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 Classification (Inception) Free trial
By:
Latest Version:
1.9
Perform time series classification in SageMaker with the InceptionTime network.
Product Overview
This algorithm performs time series classification with the InceptionTime network. 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 algorithm can be used for binary, multiclass and multilabel classification of both univariate and multivariate time series.
Key Data
Version
Categories
Type
Algorithm
Highlights
The algorithm performs time series classification with the InceptionTime model directly from CSV data.
The algorithm allows tuning the model hyperparameters to optimize performance on custom datasets.
The algorithm supports CPU, GPU and multi-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 column names of the one-hot encoded class labels should start with "y" (e.g. "y1", "y2", ...), while the column names of the time series values should start with "x" (e.g. "x1", "x2", ...). The CSV file should contain unique sample identifiers in a column named "sample" and unique feature identifiers in a column named "feature". The feature identifiers are used to determine the different dimensions of multivariate time series. When using univariate time series, the feature identifiers can be set to a constant value.
Metrics
Name | Regex |
|---|---|
train_loss | train:loss ([0-9\\.]+) |
train_accuracy | train:acc ([0-9\\.]+) |
valid_loss | valid:loss ([0-9\\.]+) |
valid_accuracy | valid:acc ([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
model
Input modes: File
Content types: "model.tar.gz" file from a previous training job
Compression types: None
Hyperparameters
Fields marked with * are required
filters
*The number of filters of each model in the ensemble
Type: Integer
Tunable: Yes
depth
*The number of blocks of each model in the ensemble
Type: Integer
Tunable: Yes
models
*The number of models in the ensemble
Type: Integer
Tunable: Yes
task
*The type of classification task
Type: Categorical
Tunable: No
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 values. The column names of the time series values should start with "x" (e.g. "x1", "x2", ...). The CSV file should contain unique sample identifiers in a column named "sample" and unique feature identifiers in a column named "feature". The feature identifiers used for inference should match the ones used for training.
Input MIME type
text/csvSample input data
Output
Summary
The inference algorithm outputs the predicted class labels and the predicted class probabilities, which are returned in CSV format.
Output MIME type
text/csvSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Time Series Classification (Inception)
AWS Infrastructure
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Learn MoreRefund Policy
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