class_docstr: An unsupervised learning algorithm that learns the usage patterns for IPv4 addresses.
  
  It is designed to capture associations between IPv4 addresses and various entities, such
  as user IDs or account numbers.
docstr: This estimator is for IP Insights.
  
  An unsupervised algorithm that learns usage patterns of IP addresses.
  
  This Estimator may be fit via calls to
  :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`.
  It requires CSV data to be stored in S3.
  
  After this Estimator is fit, model data is stored in S3. The model
  may be deployed to an Amazon SageMaker Endpoint by invoking
  :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as
  deploying an Endpoint, deploy returns a
  :class:`~sagemaker.amazon.IPInsightPredictor` object that can be used
  for inference calls using the trained model hosted in the SageMaker
  Endpoint.
  
  IPInsights Estimators can be configured by setting hyperparamters.
  The available hyperparamters are documented below.
  
  For further information on the AWS IPInsights algorithm, please
  consult AWS technical documentation:
  https://docs.aws.amazon.com/sagemaker/latest/dg/ip-insights-hyperparameters.html
  
  Args:
      role (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.
      instance_count (int or PipelineVariable): Number of Amazon EC2 instances to use
          for training.
      instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
          for example, 'ml.m5.xlarge'.
      num_entity_vectors (int): Required. The number of embeddings to
          train for entities accessing online resources. We recommend 2x
          the total number of unique entity IDs.
      vector_dim (int): Required. The size of the embedding vectors for
          both entity and IP addresses.
      batch_metrics_publish_interval (int): Optional. The period at which
          to publish metrics (batches).
      epochs (int): Optional. Maximum number of passes over the training
          data.
      learning_rate (float): Optional. Learning rate for the optimizer.
      num_ip_encoder_layers (int): Optional. The number of fully-connected
          layers to encode IP address embedding.
      random_negative_sampling_rate (int): Optional. The ratio of random
          negative samples to draw during training. Random negative
          samples are randomly drawn IPv4 addresses.
      shuffled_negative_sampling_rate (int): Optional. The ratio of
          shuffled negative samples to draw during training. Shuffled
          negative samples are IP addresses picked from within a batch.
      weight_decay (float): Optional. Weight decay coefficient. Adds L2
          regularization.
      **kwargs: base class keyword argument values.
  
  .. tip::
  
      You can find additional parameters for initializing this class at
      :class:`~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase` and
      :class:`~sagemaker.estimator.EstimatorBase`.
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code: def __init__(
      self,
      role: Optional[Union[str, PipelineVariable]] = None,
      instance_count: Optional[Union[int, PipelineVariable]] = None,
      instance_type: Optional[Union[str, PipelineVariable]] = None,
      num_entity_vectors: Optional[int] = None,
      vector_dim: Optional[int] = None,
      batch_metrics_publish_interval: Optional[int] = None,
      epochs: Optional[int] = None,
      learning_rate: Optional[float] = None,
      num_ip_encoder_layers: Optional[int] = None,
      random_negative_sampling_rate: Optional[int] = None,
      shuffled_negative_sampling_rate: Optional[int] = None,
      weight_decay: Optional[float] = None,
      **kwargs,
  ):
      """This estimator is for IP Insights.
  
      An unsupervised algorithm that learns usage patterns of IP addresses.
  
      This Estimator may be fit via calls to
      :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`.
      It requires CSV data to be stored in S3.
  
      After this Estimator is fit, model data is stored in S3. The model
      may be deployed to an Amazon SageMaker Endpoint by invoking
      :meth:`~sagemaker.amazon.estimator.EstimatorBase.deploy`. As well as
      deploying an Endpoint, deploy returns a
      :class:`~sagemaker.amazon.IPInsightPredictor` object that can be used
      for inference calls using the trained model hosted in the SageMaker
      Endpoint.
  
      IPInsights Estimators can be configured by setting hyperparamters.
      The available hyperparamters are documented below.
  
      For further information on the AWS IPInsights algorithm, please
      consult AWS technical documentation:
      https://docs.aws.amazon.com/sagemaker/latest/dg/ip-insights-hyperparameters.html
  
      Args:
          role (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.
          instance_count (int or PipelineVariable): Number of Amazon EC2 instances to use
              for training.
          instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
              for example, 'ml.m5.xlarge'.
          num_entity_vectors (int): Required. The number of embeddings to
              train for entities accessing online resources. We recommend 2x
              the total number of unique entity IDs.
          vector_dim (int): Required. The size of the embedding vectors for
              both entity and IP addresses.
          batch_metrics_publish_interval (int): Optional. The period at which
              to publish metrics (batches).
          epochs (int): Optional. Maximum number of passes over the training
              data.
          learning_rate (float): Optional. Learning rate for the optimizer.
          num_ip_encoder_layers (int): Optional. The number of fully-connected
              layers to encode IP address embedding.
          random_negative_sampling_rate (int): Optional. The ratio of random
              negative samples to draw during training. Random negative
              samples are randomly drawn IPv4 addresses.
          shuffled_negative_sampling_rate (int): Optional. The ratio of
              shuffled negative samples to draw during training. Shuffled
              negative samples are IP addresses picked from within a batch.
          weight_decay (float): Optional. Weight decay coefficient. Adds L2
              regularization.
          **kwargs: base class keyword argument values.
  
      .. tip::
  
          You can find additional parameters for initializing this class at
          :class:`~sagemaker.estimator.amazon_estimator.AmazonAlgorithmEstimatorBase` and
          :class:`~sagemaker.estimator.EstimatorBase`.
      """
      super(IPInsights, self).__init__(
          role, instance_count, instance_type, **kwargs
      )
      self.num_entity_vectors = num_entity_vectors
      self.vector_dim = vector_dim
      self.batch_metrics_publish_interval = (
          batch_metrics_publish_interval
      )
      self.epochs = epochs
      self.learning_rate = learning_rate
      self.num_ip_encoder_layers = num_ip_encoder_layers
      self.random_negative_sampling_rate = random_negative_sampling_rate
      self.shuffled_negative_sampling_rate = (
          shuffled_negative_sampling_rate
      )
      self.weight_decay = weight_decay
  
imports: from __future__ import absolute_import
  from typing import Union, Optional
  from sagemaker import image_uris
  from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.validation import ge, le
  from sagemaker.deserializers import JSONDeserializer
  from sagemaker.predictor import Predictor
  from sagemaker.model import Model
  from sagemaker.serializers import CSVSerializer
  from sagemaker.session import Session
  from sagemaker.utils import pop_out_unused_kwarg
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 1 to create a new IPInsights object from inherited base classes: AmazonAlgorithmEstimatorBase with example: obj = IPInsights(role, instance_count, instance_type, num_entity_vectors, vector_dim, batch_metrics_publish_interval, epochs, learning_rate, num_ip_encoder_layers, random_negative_sampling_rate, shuffled_negative_sampling_rate, weight_decay)