Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.
Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.
The SageMaker example notebooks are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.
Quantum machine learning (QML) has emerged as the potential solution to address the challenge of handling an ever-increasing amount of data. With these advancements there is potential for reduced training times, and hybrid quantum-classical architectures.
The quickest setup to run example notebooks includes:
- An AWS account
- Proper IAM User and Role setup
- An Amazon SageMaker Notebook Instance
- An S3 bucket
These examples provide quick walkthroughs to get you up and running with the labeling job workflow for Amazon SageMaker Ground Truth.
- Start Here is an end-to-end example that shows how to build sequential circuits that are optimized for circuit training.
These examples provide several examples how variational circuits can be trained.
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Training with Pennylane and Numpy This notebook shows how to use Pennylane's optimizers for circuit training, with Numpy to facilitate training.
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Training with PyTorch This notebook shows how to wrap circuits into a Pytorch layer, and model that can be trained.
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Training with Tensorflow This notebook shows how to wrap circuits into a Tensorflow layer, and model that can be trained. This method is currently limited by the issue stated here: PennyLaneAI/pennylane#937. The implication is that only circuit structures that have two expectation values can be trained.
This library is licensed under the Apache 2.0 License. For more details, please take a look at the LICENSE file.
Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources. Please bear with us in the short-term if pull requests take longer than expected or are closed. Please read our contributing guidelines if you'd like to open an issue or submit a pull request.