You can also find a completed Jupyter Notebook version of this guide on the GitHub samples page.
Install the Azure Machine Learning SDK (>= 1.15.0).In the samples deep learning folder on the notebook server, find a completed and expanded notebook by navigating to this directory: how-to-use-azureml > ml-frameworks > pytorch > train-hyperparameter-tune-deploy-with-pytorch folder.Complete the Quickstart: Get started with Azure Machine Learning to create a dedicated notebook server pre-loaded with the SDK and the sample repository.
Run this code on either of these environments:Īzure Machine Learning compute instance - no downloads or installation necessary You can build, deploy, version, and monitor production-grade models with Azure Machine Learning. Whether you're training a deep learning PyTorch model from the ground-up or you're bringing an existing model into the cloud, you can use Azure Machine Learning to scale out open-source training jobs using elastic cloud compute resources. To learn more about transfer learning, see the deep learning vs machine learning article. Transfer learning shortens the training process by requiring less data, time, and compute resources than training from scratch. Transfer learning is a technique that applies knowledge gained from solving one problem to a different but related problem.
The example scripts in this article are used to classify chicken and turkey images to build a deep learning neural network (DNN) based on PyTorch's transfer learning tutorial.
In this article, learn how to run your PyTorch training scripts at enterprise scale using Azure Machine Learning.