Asset naming convention

General

These conventions apply to all assets, regardless of type. Names must:

• Start with a letter.
• Use only lowercase letters, numbers, dashes, and periods.
• Be up to 255 characters long.
• Use dashes to separate words.
• Not include prefixes like "azureml" or "microsoft". Registries containing these assets will already provide this information.
• Not include the type of asset as a suffix (examples: "component" or "environment"). The asset type is already clearly represented via CLI, SDK, and UI.
• Not include a version number of the asset. Versions of included packages and frameworks are okay though.

Examples of valid asset names:

Name	Notes
tremendous-transform	Starts with a letter, uses lowercase letters and a dash
pytorch-1.9-cuda11.6-gpu	Version numbers refer to PyTorch and CUDA, not the asset itself

Examples of invalid asset names:

Name	Issue
microsoft_master_transform	Uses underscores instead of dashes
awesome-component	Ends with "component"
fastsupertransform	Should use dashes to separate words, but doesn't
1-amazing-sort	Doesn't start with a letter
pattern-detect-2.0	Asset version should be in its version, not its name

Components

If you have a lot of related components, consider using a prefix to help group them. For example: forecast-preprocessing, forecast-threat-model, etc.

Environments

Your environment will need a meaningful name that will make it easy to find. Follow the naming convention below:

<framework>-<framework-version>[-<gpu-driver><gpu-driver-version>-gpu][-inference]

Field	Description	Examples
framework	Machine learning framework supported by the environment	pytorch, sklearn, tensorflow
framework-version	Framework version, major and minor values only	1.1, 11.1, 2.8
gpu-driver	GPU driver	cuda
gpu-driver-version	GPU driver version, major and minor values only	11.6

Notes:

• Environments without a -gpu suffix are CPU-only
• Environments with an -inference suffix provide inferencing support

Examples:

• pytorch-1.9 (CPU version)
• pytorch-1.9-cuda11.6-gpu

Docker image names

To keep them easy to maintain, Docker image names should match the name of their associated environment.

Examples:

Environment Name	Docker Image Name
pytorch-1.9	azureml/curated/pytorch-1.9
pytorch-1.9-cuda11.6-gpu	azureml/curated/pytorch-1.9-cuda11.6-gpu