default: NULL). If specified, the estimator can download it. This model can be a 'model.tar.gz' from a previous training job, or other artifacts coming from a different source. In local mode, this should point to the path in which the model is located and not the file itself, as local Docker containers will try to mount the URI as a volume. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html#td-deserialization

(str): Name of the channel where 'model_uri' will be downloaded (default: 'model').

(list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs. This should be defined only for jobs that don't use an Amazon algorithm.

(bool): Specifies whether traffic between training containers is encrypted for the training job (default: “False“).

(bool): Specifies whether to use SageMaker Managed Spot instances for training. If enabled then the 'max_wait' arg should also be set. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html (default: “False“).

(int): Timeout in seconds waiting for spot training instances (default: NULL). After this amount of time Amazon SageMaker will stop waiting for Spot instances to become available (default: “NULL“).

(str): The S3 URI in which to persist checkpoints that the algorithm persists (if any) during training. (default: “NULL“).

(str): The local path that the algorithm writes its checkpoints to. SageMaker will persist all files under this path to 'checkpoint_s3_uri' continually during training. On job startup the reverse happens - data from the s3 location is downloaded to this path before the algorithm is started. If the path is unset then SageMaker assumes the checkpoints will be provided under '/opt/ml/checkpoints/'. (default: “NULL“).

(bool): Specifies whether container will run in network isolation mode (default: “False“). Network isolation mode restricts the container access to outside networks (such as the Internet). The container does not make any inbound or outbound network calls. Also known as Internet-free mode.

(list[:class:'~sagemaker.debugger.Rule']): A list of :class:'~sagemaker.debugger.Rule' objects used to define rules for continuous analysis with SageMaker Debugger (default: “NULL“). For more, see https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#continuous-analyses-through-rules

(:class:'~sagemaker.debugger.DebuggerHookConfig' or bool): Configuration for how debugging information is emitted with SageMaker Debugger. If not specified, a default one is created using the estimator's “output_path“, unless the region does not support SageMaker Debugger. To disable SageMaker Debugger, set this parameter to “False“. For more, see https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html

(:class:'~sagemaker.debugger.TensorBoardOutputConfig'): Configuration for customizing debugging visualization using TensorBoard (default: “NULL“). For more, see https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#capture-real-time-tensorboard-data-from-the-debugging-hook

(bool): enable SageMaker Metrics Time Series. For more information see: https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries (default: “NULL“).

(:class:'~sagemaker.debugger.ProfilerConfig'): Configuration for how SageMaker Debugger collects monitoring and profiling information from your training job. If not specified, Debugger will be configured with a default configuration and will save system and framework metrics the estimator's default “output_path“ in Amazon S3. Use :class:'~sagemaker.debugger.ProfilerConfig' to configure this parameter. To disable SageMaker Debugger monitoring and profiling, set the “disable_profiler“ parameter to “True“.

(bool): Specifies whether Debugger monitoring and profiling will be disabled (default: “False“).

(dict[str, str]) : Environment variables to be set for use during training job (default: “None“)

(int): The number of times to move a job to the STARTING status. You can specify between 1 and 30 attempts. If the value of attempts is greater than zero, the job is retried on InternalServerFailure the same number of attempts as the value. You can cap the total duration for your job by setting “max_wait“ and “max_run“ (default: “None“)

: additional arguements for parent class 'EstimatorBase'.

model hyperparameters

(str): The “ExecutionRoleArn“ IAM Role ARN for the “Model“, which is also used during transform jobs. If not specified, the role from the Estimator will be used.

(str): An container image to use for deploying the model. Defaults to the image used for training.

(Predictor): The predictor class to use when deploying the model.

(dict[str, list[str]]): Optional override for VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids.

: Additional parameters passed to :class:'~sagemaker.model.Model' .. tip:: You can find additional parameters for using this method at :class:'~sagemaker.model.Model'.

Whether to make a deep clone.

(str): An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role, if it needs to access an AWS resource.

(int): Number of Amazon EC2 instances to use for training.

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

(int): Size in GB of the EBS volume to use for storing input data during training (default: 30). Must be large enough to store training data if File Mode is used (which is the default).

(str): Optional. KMS key ID for encrypting EBS volume attached to the training instance (default: NULL).

(int): Timeout in seconds for training (default: 24 * 60 * 60). After this amount of time Amazon SageMaker terminates the job regardless of its current status.

(str): The input mode that the algorithm supports (default: 'File'). Valid modes: 'File' - Amazon SageMaker copies the training dataset from the S3 location to a local directory. 'Pipe' - Amazon SageMaker streams data directly from S3 to the container via a Unix-named pipe. This argument can be overriden on a per-channel basis using “TrainingInput.input_mode“.

(str): S3 location for saving the training result (model artifacts and output files). If not specified, results are stored to a default bucket. If the bucket with the specific name does not exist, the estimator creates the bucket during the :meth:'~sagemaker.estimator.EstimatorBase.fit' method execution. file:// urls are used for local mode. For example: 'file://model/' will save to the model folder in the current directory.

(str): Optional. KMS key ID for encrypting the training output (default: NULL).

(str): Prefix for training job name when the :meth:'~sagemaker.estimator.EstimatorBase.fit' method launches. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

(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.

(list[dict]): List of tags for labeling a training job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(list[str]): List of subnet ids. If not specified training job will be created without VPC config.

(list[str]): List of security group ids. If not specified training job will be created without VPC config.

(str): URI where a pre-trained model is stored, either locally or in S3 (default: NULL). If specified, the estimator will create a channel pointing to the model so the training job can download it. This model can be a 'model.tar.gz' from a previous training job, or other artifacts coming from a different source. In local mode, this should point to the path in which the model is located and not the file itself, as local Docker containers will try to mount the URI as a volume. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html#td-deserialization

(str): Name of the channel where 'model_uri' will be downloaded (default: 'model').

(list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the training jobs. Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs. This should be defined only for jobs that don't use an Amazon algorithm.

(bool): Specifies whether traffic between training containers is encrypted for the training job (default: “False“).

(bool): Specifies whether to use SageMaker Managed Spot instances for training. If enabled then the 'max_wait' arg should also be set. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html (default: “False“).

(int): Timeout in seconds waiting for spot training instances (default: NULL). After this amount of time Amazon SageMaker will stop waiting for Spot instances to become available (default: “NULL“).

(str): The S3 URI in which to persist checkpoints that the algorithm persists (if any) during training. (default: “NULL“).

(str): The local path that the algorithm writes its checkpoints to. SageMaker will persist all files under this path to 'checkpoint_s3_uri' continually during training. On job startup the reverse happens - data from the s3 location is downloaded to this path before the algorithm is started. If the path is unset then SageMaker assumes the checkpoints will be provided under '/opt/ml/checkpoints/'. (default: “NULL“).

(list[:class:'~sagemaker.debugger.Rule']): A list of :class:'~sagemaker.debugger.Rule' objects used to define rules for continuous analysis with SageMaker Debugger (default: “NULL“). For more, see https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#continuous-analyses-through-rules

(:class:'~sagemaker.debugger.DebuggerHookConfig' or bool): Configuration for how debugging information is emitted with SageMaker Debugger. If not specified, a default one is created using the estimator's “output_path“, unless the region does not support SageMaker Debugger. To disable SageMaker Debugger, set this parameter to “False“. For more, see https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html

(:class:'~sagemaker.debugger.TensorBoardOutputConfig'): Configuration for customizing debugging visualization using TensorBoard (default: “NULL“). For more, see https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_debugger.html#capture-real-time-tensorboard-data-from-the-debugging-hook

(bool): Enables SageMaker Metrics Time Series. For more information see: https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries (default: “NULL“).

(bool): Specifies whether container will run in network isolation mode (default: “False“). Network isolation mode restricts the container access to outside networks (such as the Internet). The container does not make any inbound or outbound network calls. Also known as Internet-free mode.

(:class:'~sagemaker.debugger.ProfilerConfig'): Configuration for how SageMaker Debugger collects monitoring and profiling information from your training job. If not specified, a default configuration is created using the estimator's “output_path“, unless the region does not support SageMaker Debugger. To disable SageMaker Debugger monitoring and profiling, set the “disable_profiler“ parameter to “True“.

(bool): Specifies whether Debugger monitoring and profiling will be disabled (default: “False“).

(dict[str, str]) : Environment variables to be set for use during training job (default: “None“)

(int): The number of times to move a job to the STARTING status. You can specify between 1 and 30 attempts. If the value of attempts is greater than zero, the job is retried on InternalServerFailure the same number of attempts as the value. You can cap the total duration for your job by setting “max_wait“ and “max_run“ (default: “None“)

(str): The absolute, relative, or S3 URI Path to a directory with any other training 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. The structure within this directory is preserved when training on Amazon SageMaker. If 'git_config' is provided, 'source_dir' should be a relative location to a directory in the Git repo. With the following GitHub repo directory structure, if you need 'train.py' as the entry point and 'test.py' as the training source code, you can assign entry_point='train.py' and source_dir='src'.

(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 checking out the 'master' branch, and checking out 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 the SSH passphrase when you run the 'git clone' command with SSH URLs. When HTTPS URLs are provided, if 2FA is disabled, then either “token“ or “username“ and “password“ are be used for authentication if provided. “Token“ is prioritized. If 2FA is enabled, only “token“ is used for authentication if provided. If required authentication info is not provided, the SageMaker Python SDK attempts to use local credentials to authenticate. If that fails, an error message is thrown. For CodeCommit repos, 2FA is not supported, so '2FA_enabled' should not be provided. There is no token in CodeCommit, so “token“ should also not be provided. When “repo“ is an SSH URL, the requirements are the same as GitHub repos. When “repo“ is an HTTPS URL, “username“ and “password“ are used for authentication if they are provided. If they are not provided, the SageMaker Python SDK attempts to use either the CodeCommit credential helper or local credential storage for authentication.

(dict): A dictionary containing the hyperparameters to initialize this estimator with. (Default: None).

(str): The log level to use within the container (default: logging.INFO). Valid values are defined in the Python logging module.

(str): The S3 prefix URI where custom code is uploaded (default: None). You must not include a trailing slash because a string prepended with a "/" is appended to “code_location“. The code file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'. If not specified, the default “code location“ is 's3://output_bucket/job-name/'.

(str): The absolute or relative path to the local Python source file that should be executed as the entry point to training. (Default: None). If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“. If 'git_config' is provided, 'entry_point' should be a relative location to the Python source file in the Git repo. You can assign entry_point='src/train.py'.

(list[str]): A list of absolute or relative paths to directories with any additional libraries that should be exported to the container (default: []). The library folders are 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. This is not supported with "local code" in Local Mode.

: update any deprecated parameters passed into class.

(str): Name of the training job to be created. If not specified, one is generated, using the base name given to the constructor if applicable.

(str or dict or TrainingInput): Information about the training data. This can be one of three types:

• (str) the S3 location where training data is saved, or a file:// path in local mode.

• (dict[str, str] or dict[str, TrainingInput]) If using multiple channels for training data, you can specify a dict mapping channel names to strings or :func:'~TrainingInput' objects.

• (TrainingInput) - channel configuration for S3 data sources that can provide additional information as well as the path to the training dataset. See :func:'TrainingInput' for full details.

• (sagemaker.session.FileSystemInput) - channel configuration for a file system data source that can provide additional information as well as the path to the training dataset.

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

([str]): A list of strings specifying which logs to print. Acceptable strings are "All", "NULL", "Training", or "Rules". To maintain backwards compatibility, boolean values are also accepted and converted to strings. Only meaningful when wait is True.

(str): Training job name. If not specified, the estimator generates a default job name, based on the training image name and current timestamp.

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

([str]): A list of strings specifying which logs to print. Acceptable strings are "All", "NULL", "Training", or "Rules". To maintain backwards compatibility, boolean values are also accepted and converted to strings.

(str): Identifies the device that you want to run your model after compilation, for example: ml_c5. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html.

(dict): Specifies the name and shape of the expected inputs for your trained model in json dictionary form, for example: 'data':[1,3,1024,1024], or 'var1': [1,1,28,28], 'var2':[1,1,28,28]

(str): Specifies where to store the compiled model

(str): The framework that is used to train the original model. Allowed values: 'mxnet', 'tensorflow', 'keras', 'pytorch', 'onnx', 'xgboost'

(str): The version of the framework

(int): Timeout in seconds for compilation (default: 3 * 60). After this amount of time Amazon SageMaker Neo terminates the compilation job regardless of its current status.

(list[dict]): List of tags for labeling a compilation job. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

(str): Target Platform OS, for example: 'LINUX'. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. It can be used instead of target_instance_family.

(str): Target Platform Architecture, for example: 'X86_64'. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. It can be used instead of target_instance_family.

(str, optional): Target Platform Accelerator, for example: 'NVIDIA'. For allowed strings see https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html. It can be used instead of target_instance_family.

(dict, optional): Additional parameters for compiler. Compiler Options are TargetPlatform / target_instance_family specific. See https://docs.aws.amazon.com/sagemaker/latest/dg/API_OutputConfig.html for details.

: Passed to invocation of “create_model()“. Implementations may customize “create_model()“ to accept “**kwargs“ to customize model creation during deploy. For more, see the implementation docs.

(str): The name of the training job to attach to.

(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): Name of the channel where pre-trained model data will be downloaded (default: 'model'). If no channel with the same name exists in the training job, this option will be ignored.

(int): Minimum number of EC2 instances to deploy to an endpoint for prediction.

(str): Type of EC2 instance to deploy to an endpoint for prediction, for example, 'ml.c4.xlarge'.

(:class:'~sagemaker.serializers.BaseSerializer'): A serializer object, used to encode data for an inference endpoint (default: None). If “serializer“ is not None, then “serializer“ will override the default serializer. The default serializer is set by the “predictor_cls“.

(:class:'~sagemaker.deserializers.BaseDeserializer'): A deserializer object, used to decode data from an inference endpoint (default: None). If “deserializer“ is not None, then “deserializer“ will override the default deserializer. The default deserializer is set by the “predictor_cls“.

(str): Type of Elastic Inference accelerator to attach to an endpoint for model loading and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator will be attached to the endpoint. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

(str): Name to use for creating an Amazon SageMaker endpoint. If not specified, the name of the training job is used.

(bool): Flag to select whether to use compiled (optimized) model. Default: False.

(bool): Whether the call should wait until the deployment of model completes (default: True).

(str): Name to use for creating an Amazon SageMaker model. If not specified, the estimator generates a default job name based on the training image name and current timestamp.

(str): The ARN of the KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.

(sagemaker.model_monitor.DataCaptureConfig): Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

(List[dict[str, str]]): Optional. The list of tags to attach to this specific endpoint. Example: >>> tags = ['Key': 'tagname', 'Value': 'tagvalue'] For more information about tags, see https://boto3.amazonaws.com/v1/documentation\ /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags

: Passed to invocation of “create_model()“. Implementations may customize “create_model()“ to accept “**kwargs“ to customize model creation during deploy. For more, see the implementation docs.

(list): The supported MIME types for the input data.

(list): The supported MIME types for the output data.

(list): A list of the instance types that are used to generate inferences in real-time.

(list): A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.

(str): The container image uri for Model Package, if not specified, Estimator's training container image will be used (default: None).

(str): Model Package name, exclusive to 'model_package_group_name', using 'model_package_name' makes the Model Package un-versioned (default: None).

(str): Model Package Group name, exclusive to 'model_package_name', using 'model_package_group_name' makes the Model Package versioned (default: None).

(ModelMetrics): ModelMetrics object (default: None).

(MetadataProperties): MetadataProperties (default: None).

(bool): A boolean value indicating if the Model Package is certified for AWS Marketplace (default: False).

(str): Model Approval Status, values can be "Approved", "Rejected", or "PendingManualApproval" (default: "PendingManualApproval").

(str): Model Package description (default: None).

(str): Instance family for compiled model, if specified, a compiled model will be used (default: None).

(str): User defined model name (default: None).

(DriftCheckBaselines): DriftCheckBaselines object (default: None).

: Passed to invocation of “create_model()“. Implementations may customize “create_model()“ to accept “**kwargs“ to customize model creation during deploy. For more, see the implementation docs.

: Keyword arguments used by the implemented method for creating the “Model“.

(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: NULL). Valid values: 'MultiRecord' and 'SingleRecord'.

(str): How the output is assembled (default: NULL). Valid values: 'Line' or 'NULL'.

(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: NULL).

(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.

(dict): Environment variables to be set for use during the transform job (default: NULL).

(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. If NULL specified, then the tags used for the training job are used for the transform job.

(str): The “ExecutionRoleArn“ IAM Role ARN for the “Model“, which is also used during transform jobs. If not specified, the role from the Estimator will be used.

(str): Optional. KMS key ID for encrypting the volume attached to the ML compute instance (default: NULL).

(dict[str, list[str]]): Optional override for the VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids.

(bool): Specifies whether container will run in network isolation mode. Network isolation mode restricts the container access to outside networks (such as the internet). The container does not make any inbound or outbound network calls. If True, a channel named "code" will be created for any user entry script for inference. Also known as Internet-free mode. If not specified, this setting is taken from the estimator's current configuration.

(str): Name to use for creating an Amazon SageMaker model. If not specified, the name of the training job is used.

:

(str): Name of the training job to be created. If not specified, one is generated, using the base name given to the constructor if applicable.

(list[:class:'~sagemaker.debugger.ProfilerRule']): A list of :class:'~sagemaker.debugger.ProfilerRule' objects to define rules for continuous analysis with SageMaker Debugger. Currently, you can only add new profiler rules during the training job. (default: “None“)

(int): How often profiling system metrics are collected; Unit: Milliseconds (default: “None“)

(str): The location in S3 to store the output. If profiler is enabled once, s3_output_path cannot be changed. (default: “None“)

(: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'. (default: “None“)

(bool): Specify whether to disable all the framework metrics. This won't update system metrics and the Debugger built-in rules for monitoring. To stop both monitoring and profiling, use the :class:'~sagemaker.estimator.Estimator.desable_profiling' method. (default: “False“)

Whether to make a deep clone.

(sagemaker.clarify.ExplainabilityConfig): Config object related to explainability configurations.

(sagemaker.clarify.ModelConfig): Config object related to bias configurations.

(list[str]): A list of feature names (without label) of model/endpint input.

Whether to make a deep clone.

(str): The S3 URI of the metric

(str): The digest of the metric (default: None)

(str): Specifies the type of content in S3 URI (default: None)

Whether to make a deep clone.

(str): An Amazon file system ID starting with 'fs-'.

(str): The type of file system used for the input. Valid values: 'EFS', 'FSxLustre'.

(str): Absolute or normalized path to the root directory (mount point) in the file system. Reference: https://docs.aws.amazon.com/efs/latest/ug/mounting-fs.html and https://docs.aws.amazon.com/efs/latest/ug/wt1-test.html

(int): The number of records in the set.

(int): The dimensionality of "values" arrays in the Record features, and label (if each Record is labeled).

(str): Permissions for read and write. Valid values: 'ro' or 'rw'. Defaults to 'ro'.

(str): The SageMaker Training Job channel this RecordSet should be bound to

Whether to make a deep clone.

(str): Path (absolute or relative) to the local Python source file which should be executed as the entry point to training. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“. If 'git_config' is provided, 'entry_point' should be a relative location to the Python source file in the Git repo. Example: With the following GitHub repo directory structure: >>> |—– README.md >>> |—– src >>> |—– train.py >>> |—– test.py You can assign entry_point='src/train.py'.

(str): Path (absolute, relative or an S3 URI) to a directory with any other training 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 training on Amazon SageMaker. If 'git_config' is provided, 'source_dir' should be a relative location to a directory in the Git repo. .. admonition:: Example With the following GitHub repo directory structure: >>> |—– README.md >>> |—– src >>> |—– train.py >>> |—– test.py and you need 'train.py' as entry point and 'test.py' as training source code as well, you can assign entry_point='train.py', source_dir='src'.

(dict): Hyperparameters that will be used for training (default: None). The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker. For convenience, this accepts other types for keys and values, but “str()“ will be called to convert them before training.

(str): Log level to use within the container (default: "INFO")

(str): The S3 prefix URI where custom code will be uploaded (default: None) - don't include a trailing slash since a string prepended with a "/" is appended to “code_location“. The code file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'. If not specified, the default “code location“ is s3://output_bucket/job-name/.

(str): An alternate image name to use instead of the official Sagemaker image for the framework. This is useful to run one of the Sagemaker supported frameworks with an image containing custom dependencies.

(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. .. admonition:: Example The following call >>> Estimator(entry_point='train.py', ... dependencies=['my/libs/common', 'virtual-env']) results in the following inside the container: >>> $ ls >>> opt/ml/code >>> |—— train.py >>> |—— common >>> |—— virtual-env This is not supported with "local code" in Local Mode.

(bool): Specifies whether container will run in network isolation mode. Network isolation mode restricts the container access to outside networks (such as the internet). The container does not make any inbound or outbound network calls. If True, a channel named "code" will be created for any user entry script for training. The user entry script, files in source_dir (if specified), and dependencies will be uploaded in a tar to S3. Also known as internet-free mode (default: 'False').

(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. .. admonition:: Example The following config: >>> git_config = 'repo': 'https://github.com/aws/sagemaker-python-sdk.git', >>> 'branch': 'test-branch-git-config', >>> 'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a' 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.

(str): The S3 URI in which to persist checkpoints that the algorithm persists (if any) during training. (default: “None“).

(str): The local path that the algorithm writes its checkpoints to. SageMaker will persist all files under this path to 'checkpoint_s3_uri' continually during training. On job startup the reverse happens - data from the s3 location is downloaded to this path before the algorithm is started. If the path is unset then SageMaker assumes the checkpoints will be provided under '/opt/ml/checkpoints/'. (default: “None“).

(bool): enable SageMaker Metrics Time Series. For more information see: https://docs.aws.amazon.com/sagemaker/latest/dg/API_AlgorithmSpecification.html#SageMaker-Type-AlgorithmSpecification-EnableSageMakerMetricsTimeSeries (default: “None“).

: Additional kwargs passed to the “EstimatorBase“ constructor. .. tip:: You can find additional parameters for initializing this class at :class:'~sagemaker.estimator.EstimatorBase'.

(str): Name of the training job to be created. If not specified, one is generated, using the base name given to the constructor if applicable.

(str): The name of the training job to attach to.

(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): Name of the channel where pre-trained model data will be downloaded (default: 'model'). If no channel with the same name exists in the training job, this option will be ignored.

(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.

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

(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. If none specified, then the tags used for the training job are used for the transform job.

(str): The “ExecutionRoleArn“ IAM Role ARN for the “Model“, which is also used during transform jobs. If not specified, the role from the Estimator will be used.

(int): Optional. The number of worker processes used by the inference server. If None, server will use one worker per vCPU.

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

(str): Path (absolute or relative) to the local Python source file which should be executed as the entry point to training. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“. If not specified, the training entry point is used.

(dict[str, list[str]]): Optional override for the VpcConfig set on the model. Default: use subnets and security groups from this Estimator. * 'Subnets' (list[str]): List of subnet ids. * 'SecurityGroupIds' (list[str]): List of security group ids.

(bool): Specifies whether container will run in network isolation mode. Network isolation mode restricts the container access to outside networks (such as the internet). The container does not make any inbound or outbound network calls. If True, a channel named "code" will be created for any user entry script for inference. Also known as Internet-free mode. If not specified, this setting is taken from the estimator's current configuration.

(str): Name to use for creating an Amazon SageMaker model. If not specified, the name of the training job is used.

Whether to make a deep clone.

(str): The S3 location of a SageMaker model data “.tar.gz“ file.

(str): A Docker image URI.

(str): An IAM role name or ARN for SageMaker to access AWS resources on your behalf.

(str): Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. This should be compatible with either Python 2.7 or Python 3.5. If 'git_config' is provided, 'entry_point' should be a relative location to the Python source file in the Git repo. Example With the following GitHub repo directory structure: >>> |—– README.md >>> |—– src >>> |—– inference.py >>> |—– test.py You can assign entry_point='src/inference.py'.

(str): Path (absolute, relative or an S3 URI) to a directory with any other training 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 training on Amazon SageMaker. If 'git_config' is provided, 'source_dir' should be a relative location to a directory in the Git repo. If the directory points to S3, no code will be uploaded and the S3 location will be used instead. .. admonition:: Example With the following GitHub repo directory structure: >>> |—– README.md >>> |—– src >>> |—– inference.py >>> |—– test.py You can assign entry_point='inference.py', source_dir='src'.

(callable[string, sagemaker.session.Session]): A function to call to create a predictor (default: None). If not None, “deploy“ will return the result of invoking this function on the created endpoint name.

(dict[str, str]): Environment variables to run with “image“ when hosted in SageMaker (default: None).

(str): The model name. If None, a default model name will be selected on each “deploy“.

(str): Log level to use within the container (default: "INFO").

(str): Name of the S3 bucket where custom code is uploaded (default: None). If not specified, default bucket created by “sagemaker.session.Session“ is used.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, one is created using the default AWS configuration chain.

(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. If the “'source_dir“' points to S3, code will be uploaded and the S3 location will be used instead. .. admonition:: Example The following call >>> Estimator(entry_point='inference.py', dependencies=['my/libs/common', 'virtual-env']) results in the following inside the container: >>> $ ls >>> opt/ml/code >>> |—— inference.py >>> |—— common >>> |—— virtual-env

(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. .. admonition:: Example The following config: >>> git_config = 'repo': 'https://github.com/aws/sagemaker-python-sdk.git', >>> 'branch': 'test-branch-git-config', >>> 'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a' 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.

: Keyword arguments passed to the “Model“ initializer.

(str): The EC2 instance type to deploy this Model to. For example, 'ml.p2.xlarge'.

(str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model. For example, 'ml.eia1.medium'.

Whether to make a deep clone.

(str): The name of this hyperparameter validate

(callable[object]->[bool]): A validation function or list of validation functions. Each function validates an object and returns False if the object value is invalid for this hyperparameter.

(str): A usage guide to display on validation failure.

: function to convert data types

(R6Class): R6Class for descriptor class to modify

: values to be validated

: R object to be serialized

Whether to make a deep clone.

(sagemaker.estimator.EstimatorBase): An estimator object that has been initialized with the desired configuration. There does not need to be a training job associated with this instance.

(str): Name of the metric for evaluating training jobs.

(dict[str, sagemaker.parameter.ParameterRange]): Dictionary of parameter ranges. These parameter ranges can be one of three types: Continuous, Integer, or Categorical. The keys of the dictionary are the names of the hyperparameter, and the values are the appropriate parameter range class to represent the range.

(list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the training jobs (default: None). Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs. This should be defined only for hyperparameter tuning jobs that don't use an Amazon algorithm.

(str): Strategy to be used for hyperparameter estimations (default: 'Bayesian').

(str): The type of the objective metric for evaluating training jobs. This value can be either 'Minimize' or 'Maximize' (default: 'Maximize').

(int): Maximum total number of training jobs to start for the hyperparameter tuning job (default: 1).

(int): Maximum number of parallel training jobs to start (default: 1).

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

(str): Prefix for the hyperparameter tuning job name when the :meth:'~sagemaker.tuner.HyperparameterTuner.fit' method launches. If not specified, a default job name is generated, based on the training image name and current timestamp.

(sagemaker.tuner.WarmStartConfig): A “WarmStartConfig“ object that has been initialized with the configuration defining the nature of warm start tuning job.

(str): Specifies whether early stopping is enabled for the job. Can be either 'Auto' or 'Off' (default: 'Off'). If set to 'Off', early stopping will not be attempted. If set to 'Auto', early stopping of some training jobs may happen, but is not guaranteed to.

(str): A unique name to identify an estimator within the hyperparameter tuning job, when more than one estimator is used with the same tuning job (default: None).

: Information about the training data. Please refer to the “fit()“ method of the associated estimator, as this can take any of the following forms: * (str) - The S3 location where training data is saved. * (dict[str, str] or dict[str, TrainingInput]) - If using multiple channels for training data, you can specify a dict mapping channel names to strings or :func:'~TrainingInput' objects. * (TrainingInput) - Channel configuration for S3 data sources that can provide additional information about the training dataset. See :func:'TrainingInput' for full details. * (sagemaker.session.FileSystemInput) - channel configuration for a file system data source that can provide additional information as well as the path to the training dataset. * (sagemaker.amazon.amazon_estimator.RecordSet) - A collection of Amazon :class:~'Record' objects serialized and stored in S3. For use with an estimator for an Amazon algorithm. * (sagemaker.amazon.amazon_estimator.FileSystemRecordSet) - Amazon SageMaker channel configuration for a file system data source for Amazon algorithms. * (list[sagemaker.amazon.amazon_estimator.RecordSet]) - A list of :class:~'sagemaker.amazon.amazon_estimator.RecordSet' objects, where each instance is a different channel of training data. * (list[sagemaker.amazon.amazon_estimator.FileSystemRecordSet]) - A list of :class:~'sagemaker.amazon.amazon_estimator.FileSystemRecordSet' objects, where each instance is a different channel of training data.

(str): Tuning job name. If not specified, the tuner generates a default job name, based on the training image name and current timestamp.

: It can take one of the following two forms. * (bool) - Whether or not the hyperparameter tuning job should include information about the estimator class (default: False). This information is passed as a hyperparameter, so if the algorithm you are using cannot handle unknown hyperparameters (e.g. an Amazon SageMaker built-in algorithm that does not have a custom estimator in the Python SDK), then set “include_cls_metadata“ to “False“. * (dict[str, bool]) - This version should be used for tuners created via the factory method create(), to specify the flag for each estimator provided in the estimator_dict argument of the method. The keys would be the same estimator names as in estimator_dict. If one estimator doesn't need the flag set, then no need to include it in the dictionary.

(dict[str, dict]): Dictionary for other arguments needed for training. Should be used only for tuners created via the factory method create(). The keys are the estimator names for the estimator_dict argument of create() method. Each value is a dictionary for the other arguments needed for training of the corresponding estimator.

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

: Other arguments needed for training. Please refer to the “fit()“ method of the associated estimator to see what other arguments are needed.

(str): The name of the hyperparameter tuning job to attach to.

(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.

(dict): The response to a “DescribeHyperParameterTuningJob“ call. If not specified, the “HyperparameterTuner“ will perform one such call with the provided hyperparameter tuning job name.

: It can take one of the following two forms. (str): The estimator class name associated with the training jobs, e.g. 'sagemaker.estimator.Estimator'. If not specified, the “HyperparameterTuner“ will try to derive the correct estimator class from training job metadata, defaulting to :class:~'Estimator' if it is unable to determine a more specific class. (dict[str, str]): This form should be used only when the 'TrainingJobDefinitions' field (list) is present in tuning job description. In this scenario training jobs could be created from different training job definitions in the 'TrainingJobDefinitions' field, each of which would be mapped to a different estimator after the “attach()“ call. The “estimator_cls“ should then be a dictionary to specify estimator class names for individual estimators as needed. The keys should be the 'DefinitionName' value of items in 'TrainingJobDefinitions', which would be used as estimator names in the resulting tuner instance. # Example #1 - assuming we have the following tuning job description, which has the # 'TrainingJobDefinition' field present using a SageMaker built-in algorithm (i.e. PCA), # and “attach()“ can derive the estimator class from the training image. # So “estimator_cls“ would not be needed.

# .. code:: R list( 'BestTrainingJob'= 'best_training_job_name', 'TrainingJobDefinition' = list( 'AlgorithmSpecification' = list( 'TrainingImage'= '174872318107.dkr.ecr.us-west-2.amazonaws.com/pca:1 ) ) ) #>>> my_tuner.fit() #>>> job_name = my_tuner$latest_tuning_job$name #Later on: #>>> attached_tuner = HyperparameterTuner.attach(job_name) #>>> attached_tuner.deploy() #Example #2 - assuming we have the following tuning job description, which has a 2-item #list for the 'TrainingJobDefinitions' field. In this case 'estimator_cls' is only #needed for the 2nd item since the 1st item uses a SageMaker built-in algorithm #(i.e. PCA).

#.. code:: R list( 'BestTrainingJob' = 'best_training_job_name', 'TrainingJobDefinitions'= list( list( 'DefinitionName'= 'estimator_pca', 'AlgorithmSpecification'= list( 'TrainingImage'= '174872318107.dkr.ecr.us-west-2.amazonaws.com/pca:1) ), list( 'DefinitionName'= 'estimator_byoa', 'AlgorithmSpecification' = list( 'TrainingImage'= '123456789012.dkr.ecr.us-west-2.amazonaws.com/byoa:latest) ) ) ) >>> my_tuner.fit() >>> job_name = my_tuner.latest_tuning_job.name Later on: >>> attached_tuner = HyperparameterTuner.attach( >>> job_name, >>> estimator_cls= >>> 'estimator_byoa': 'org.byoa.Estimator' >>> ) >>> attached_tuner.deploy()

(int): Minimum number of EC2 instances to deploy to an endpoint for prediction.

(str): Type of EC2 instance to deploy to an endpoint for prediction, for example, 'ml.c4.xlarge'.

(str): Type of Elastic Inference accelerator to attach to an endpoint for model loading and inference, for example, 'ml.eia1.medium'. If not specified, no Elastic Inference accelerator will be attached to the endpoint. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

(str): Name to use for creating an Amazon SageMaker endpoint. If not specified, the name of the training job is used.

(bool): Whether the call should wait until the deployment of model completes (default: True).

(str): Name to use for creating an Amazon SageMaker model. If not specified, the name of the training job is used.

(str): The ARN of the KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.

(sagemaker.model_monitor.DataCaptureConfig): Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

: Other arguments needed for deployment. Please refer to the “create_model()“ method of the associated estimator to see what other arguments are needed.

(dict): Dictionary containing "TrainingJobName" and "TrainingJobDefinitionName". Example: .. code:: R list( "TrainingJobName"= "my_training_job_name", "TrainingJobDefinitionName" "my_training_job_definition_name" )

(str): Name of the endpoint to delete

(setstr): Set of additional parents along with the self to be used in warm starting

(sagemaker.estimator.EstimatorBase): An estimator object that has been initialized with the desired configuration. There does not need to be a training job associated with this instance.

(setstr): Set of additional parents along with the self to be used in warm starting

(dict[str, sagemaker.estimator.EstimatorBase]): Dictionary of estimator instances that have been initialized with the desired configuration. There does not need to be a training job associated with the estimator instances. The keys of the dictionary would be referred to as "estimator names".

(dict[str, str]): Dictionary of names of the objective metric for evaluating training jobs. The keys are the same set of estimator names as in “estimator_dict“, and there must be one entry for each estimator in “estimator_dict“.

(dict[str, dict[str, sagemaker.parameter.ParameterRange]]): Dictionary of tunable hyperparameter ranges. The keys are the same set of estimator names as in estimator_dict, and there must be one entry for each estimator in estimator_dict. Each value is a dictionary of sagemaker.parameter.ParameterRange instance, which can be one of three types: Continuous, Integer, or Categorical. The keys of each ParameterRange dictionaries are the names of the hyperparameter, and the values are the appropriate parameter range class to represent the range.

(dict(str, list[dict]])): Dictionary of metric definitions. The keys are the same set or a subset of estimator names as in estimator_dict, and there must be one entry for each estimator in estimator_dict. Each value is a list of dictionaries that defines the metric(s) used to evaluate the training jobs (default: None). Each of these dictionaries contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs. This should be defined only for hyperparameter tuning jobs that don't use an Amazon algorithm.

(str): Prefix for the hyperparameter tuning job name when the :meth:'~sagemaker.tuner.HyperparameterTuner.fit' method launches. If not specified, a default job name is generated, based on the training image name and current timestamp.

(str): Strategy to be used for hyperparameter estimations (default: 'Bayesian').

(str): The type of the objective metric for evaluating training jobs. This value can be either 'Minimize' or 'Maximize' (default: 'Maximize').

(int): Maximum total number of training jobs to start for the hyperparameter

(int): Maximum number of parallel training jobs to start (default: 1).

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

(sagemaker.tuner.WarmStartConfig): A “WarmStartConfig“ object that has been initialized with the configuration defining the nature of warm start tuning job.

(str): Specifies whether early stopping is enabled for the job. Can be either 'Auto' or 'Off' (default: 'Off'). If set to 'Off', early stopping will not be attempted. If set to 'Auto', early stopping of some training jobs may happen, but is not guaranteed to.

job (default: 1).

(str): A unique name to identify an estimator within the hyperparameter tuning job, when more than one estimator is used with the same tuning job (default: None).

(sagemaker.estimator.EstimatorBase): An estimator object that has been initialized with the desired configuration. There does not need to be a training job associated with this instance.

(str): Name of the metric for evaluating training jobs.

(dict[str, sagemaker.parameter.ParameterRange]): Dictionary of parameter ranges. These parameter ranges can be one of three types: Continuous, Integer, or Categorical. The keys of the dictionary are the names of the hyperparameter, and the values are the appropriate parameter range class to represent the range.

(list[dict]): A list of dictionaries that defines the metric(s) used to evaluate the training jobs (default: None). Each dictionary contains two keys: 'Name' for the name of the metric, and 'Regex' for the regular expression used to extract the metric from the logs. This should be defined only for hyperparameter tuning jobs that don't use an Amazon algorithm.

Whether to make a deep clone.

(str): The MIME type to signal to the inference endpoint when sending request data (default: "application/octet-stream").

(object): Data to be serialized.

Whether to make a deep clone.

(int): The minimum value for the range.

(int): The maximum value for the range.

(str): The scale used for searching the range during tuning (default: 'Auto'). Valid values: 'Auto', 'Linear', Logarithmic' and 'ReverseLogarithmic'.

The value to be verified.

Whether to make a deep clone.

(union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that is expected from the inference endpoint (default: "application/json").

(botocore.response.StreamingBody): Data to be deserialized.

(str): The MIME type of the data.

Whether to make a deep clone.

(union[str, tuple[str]]): The MIME type (or tuple of allowable MIME types) that is expected from the inference endpoint (default: ("text/csv","application/json")).

(botocore.response.StreamingBody): Data to be deserialized.

(str): The MIME type of the data.

Whether to make a deep clone.

(str): The MIME type to signal to the inference endpoint when sending request data (default: "application/jsonlines").

(object): Data to be serialized. The data can be a string, iterable of JSON serializable objects, or a file-like object.

Whether to make a deep clone.

(object): Data to be serialized.

Whether to make a deep clone.

(str): The MIME type to signal to the inference endpoint when sending request data (default: "text/libsvm").

(object): Data to be serialized. Can be a string, a file-like object, sparse matrix, or a list (format: list(<sparse matrix>, <label>)).

Whether to make a deep clone.