Tracking your project’s software dependencies is an integral part of the machine learning lifecycle. But managing these entities and ensuring reproducibility can be a challenging process leading to delays in the training and deployment of models. Azure Machine Learning Environments capture the Python packages and Docker settings for that are used in machine learning experiments, including in data preparation, training, and deployment to a web service. And we are excited to announce the following feature releases:
The new Environments UI in Azure Machine Learning studio is now in public preview.
Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They are backed by cached Docker images that use the latest version of the Azure Machine Learning SDK and support popular machine learning frameworks and packages, reducing the run preparation cost and allowing for faster deployment time. Environment details as well as their Dockerfiles can be viewed through the Environments UI in the studio. Use these environments to quickly get started with PyTorch, Tensorflow, Sci-kit learn, and more.
At Microsoft Build 2021 we announced Public Preview of Prebuilt docker images and curated environments for Inferencing workloads. These docker images come with popular machine learning frameworks and Python packages. These are optimized for inferencing only and provided for CPU and GPU based scenarios. They are published to Microsoft Container Registry (MCR). Customers can pull our images directly from MCR or use Azure Machine Learning curated environments. The complete list of inference images is documented here: List of Prebuilt images and curated environments.
The difference between current base images and inference prebuilt docker images:
Use the environments to track and reproduce your projects' software dependencies as they evolve.
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