Package 'sagemaker.mlframework'

(str): An AWS IAM role name or 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.

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

(int): The number of instances to run the Processing job with. Defaults to 1.

(str): The version of SageMaker PySpark.

(str): The version of python.

(str): The version of spark container.

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

(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 all ProcessingOutputs.

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

(str): Prefix for processing name. If not specified, the processor 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 processor creates one using the default AWS configuration chain.

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

([dict]): List of tags to be passed to the processing job. network_config (sagemaker.network.NetworkConfig): A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.

(sagemaker.network.NetworkConfig): A 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): .py or .jar file to submit to Spark as the primary application

(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).

(str): S3 URI where Spark events are stored.

Whether to make a deep clone.

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(str or list[str] or AutoMLInput): Local path or S3 Uri where the training data is stored. Or an AutoMLInput object. If a local path is provided, the dataset will be uploaded to an S3 location.

(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). if “wait“ is False, “logs“ will be set to False as well.

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

(str): AutoML job name

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, the one originally associated with the “AutoML“ instance is used.

(str): The name of the AutoML job to describe. If None, will use object's latest_auto_ml_job name.

(str): The name of the AutoML job. If None, will use object's .current_auto_ml_job_name.

(str): The name of the AutoML job. If None, will use object's .current_job name.

(str): Filter the result with candidate status, values could be "Completed", "InProgress", "Failed", "Stopped", "Stopping"

(str): The name of a specified candidate to list. Default to None.

(str): The Arn of a specified candidate to list. Default to None.

(str): The order that the candidates will be listed in result. Default to None.

(str): The value that the candidates will be sorted by. Default to None.

(int): The number of candidates will be listed in results, between 1 to 100. Default to None. If None, will return all the candidates.

(str): The pipeline model name.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, the one originally associated with the “AutoML“ instance is used.:

(CandidateEstimator or dict): a CandidateEstimator used for deploying to a SageMaker Inference Pipeline. If None, the best candidate will be used. If the candidate input is a dict, a CandidateEstimator will be created from it.

(dict): Specifies a VPC that your training jobs and hosted models have access to. Contents include "SecurityGroupIds" and "Subnets".

(bool): Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. Default: False

(str): KMS key ARN used to encrypt the repacked model archive file if the model is repacked

(callable[string, sagemaker.session.Session]): A function to call to create a predictor (default: None). If specified, “deploy()“ returns the result of invoking this function on the created endpoint name.

(list): List of keys for response content. The order of the keys will dictate the content order in the response.

(int): The initial number of instances to run in the “Endpoint“ created from this “Model“.

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

(CandidateEstimator or dict): a CandidateEstimator used for deploying to a SageMaker Inference Pipeline. If None, the best candidate will be used. If the candidate input is a dict, a CandidateEstimator will be created from it.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, the one originally associated with the “AutoML“ instance is used.

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

(str): The name of the endpoint to create (default: None). If not specified, a unique endpoint name will be created.

(List[dict[str, str]]): The list of tags to attach to this specific endpoint.

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

(dict): Specifies a VPC that your training jobs and hosted models have access to. Contents include "SecurityGroupIds" and "Subnets".

(bool): Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. Default: False

(str): KMS key ARN used to encrypt the repacked model archive file if the model is repacked

(callable[string, sagemaker.session.Session]): A function to call to create a predictor (default: None). If specified, “deploy()“ returns the result of invoking this function on the created endpoint name.

(list): List of keys for response content. The order of the keys will dictate the content order in the response.

(default: None). If “deserializer“ is not None, then “deserializer“ will override the default deserializer. The default deserializer is set by the “predictor_cls“.

(list): list of inference containers

(list): list of inference response keys

Whether to make a deep clone.

(str, list[str]): a string or a list of string that points to (a) S3 location(s) where input data is stored.

(str): the target attribute name for regression or classification.

(str): if training data is compressed, the compression type. The default value is None.

Whether to make a deep clone.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: None). If not specified, the one originally associated with the “AutoMLJob“ instance is used.

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(str, list[str]): Parameters used when called :meth:'~sagemaker.automl.AutoML.fit'.

(sagemaker.automl.AutoML): AutoML object created by the user.

(str, list[str]): Parameters used when called :meth:'~sagemaker.automl.AutoML.fit'.

(bool): indicate whether to output logs.

Whether to make a deep clone.

(dict): a dictionary of candidate returned by AutoML.list_candidates() or AutoML.best_candidate().

(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 or list[str]): Local path or S3 Uri where the training data is stored. If a local path is provided, the dataset will be uploaded to an S3 location.

(str): name of the candidate to be rerun, if None, candidate's original name will be used.

(str): The KMS key id to encrypt data on the storage volume attached to the ML compute instance(s).

(bool): To encrypt all communications between ML compute instances in distributed training. Default: False.

(dict): Specifies a VPC that jobs and hosted models have access to. Control access to and from training and model containers by configuring the VPC

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

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

Whether to make a deep clone.

(str): Name of the candidate step

(dict): Inputs of the candidate step

(str): Type of the candidate step, Training or Transform

(dict): Description of candidate step job

Whether to make a deep clone.

(str): Path (absolute or relative) to the 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“.

(bool): If true, entry point is run as an MPI script. By default, the Chainer Framework runs the entry point with 'mpirun' if more than one instance is used.

(int): Total number of processes to run the entry point with. By default, the Chainer Framework runs one process per GPU (on GPU instances), or one process per host (on CPU instances).

(int): The number of processes that can run on each instance. By default, this is set to the number of GPUs on the instance (on GPU instances), or one (on CPU instances).

(str): String of options to the 'mpirun' command used to run the entry point. For example, '-X NCCL_DEBUG=WARN' will pass that option string to the mpirun command.

(str): Path (absolute or relative) to a directory with any other training source code dependencies aside from the entry point file (default: None). Structure within this directory are preserved when training on Amazon SageMaker.

(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): Chainer version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators.

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided.

(str): If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples * “123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0“ * “custom-image:latest“ If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

: Additional kwargs passed to the :class:'~sagemaker.estimator.Framework' constructor.

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

(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.

(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.

(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.

(str): Path (absolute or relative) to a directory with any other serving source code dependencies aside from the entry point file. If not specified, the model source directory from training is used.

(list[str]): A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container. If not specified, the dependencies from training are used. This is not supported with "local code" in Local Mode.

: Additional kwargs passed to the ChainerModel constructor.

Whether to make a deep clone.

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

(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.

(str): Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“.

(str): A Docker image URI (default: None). If not specified, a default image for Chainer will be used. If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

(str): Chainer version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided.

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided.

(callable[str, sagemaker.session.Session]): A function to call to create a predictor with an endpoint name and SageMaker “Session“. If specified, “deploy()“ returns the result of invoking this function on the created endpoint name.

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

: Keyword arguments passed to the :class:'~sagemaker.model.FrameworkModel' 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'.

(str): AWS region where the image is uploaded.

(str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).

(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 the endpoint to perform inference on.

(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.

(sagemaker.serializers.BaseSerializer): Optional. Default serializes input data to .npy format. Handles lists and numpy arrays.

(sagemaker.deserializers.BaseDeserializer): Optional. Default parses the response from .npy format to numpy array.

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 accessing 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): Dimensionality of factorization.

(str): Type of predictor 'binary_classifier' or 'regressor'.

(int): Number of training epochs to run.

(float): Optimizer parameter. Clip the gradient by projecting onto the box [-clip_gradient, +clip_gradient]

(float): Optimizer parameter. Small value to avoid division by 0.

(float): Optimizer parameter. If set, multiplies the gradient with rescale_grad before updating. Often choose to be 1.0/batch_size.

(float): Non-negative learning rate for the bias term.

(float): Non-negative learning rate for linear terms.

(float): Non-negative learning rate for factorization terms.

(float): Non-negative weight decay for the bias term.

(float): Non-negative weight decay for linear terms.

(float): Non-negative weight decay for factorization terms.

(string): Initialization method for the bias term: 'normal', 'uniform' or 'constant'.

(float): Non-negative range for initialization of the bias term that takes effect when bias_init_method parameter is 'uniform'

(float): Non-negative standard deviation for initialization of the bias term that takes effect when bias_init_method parameter is 'normal'.

(float): Initial value of the bias term that takes effect when bias_init_method parameter is 'constant'.

(string): Initialization method for linear term: 'normal', 'uniform' or 'constant'.

(float): Non-negative range for initialization of linear terms that takes effect when linear_init_method parameter is 'uniform'.

(float): Non-negative standard deviation for initialization of linear terms that takes effect when linear_init_method parameter is 'normal'.

(float): Initial value of linear terms that takes effect when linear_init_method parameter is 'constant'.

(string): Initialization method for factorization term: 'normal', 'uniform' or 'constant'.

(float): Non-negative range for initialization of factorization terms that takes effect when factors_init_method parameter is 'uniform'.

(float): Non-negative standard deviation for initialization of factorization terms that takes effect when factors_init_method parameter is 'normal'.

(float): Initial value of factorization terms that takes effect when factors_init_method parameter is 'constant'.

: base class keyword argument values. You can find additional parameters for initializing this class at :class:'~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase' and :class:'~sagemaker.estimator.EstimatorBase'.

(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 kwargs passed to the FactorizationMachinesModel constructor.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(sagemaker.session.Session): A SageMaker Session object, used for SageMaker interactions (default: NULL). If not specified, one is created using the default AWS configuration chain.

Whether to make a deep clone.

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators

(str): Path (absolute or relative) to the 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“.

(str): Transformers version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. The current supported version is “4.6.1“.

(str): TensorFlow version you want to use for executing your model training code. Defaults to “None“. Required unless “pytorch_version“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.

(str): PyTorch version you want to use for executing your model training code. Defaults to “None“. Required unless “tensorflow_version“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.

(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.

(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): If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples: * “123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0“ * “custom-image:latest“ If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

(dict): A dictionary with information on how to run distributed training (default: None). Currently, the following are supported: distributed training with parameter servers, SageMaker Distributed (SMD) Data and Model Parallelism, and MPI. SMD Model Parallelism can only be used with MPI. To enable parameter server use the following setup: .. code:: python "parameter_server": "enabled": True To enable MPI: .. code:: python "mpi": "enabled": True To enable SMDistributed Data Parallel or Model Parallel: .. code:: python "smdistributed": "dataparallel": "enabled": True , "modelparallel": "enabled": True, "parameters":

(:class:'sagemaker.mlcore::TrainingCompilerConfig'): Configures SageMaker Training Compiler to accelerate training.

: Additional kwargs passed to the :class:'~sagemaker.estimator.Framework' constructor.

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

(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.

(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.

(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.

(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.

(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.

: 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 specified with either the 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.

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

(str): The absolute or relative path to the Python source file that should be executed as the entry point to model hosting. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“. Defaults to None.

(str): Transformers version you want to use for executing your model training code. Defaults to None. Required unless “image_uri“ is provided.

(str): TensorFlow version you want to use for executing your inference code. Defaults to “None“. Required unless “pytorch_version“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.

(str): PyTorch version you want to use for executing your inference code. Defaults to “None“. Required unless “tensorflow_version“ is provided. List of supported versions: https://github.com/aws/sagemaker-python-sdk#huggingface-sagemaker-estimators.

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided.

(str): A Docker image URI. Defaults to None. If not specified, a default image for PyTorch will be used. If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

(callable[str, sagemaker.session.Session]): A function to call to create a predictor with an endpoint name and SageMaker “Session“. If specified, “deploy()“ returns the result of invoking this function on the created endpoint name.

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

: Keyword arguments passed to the superclass :class:'~sagemaker.model.FrameworkModel' and, subsequently, its superclass :class:'~sagemaker.model.Model'.,

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

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

(str): Inference image URI for the container. Model class' self.image will be used if it is None. Defaults to “None“.

(ModelMetrics): ModelMetrics object. Defaults to “None“.

(MetadataProperties): MetadataProperties object. Defaults to “None“.

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

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

(str): Model Package description. Defaults to “None“.

(DriftCheckBaselines): DriftCheckBaselines object (default: None)

(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.

(str): AWS region where the image is uploaded.

(str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).

(str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model.

Whether to make a deep clone.

(str): The name of the endpoint to perform inference on.

(sagemaker.session.Session): Session object that 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.

(sagemaker.serializers.BaseSerializer): Optional. Default serializes input data to .npy format. Handles lists and numpy arrays.

(sagemaker.deserializers.BaseDeserializer): Optional. Default parses the response from .npy format to numpy array.

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'.

(str): Transformers version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. The current supported version is “4.4.2“.

(str): TensorFlow version you want to use for executing your model training code. Defaults to “None“. Required unless “pytorch_version“ is provided. The current supported version is “1.6.0“.

(str): PyTorch version you want to use for executing your model training code. Defaults to “None“. Required unless “tensorflow_version“ is provided. The current supported version is “2.4.1“.

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided. If using PyTorch, the current supported version is “py36“. If using TensorFlow, the current supported version is “py37“.

(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).

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 accessing AWS resource.

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

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

(int): Required. The number of embeddings to train for entities accessing online resources. We recommend 2x the total number of unique entity IDs.

(int): Required. The size of the embedding vectors for both entity and IP addresses.

(int): Optional. The period at which to publish metrics (batches).

(int): Optional. Maximum number of passes over the training data.

(float): Optional. Learning rate for the optimizer.

(int): Optional. The number of fully-connected layers to encode IP address embedding.

(int): Optional. The ratio of random negative samples to draw during training. Random negative samples are randomly drawn IPv4 addresses.

(int): Optional. The ratio of shuffled negative samples to draw during training. Shuffled negative samples are IP addresses picked from within a batch.

(float): Optional. Weight decay coefficient. Adds L2 regularization.

: base class keyword argument values.

(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 kwargs passed to the IPInsightsModel constructor.

(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.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(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.

(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 accessing 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): The number of clusters to produce.

(str): How to initialize cluster locations. One of 'random' or 'kmeans++'.

(int): Maximum iterations for Lloyds EM procedure in the local kmeans used in finalize stage.

(float): Tolerance for change in ssd for early stopping in local kmeans.

(int): Local version is run multiple times and the one with the best loss is chosen. This determines how many times.

(str): Initialization method for local version. One of 'random', 'kmeans++'

(int): The points can have a decayed weight. When a point is observed its weight, with regard to the computation of the cluster mean is 1. This weight will decay exponentially as we observe more points. The exponent coefficient is chosen such that after observing “half_life_time_size“ points after the mentioned point, its weight will become 1/2. If set to 0, there will be no decay.

(int): Number of passes done over the training data.

(int): The algorithm will create “num_clusters * extra_center_factor“ as it runs and reduce the number of centers to “k“ when finalizing

(list): JSON list of metrics types to be used for reporting the score for the model. Allowed values are "msd" Means Square Error, "ssd": Sum of square distance. If test data is provided, the score shall be reported in terms of all requested metrics.

: base class keyword argument values.

(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 kwargs passed to the KMeansModel constructor.

(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.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(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.

(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 accessing 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): Required. Number of nearest neighbors.

(int): Required. Number of data points to be sampled from the training data set.

(str): Required. Type of inference to use on the data's labels, allowed values are 'classifier' and 'regressor'.

(str): Optional. Type of dimension reduction technique to use. Valid values: "sign", "fjlt"

(int): Optional. Target dimension to reduce to. Required when dimension_reduction_type is specified.

(str): Optional. Type of index to use. Valid values are "faiss.Flat", "faiss.IVFFlat", "faiss.IVFPQ".

(str): Optional. Distance metric to measure between points when finding nearest neighbors. Valid values are "COSINE", "INNER_PRODUCT", "L2"

(str): Optional. Number of centroids to construct in the index if index_type is "faiss.IVFFlat" or "faiss.IVFPQ".

(int): Optional. Number of vector sub-components to construct in the index, if index_type is "faiss.IVFPQ".

: base class keyword argument values.

(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 kwargs passed to the KNNModel constructor.

(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.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(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.

(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 accessing AWS resource.

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

(int): The number of topics for LDA to find within the data.

(float): Optional. Initial guess for the concentration parameter

(int): Optional. The number of restarts to perform during the Alternating Least Squares (ALS) spectral decomposition phase of the algorithm.

(int): Optional. The maximum number of iterations to perform during the ALS phase of the algorithm.

(float): Optional. Target error tolerance for the ALS phase of the algorithm.

: base class keyword argument values.

(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 kwargs passed to the LDAModel constructor.

(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.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(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.

(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 accessing 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): The type of predictor to learn. Either "binary_classifier" or "multiclass_classifier" or "regressor".

(str): One of 'accuracy', 'f1', 'f_beta', 'precision_at_target_recall', 'recall_at_target_precision', 'cross_entropy_loss', 'loss_function'

(float): Target recall. Only applicable if binary_classifier_model_selection_criteria is precision_at_target_recall.

(float): Target precision. Only applicable if binary_classifier_model_selection_criteria is recall_at_target_precision.

(float): The importance weight of positive examples is multiplied by this constant. Useful for skewed datasets. Only applies for classification tasks.

(int): The maximum number of passes to make over the training data.

(bool): Whether to include a bias field

(int): Number of models to train in parallel. If not set, the number of parallel models to train will be decided by the algorithm itself. One model will be trained according to the given training parameter (regularization, optimizer, loss) and the rest by close by parameters.

(int): Number of observations to use from validation dataset for doing model calibration (finding the best threshold).

(str): Function to use to set the initial model weights. One of "uniform" or "normal"

(float): For "uniform" init, the range of values.

(float): For "normal" init, the standard-deviation.

(float): Initial weight for bias term

(str): One of 'sgd', 'adam', 'rmsprop' or 'auto'

(str): One of 'logistic', 'squared_loss', 'absolute_loss', 'hinge_loss', 'eps_insensitive_squared_loss', 'eps_insensitive_absolute_loss', 'quantile_loss', 'huber_loss' or 'softmax_loss' or 'auto'.

(float): L2 regularization parameter i.e. the weight decay parameter. Use 0 for no L2 regularization.

(float): L1 regularization parameter. Use 0 for no L1 regularization.

(float): Momentum parameter of sgd optimizer.

(float): The SGD learning rate

(float): Exponential decay rate for first moment estimates. Only applies for adam optimizer.

(float): Exponential decay rate for second moment estimates. Only applies for adam optimizer.

(float): Allows different learning rate for the bias term. The actual learning rate for the bias is learning rate times bias_lr_mult.

(float): Allows different regularization for the bias term. The actual L2 regularization weight for the bias is wd times bias_wd_mult. By default there is no regularization on the bias term.

(bool): If true, we use a scheduler for the learning rate.

(int): The number of steps between decreases of the learning rate. Only applies to learning rate scheduler.

(float): Every lr_scheduler_step the learning rate will decrease by this quantity. Only applies for learning rate scheduler.

(float): The learning rate will never decrease to a value lower than this. Only applies for learning rate scheduler.

(bool): Normalizes the features before training to have standard deviation of 1.0.

(bool): Normalizes the regression label to have a standard deviation of 1.0. If set for classification, it will be ignored.

(bool): If true, features are modified to have mean 0.0.

(bool): If true, labels are modified to have mean 0.0.

(int): The number of data points to use for calculating the normalizing and unbiasing terms.

(float): The margin for hinge_loss.

(float): Quantile for quantile loss. For quantile q, the model will attempt to produce predictions such that true_label < prediction with probability q.

(float): Parameter for epsilon insensitive loss type. During training and metric evaluation, any error smaller than this is considered to be zero.

(float): Parameter for Huber loss. During training and metric evaluation, compute L2 loss for errors smaller than delta and L1 loss for errors larger than delta.

(int): The number of epochs to wait before ending training if no improvement is made. The improvement is training loss if validation data is not provided, or else it is the validation loss or the binary classification model selection criteria like accuracy, f1-score etc. To disable early stopping, set early_stopping_patience to a value larger than epochs.

(float): Relative tolerance to measure an improvement in loss. If the ratio of the improvement in loss divided by the previous best loss is smaller than this value, early stopping will consider the improvement to be zero.

(int): The number of classes for the response variable. Required when predictor_type is multiclass_classifier and ignored otherwise. The classes are assumed to be labeled 0, ..., num_classes - 1.

(int): The value of k when computing the Top K Accuracy metric for multiclass classification. An example is scored as correct if the model assigns one of the top k scores to the true label.

(float): The value of beta to use when calculating F score metrics for binary or multiclass classification. Also used if binary_classifier_model_selection_criteria is f_beta.

(bool): Whether to use class weights which give each class equal importance in the loss function. Only used when predictor_type is multiclass_classifier.

: base class keyword argument values.

(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 kwargs passed to the LinearLearnerModel constructor.

(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.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(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.

(str): Path (absolute or relative) to the 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“.

(str): MXNet version you want to use for executing your model training code. Defaults to 'None'. Required unless “image_uri“ is provided. List of supported versions. https://github.com/aws/sagemaker-python-sdk#mxnet-sagemaker-estimators.

(str): Python version you want to use for executing your model training code. One of 'py2' or 'py3'. Defaults to “None“. Required unless “image_uri“ is provided.

(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.

(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): If specified, the estimator will use this image for training and hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples: * “123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0“ * “custom-image:latest“ If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

(dict): A dictionary with information on how to run distributed training (default: None). Currently we support distributed training with parameter server and MPI [Horovod].

: Additional kwargs passed to the :class:'~sagemaker.estimator.Framework' constructor.

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

(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.

(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.

(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.

(str): Path (absolute or relative) to a directory with any other serving source code dependencies aside from the entry point file. If not specified, the model source directory from training is used.

(list[str]): A list of paths to directories (absolute or relative) with any additional libraries that will be exported to the container. If not specified, the dependencies from training are used. This is not supported with "local code" in Local Mode.

(str): If specified, the estimator will use this image for hosting, instead of selecting the appropriate SageMaker official image based on framework_version and py_version. It can be an ECR url or dockerhub image and tag. Examples: * “123412341234.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0“ * “custom-image:latest“

: Additional kwargs passed to the :class:'~sagemaker.mxnet.model.MXNetModel' constructor.

Whether to make a deep clone.

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

(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.

(str): Path (absolute or relative) to the Python source file which should be executed as the entry point to model hosting. If “source_dir“ is specified, then “entry_point“ must point to a file located at the root of “source_dir“.

(str): MXNet version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided.

(str): Python version you want to use for executing your model training code. Defaults to “None“. Required unless “image_uri“ is provided.

(str): A Docker image URI (default: None). If not specified, a default image for MXNet will be used. If “framework_version“ or “py_version“ are “None“, then “image_uri“ is required. If also “None“, then a “ValueError“ will be raised.

(callable[str, sagemaker.session.Session]): A function to call to create a predictor with an endpoint name and SageMaker “Session“. If specified, “deploy()“ returns the result of invoking this function on the created endpoint name.

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

: Keyword arguments passed to the superclass :class:'~sagemaker.model.FrameworkModel' and, subsequently, its superclass :class:'~sagemaker.model.Model'.

(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'.

(str): AWS region where the image is uploaded.

(str): SageMaker instance type. Used to determine device type (cpu/gpu/family-specific optimized).

(str): The Elastic Inference accelerator type to deploy to the instance for loading and making inferences to the model (default: None). For example, 'ml.eia1.medium'.

Whether to make a deep clone.

(str): The name of the endpoint to perform inference on.

(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.

(callable): Optional. Default serializes input data to json. Handles dicts, lists, and numpy arrays.

(callable): Optional. Default parses the response using “json.load(...)“.

Whether to make a deep clone.

(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).

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 accessing 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): Required. The number of topics for NTM to find within the data.

(list): Optional. Represents number of layers in the encoder and the output size of each layer.

(int): Optional. Maximum number of passes over the training data.

(str): Optional. Activation function to use in the encoder layers.

(str): Optional. Optimizer to use for training.

(float): Optional. Maximum relative change in the loss function within the last num_patience_epochs number of epochs below which early stopping is triggered.

(int): Optional. Number of successive epochs over which early stopping criterion is evaluated.

(bool): Optional. Whether to use batch normalization during training.

(float): Optional. Rescale factor for gradient.

(float): Optional. Maximum magnitude for each gradient component.

(float): Optional. Weight decay coefficient. Adds L2 regularization.

(float): Optional. Learning rate for the optimizer.

: base class keyword argument values.

(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 kwargs passed to the NTMModel constructor.

(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.

Whether to make a deep clone.

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

(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.

(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.

: Keyword arguments passed to the “FrameworkModel“ initializer.

Whether to make a deep clone.

(str): Name of the Amazon SageMaker endpoint to which requests are sent.

(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.