Package: lookoutequipment
Amazon Lookout for Equipment Python ToolboxThe Amazon Lookout for Equipment Python toolbox is an open-source library that
allows data scientists and software developers to easily build, train and deploy
anomaly detection models for industrial time series data usingAmazon Lookout for Equipment.
This library is a wrapper on top of the Lookout for Equipment boto3 API and is
provided to kick start your journey with this service. Should you have any improvement
suggestions or bugs to report, please file an issue against this GitHub repository.The Amazon Lookout for Equipment Python toolbox enables you to do the following:Build dataset schemaData upload to the necessary S3 structureTrain an anomaly detection model using Amazon Lookout for EquipmentBuild beautiful visualization for your model evaluationConfigure and start an inference schedulerManage schedulers (start, stop, delete) whenever necessaryVisualize scheduler inferences resultsGetting Started With Sample Jupyter NotebooksThe best way to quickly review how the Amazon Lookout for Equipment Python toolbox
works is to review the related example notebook.Thisnotebookprovides code and descriptions for creating
and running a full project in Amazon Lookout for Equipment using the Amazon Lookout
for Equipment Python toolbox.Example Notebooks in SageMakerIn Amazon SageMaker, upload the Jupyter notebook from theexamples/folder of this repository.To run this exampleCreate a Notebook Instancein SageMaker.Add an inline policy to your Amazon SageMaker role in IAM with the following JSON structure{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Action":["lookoutequipment:*"],"Resource":"*"}]}Upload the Jupyter notebook fromexamples/folder.Run the notebook cellsInstalling the Amazon Lookout for Equipment Python toolboxThe Amazon Lookout for Equipment Python toolbox is built to PyPI and can be
installed with pip as follows:pip install lookoutequipmentYou can install from source by cloning this repository and running a pip install
command in the root directory of the repository:git clone https://github.com/aws-samples/amazon-lookout-for-equipment-python-sdk.git
	cd amazon-lookout-for-equipment-python-sdk
	pip install .Supported Operating SystemsThe Amazon Lookout for Equipment Python toolbox supports Unix/Linux and Mac.Supported Python VersionsThe Amazon Lookout for Equipment Python toolbox is tested on:Python 3.6Overview of toolboxThe Amazon Lookout for Equipment Python toolbox provides a Python API that enables
you to easily build, train and deploy anomaly detection models for industrial
time series data using Amazon Lookout for Equipment, and directly in your python
code and Jupyter notebooks.Using this toolbox you can:Build dataset schemaData upload to the necessary S3 structureTrain an anomaly detection model using Amazon Lookout for EquipmentBuild beautiful visualization for your model evaluationConfigure and start an inference schedulerManage schedulers (start, stop, delete) whenever necessaryVisualize scheduler inferences resultsFor a detailed API reference of the Amazon Lookout for Equipment Python toolbox,
be sure to view its documentation hosted onreadthedocs.Amazon Lookout for EquipmentAmazon Lookout for Equipment uses the data from your sensors to detect abnormal
equipment behavior, so you can take action before machine failures occur and
avoid unplanned downtime.AWS PermissionsAs a managed service, Amazon Lookout for Equipment performs operations on your
behalf on AWS hardware that is managed by Amazon Lookout for Equipment. Amazon
Lookout for Equipment can perform only operations that the user permits.  You
can read more about which permissions are necessary in theAWS Documentation.The Amazon Lookout for Equipment Python toolbox should not require any additional
permissions aside from what is required for usingboto3However, if you are
using an IAM role with a path in it, you should grant permission foriam:GetRole.SecuritySeeCONTRIBUTING.mdfor more information.LicensingAmazon Lookout for Equipment Python toolbox is licensed under the Apache 2.0
License. It is copyright 2021 Amazon.com, Inc. or its affiliates. All Rights
Reserved. The license is available at:http://aws.amazon.com/apache2.0/.