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Adding Object2Vec support to SageMaker Python SDK (#467)
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| # Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"). You | ||
| # may not use this file except in compliance with the License. A copy of | ||
| # the License is located at | ||
| # | ||
| # http://aws.amazon.com/apache2.0/ | ||
| # | ||
| # or in the "license" file accompanying this file. This file is | ||
| # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF | ||
| # ANY KIND, either express or implied. See the License for the specific | ||
| # language governing permissions and limitations under the License. | ||
| from __future__ import absolute_import | ||
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| from sagemaker.amazon.amazon_estimator import AmazonAlgorithmEstimatorBase, registry | ||
| from sagemaker.amazon.hyperparameter import Hyperparameter as hp # noqa | ||
| from sagemaker.amazon.validation import ge, le, isin | ||
| from sagemaker.predictor import RealTimePredictor | ||
| from sagemaker.model import Model | ||
| from sagemaker.session import Session | ||
| from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT | ||
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| class Object2Vec(AmazonAlgorithmEstimatorBase): | ||
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| repo_name = 'object2vec' | ||
| repo_version = 1 | ||
| MINI_BATCH_SIZE = 32 | ||
|
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| enc_dim = hp('enc_dim', (ge(4), le(10000)), | ||
| 'An integer in [4, 10000]', int) | ||
| mini_batch_size = hp('mini_batch_size', (ge(1), le(10000)), | ||
| 'An integer in [1, 10000]', int) | ||
| epochs = hp('epochs', (ge(1), le(100)), | ||
| 'An integer in [1, 100]', int) | ||
| early_stopping_patience = hp('early_stopping_patience', (ge(1), le(5)), | ||
| 'An integer in [1, 5]', int) | ||
| early_stopping_tolerance = hp('early_stopping_tolerance', (ge(1e-06), le(0.1)), | ||
| 'A float in [1e-06, 0.1]', float) | ||
| dropout = hp('dropout', (ge(0.0), le(1.0)), | ||
| 'A float in [0.0, 1.0]', float) | ||
| weight_decay = hp('weight_decay', (ge(0.0), le(10000.0)), | ||
| 'A float in [0.0, 10000.0]', float) | ||
| bucket_width = hp('bucket_width', (ge(0), le(100)), | ||
| 'An integer in [0, 100]', int) | ||
| num_classes = hp('num_classes', (ge(2), le(30)), | ||
| 'An integer in [2, 30]', int) | ||
| mlp_layers = hp('mlp_layers', (ge(1), le(10)), | ||
| 'An integer in [1, 10]', int) | ||
| mlp_dim = hp('mlp_dim', (ge(2), le(10000)), | ||
| 'An integer in [2, 10000]', int) | ||
| mlp_activation = hp('mlp_activation', isin("tanh", "relu", "linear"), | ||
| 'One of "tanh", "relu", "linear"', str) | ||
| output_layer = hp('output_layer', isin("softmax", "mean_squared_error"), | ||
| 'One of "softmax", "mean_squared_error"', str) | ||
| optimizer = hp('optimizer', isin("adagrad", "adam", "rmsprop", "sgd", "adadelta"), | ||
| 'One of "adagrad", "adam", "rmsprop", "sgd", "adadelta"', str) | ||
| learning_rate = hp('learning_rate', (ge(1e-06), le(1.0)), | ||
| 'A float in [1e-06, 1.0]', float) | ||
| enc0_network = hp('enc0_network', isin("hcnn", "bilstm", "pooled_embedding"), | ||
| 'One of "hcnn", "bilstm", "pooled_embedding"', str) | ||
| enc1_network = hp('enc1_network', isin("hcnn", "bilstm", "pooled_embedding", "enc0"), | ||
| 'One of "hcnn", "bilstm", "pooled_embedding", "enc0"', str) | ||
| enc0_cnn_filter_width = hp('enc0_cnn_filter_width', (ge(1), le(9)), | ||
| 'An integer in [1, 9]', int) | ||
| enc1_cnn_filter_width = hp('enc1_cnn_filter_width', (ge(1), le(9)), | ||
| 'An integer in [1, 9]', int) | ||
| enc0_max_seq_len = hp('enc0_max_seq_len', (ge(1), le(5000)), | ||
| 'An integer in [1, 5000]', int) | ||
| enc1_max_seq_len = hp('enc1_max_seq_len', (ge(1), le(5000)), | ||
| 'An integer in [1, 5000]', int) | ||
| enc0_token_embedding_dim = hp('enc0_token_embedding_dim', (ge(2), le(1000)), | ||
| 'An integer in [2, 1000]', int) | ||
| enc1_token_embedding_dim = hp('enc1_token_embedding_dim', (ge(2), le(1000)), | ||
| 'An integer in [2, 1000]', int) | ||
| enc0_vocab_size = hp('enc0_vocab_size', (ge(2), le(3000000)), | ||
| 'An integer in [2, 3000000]', int) | ||
| enc1_vocab_size = hp('enc1_vocab_size', (ge(2), le(3000000)), | ||
| 'An integer in [2, 3000000]', int) | ||
| enc0_layers = hp('enc0_layers', (ge(1), le(4)), | ||
| 'An integer in [1, 4]', int) | ||
| enc1_layers = hp('enc1_layers', (ge(1), le(4)), | ||
| 'An integer in [1, 4]', int) | ||
| enc0_freeze_pretrained_embedding = hp('enc0_freeze_pretrained_embedding', (), | ||
| 'Either True or False', bool) | ||
| enc1_freeze_pretrained_embedding = hp('enc1_freeze_pretrained_embedding', (), | ||
| 'Either True or False', bool) | ||
|
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| def __init__(self, role, train_instance_count, train_instance_type, | ||
| epochs, | ||
| enc0_max_seq_len, | ||
| enc0_vocab_size, | ||
| enc_dim=None, | ||
| mini_batch_size=None, | ||
| early_stopping_patience=None, | ||
| early_stopping_tolerance=None, | ||
| dropout=None, | ||
| weight_decay=None, | ||
| bucket_width=None, | ||
| num_classes=None, | ||
| mlp_layers=None, | ||
| mlp_dim=None, | ||
| mlp_activation=None, | ||
| output_layer=None, | ||
| optimizer=None, | ||
| learning_rate=None, | ||
| enc0_network=None, | ||
| enc1_network=None, | ||
| enc0_cnn_filter_width=None, | ||
| enc1_cnn_filter_width=None, | ||
| enc1_max_seq_len=None, | ||
| enc0_token_embedding_dim=None, | ||
| enc1_token_embedding_dim=None, | ||
| enc1_vocab_size=None, | ||
| enc0_layers=None, | ||
| enc1_layers=None, | ||
| enc0_freeze_pretrained_embedding=None, | ||
| enc1_freeze_pretrained_embedding=None, | ||
| **kwargs): | ||
| """Object2Vec is :class:`Estimator` used for anomaly detection. | ||
| This Estimator may be fit via calls to | ||
| :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.fit`. | ||
| There is an utility :meth:`~sagemaker.amazon.amazon_estimator.AmazonAlgorithmEstimatorBase.record_set` that | ||
| can be used to upload data to S3 and creates :class:`~sagemaker.amazon.amazon_estimator.RecordSet` to be passed | ||
| to the `fit` call. | ||
| 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.RealTimePredictor` object that can be used | ||
| for inference calls using the trained model hosted in the SageMaker Endpoint. | ||
| Object2Vec Estimators can be configured by setting hyperparameters. The available hyperparameters for | ||
| Object2Vec are documented below. | ||
| For further information on the AWS Object2Vec algorithm, | ||
| please consult AWS technical documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/object2vec.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. | ||
| train_instance_count (int): Number of Amazon EC2 instances to use for training. | ||
| train_instance_type (str): Type of EC2 instance to use for training, for example, 'ml.c4.xlarge'. | ||
| epochs(int): Total number of epochs for SGD training | ||
| enc0_max_seq_len(int): Maximum sequence length | ||
| enc0_vocab_size(int): Vocabulary size of tokens | ||
| enc_dim(int): Optional. Dimension of the output of the embedding layer | ||
| mini_batch_size(int): Optional. mini batch size for SGD training | ||
| early_stopping_patience(int): Optional. The allowed number of consecutive epochs without improvement | ||
| before early stopping is applied | ||
| early_stopping_tolerance(float): Optional. The value used to determine whether the algorithm has made | ||
| improvement between two consecutive epochs for early stopping | ||
| dropout(float): Optional. Dropout probability on network layers | ||
| weight_decay(float): Optional. Weight decay parameter during optimization | ||
| bucket_width(int): Optional. The allowed difference between data sequence length when bucketing is enabled | ||
| num_classes(int): Optional. Number of classes for classification training (ignored for regression problems) | ||
| mlp_layers(int): Optional. Number of MLP layers in the network | ||
| mlp_dim(int): Optional. Dimension of the output of MLP layer | ||
| mlp_activation(str): Optional. Type of activation function for the MLP layer | ||
| output_layer(str): Optional. Type of output layer | ||
| optimizer(str): Optional. Type of optimizer for training | ||
| learning_rate(float): Optional. Learning rate for SGD training | ||
| enc0_network(str): Optional. Network model of encoder "enc0" | ||
| enc1_network(str): Optional. Network model of encoder "enc1" | ||
| enc0_cnn_filter_width(int): Optional. CNN filter width | ||
| enc1_cnn_filter_width(int): Optional. CNN filter width | ||
| enc1_max_seq_len(int): Optional. Maximum sequence length | ||
| enc0_token_embedding_dim(int): Optional. Output dimension of token embedding layer | ||
| enc1_token_embedding_dim(int): Optional. Output dimension of token embedding layer | ||
| enc1_vocab_size(int): Optional. Vocabulary size of tokens | ||
| enc0_layers(int): Optional. Number of layers in encoder | ||
| enc1_layers(int): Optional. Number of layers in encoder | ||
| enc0_freeze_pretrained_embedding(bool): Optional. Freeze pretrained embedding weights | ||
| enc1_freeze_pretrained_embedding(bool): Optional. Freeze pretrained embedding weights | ||
| **kwargs: base class keyword argument values. | ||
| """ | ||
|
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| super(Object2Vec, self).__init__(role, train_instance_count, train_instance_type, **kwargs) | ||
|
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| self.enc_dim = enc_dim | ||
| self.mini_batch_size = mini_batch_size | ||
| self.epochs = epochs | ||
| self.early_stopping_patience = early_stopping_patience | ||
| self.early_stopping_tolerance = early_stopping_tolerance | ||
| self.dropout = dropout | ||
| self.weight_decay = weight_decay | ||
| self.bucket_width = bucket_width | ||
| self.num_classes = num_classes | ||
| self.mlp_layers = mlp_layers | ||
| self.mlp_dim = mlp_dim | ||
| self.mlp_activation = mlp_activation | ||
| self.output_layer = output_layer | ||
| self.optimizer = optimizer | ||
| self.learning_rate = learning_rate | ||
| self.enc0_network = enc0_network | ||
| self.enc1_network = enc1_network | ||
| self.enc0_cnn_filter_width = enc0_cnn_filter_width | ||
| self.enc1_cnn_filter_width = enc1_cnn_filter_width | ||
| self.enc0_max_seq_len = enc0_max_seq_len | ||
| self.enc1_max_seq_len = enc1_max_seq_len | ||
| self.enc0_token_embedding_dim = enc0_token_embedding_dim | ||
| self.enc1_token_embedding_dim = enc1_token_embedding_dim | ||
| self.enc0_vocab_size = enc0_vocab_size | ||
| self.enc1_vocab_size = enc1_vocab_size | ||
| self.enc0_layers = enc0_layers | ||
| self.enc1_layers = enc1_layers | ||
| self.enc0_freeze_pretrained_embedding = enc0_freeze_pretrained_embedding | ||
| self.enc1_freeze_pretrained_embedding = enc1_freeze_pretrained_embedding | ||
|
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| def create_model(self, vpc_config_override=VPC_CONFIG_DEFAULT): | ||
| """Return a :class:`~sagemaker.amazon.Object2VecModel` referencing the latest | ||
| s3 model data produced by this Estimator. | ||
| Args: | ||
| vpc_config_override (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. | ||
| """ | ||
| return Object2VecModel(self.model_data, self.role, sagemaker_session=self.sagemaker_session, | ||
| vpc_config=self.get_vpc_config(vpc_config_override)) | ||
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| def _prepare_for_training(self, records, mini_batch_size=None, job_name=None): | ||
| if mini_batch_size is None: | ||
| mini_batch_size = self.MINI_BATCH_SIZE | ||
|
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| super(Object2Vec, self)._prepare_for_training(records, mini_batch_size=mini_batch_size, job_name=job_name) | ||
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| class Object2VecModel(Model): | ||
| """Reference Object2Vec s3 model data. Calling :meth:`~sagemaker.model.Model.deploy` creates an | ||
| Endpoint and returns a Predictor that calculates anomaly scores for datapoints.""" | ||
|
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| def __init__(self, model_data, role, sagemaker_session=None, **kwargs): | ||
| sagemaker_session = sagemaker_session or Session() | ||
| repo = '{}:{}'.format(Object2Vec.repo_name, Object2Vec.repo_version) | ||
| image = '{}/{}'.format(registry(sagemaker_session.boto_session.region_name, | ||
| Object2Vec.repo_name), repo) | ||
| super(Object2VecModel, self).__init__(model_data, image, role, | ||
| predictor_cls=RealTimePredictor, | ||
| sagemaker_session=sagemaker_session, | ||
| **kwargs) |
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