Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
Time Series Clustering (CNN-KMeans) Free trial
By:
Latest Version:
1.7
Perform time series clustering in SageMaker with Convolutional Neural Networks and K-Means.
Product Overview
This algorithm performs time series clustering with an unsupervised convolutional neural network trained using contrastive learning followed by a K-Means clusterer. It implements both training and inference from CSV data and supports both CPU and GPU instances. The training and inference Docker images were built by extending the PyTorch 2.1.0 Python 3.10 SageMaker containers. The Docker images include software licensed under the Apache License 2.0.
Key Data
Version
Categories
Type
Algorithm
Highlights
The algorithm performs time series clustering with a CNN-KMeans model directly from CSV data.
The algorithm allows tuning the model hyperparameters to optimize performance on custom datasets.
The algorithm supports both CPU and GPU training.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Algorithm Training$4.99/hr
running on ml.m5.2xlarge
Model Realtime Inference$0.99/hr
running on ml.m5.2xlarge
Model Batch Transform$4.99/hr
running on ml.m5.2xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Algorithm Training$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Realtime Inference$0.461/host/hr
running on ml.m5.2xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
About Free trial
Try this product for 5 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
Algorithm Training
For algorithm training in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Algorithm/hr | |
|---|---|---|
ml.m4.4xlarge | $4.99 | |
ml.c5n.18xlarge | $4.99 | |
ml.g4dn.4xlarge | $4.99 | |
ml.m5.4xlarge | $4.99 | |
ml.m4.16xlarge | $4.99 | |
ml.m5.2xlarge Vendor Recommended | $4.99 | |
ml.p3.16xlarge | $4.99 | |
ml.g5.xlarge | $4.99 | |
ml.g5.12xlarge | $4.99 | |
ml.g4dn.2xlarge | $4.99 | |
ml.g5.4xlarge | $4.99 | |
ml.c5n.xlarge | $4.99 | |
ml.m4.2xlarge | $4.99 | |
ml.c5.2xlarge | $4.99 | |
ml.p3.2xlarge | $4.99 | |
ml.c4.2xlarge | $4.99 | |
ml.g4dn.12xlarge | $4.99 | |
ml.p4d.24xlarge | $4.99 | |
ml.m4.10xlarge | $4.99 | |
ml.c4.xlarge | $4.99 | |
ml.m5.24xlarge | $4.99 | |
ml.c5.xlarge | $4.99 | |
ml.g4dn.xlarge | $4.99 | |
ml.g5.48xlarge | $4.99 | |
ml.p2.xlarge | $4.99 | |
ml.m5.12xlarge | $4.99 | |
ml.g4dn.16xlarge | $4.99 | |
ml.p2.16xlarge | $4.99 | |
ml.c4.4xlarge | $4.99 | |
ml.g5.8xlarge | $4.99 | |
ml.m5.xlarge | $4.99 | |
ml.c5.9xlarge | $4.99 | |
ml.g5.16xlarge | $4.99 | |
ml.m4.xlarge | $4.99 | |
ml.c5.4xlarge | $4.99 | |
ml.p3.8xlarge | $4.99 | |
ml.m5.large | $4.99 | |
ml.c4.8xlarge | $4.99 | |
ml.c5n.2xlarge | $4.99 | |
ml.p2.8xlarge | $4.99 | |
ml.g4dn.8xlarge | $4.99 | |
ml.c5n.9xlarge | $4.99 | |
ml.c5.18xlarge | $4.99 | |
ml.g5.2xlarge | $4.99 | |
ml.c5n.4xlarge | $4.99 | |
ml.g5.24xlarge | $4.99 |
Usage Information
Training
The input dataset should be provided as a CSV file. The CSV file should not contain any index column or column headers. Each row of the CSV file represents a time series, while each column represents a time step. The time series can have different lengths and can contain missing values. The time series are scaled internally by the algorithm, there is no need to scale the time series beforehand.
Metrics
Name | Regex |
|---|---|
train_loss | train:loss ([0-9\\.]+) |
valid_loss | valid:loss ([0-9\\.]+) |
Channel specification
Fields marked with * are required
training
*Input modes: File
Content types: text/csv
Compression types: None
validation
Input modes: File
Content types: text/csv
Compression types: None
Hyperparameters
Fields marked with * are required
clusters
*The number of clusters
Type: Integer
Tunable: Yes
algorithm
*The clustering algorithm
Type: Categorical
Tunable: Yes
blocks
*The number of blocks of convolutional layers
Type: Integer
Tunable: Yes
filters
*The number of filters of all but the last convolutional layers
Type: Integer
Tunable: Yes
kernel-size
*The size of the kernel of all non-residual convolutional layers
Type: Integer
Tunable: Yes
reduced-size
*The number of filters of the last convolutional layer
Type: Integer
Tunable: Yes
output-size
*The number of hidden units of the linear output layer
Type: Integer
Tunable: Yes
negative-samples
*The number of negative samples used for calculating the triplet loss
Type: Integer
Tunable: Yes
lr
*The learning rate used for training
Type: Continuous
Tunable: Yes
batch-size
*The batch size used for training
Type: Integer
Tunable: Yes
epochs
*The number of training epochs
Type: Integer
Tunable: Yes
Model input and output details
Input
Summary
The inference algorithm takes as input a CSV file containing the time series. The CSV file should not contain any index column or column headers. Each row of the CSV file represents a time series, while each column represents a time step. The time series can have different lengths and can contain missing values. The time series are scaled internally by the algorithm, there is no need to scale the time series beforehand.
Input MIME type
text/csvSample input data
Output
Summary
The inference algorithm outputs the predicted cluster labels and the extracted features, which are returned in CSV format. The predicted cluster labels are included in the first column, while the extracted features are included in the subsequent columns.
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Time Series Clustering (CNN-KMeans)
AWS Infrastructure
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Learn MoreRefund Policy
Subscriptions are not refundable.
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