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Environments
In addition to the files common to all assets, the following are used for environments.
An Azure CLI v2 spec file. An environment example would look like this:
$schema: https://azuremlschemas.azureedge.net/latest/environment.schema.json
description: >-
A sample environment.
name: "{{asset.name}}"
version: "{{asset.version}}"
build:
path: "{{asset.repo.url}}#{{asset.repo.commit_hash}}:{{asset.repo.build_context.path}}"
dockerfile_path: "{{image.dockerfile.path}}"
os_type: linux
tags:
PyTorch: "1.11"
GPU: Cuda11
OS: Ubuntu20.04
Training: ""
Preview: ""
In addition to the template tags that are common to all assets, environment assets support the following additional ones:
Tag | Resolves to |
---|---|
{{asset.repo.build_context.path}} |
Path to the environment's Docker build context in the release branch |
{{image.name}} |
Name of the environment's Docker image |
{{image.context.path}} |
Path to the Docker build context, relative to the spec file |
{{image.dockerfile.path}} |
Path to the Dockerfile, relative to the Docker build context root |
{{image.publish.hostname}} |
Hostname of the container registry to which the image will be published |
An environment config file provides configuration specific to environments.
image: # Image configuration
name: azureml/curated/my_env # Name of the environment's Docker image
os: linux # OS type, either linux or windows
context: # Docker build context information
dir: context # Directory containing the build context, relative to the environment config file
dockerfile: Dockerfile # Dockerfile location, relative to the build context directory. Defaults to Dockerfile if unspecified.
template_files: # Optional list of files that contain template tags that should be resolved before building the image
- Dockerfile # Template tags in this file will be replaced
publish: # Publishing settings for the image
location: mcr # Location to which the image will be published. Must be set to mcr.
visibility: public # Visibility of published image. Options are public, internal, staging, or unlisted.
Note that the image name will need to be registered with MCR before the environment is added to azureml-assets. Otherwise, the environment will fail to create in any registry. The image name should largely match the environment name and be prefixed with azureml/curated/
.
The following template tags are supported:
Tag | Normally used in | Resolves to | Example |
---|---|---|---|
{{latest-image-tag}} |
Dockerfile |
Either an image tag that with the same digest as latest , or the digest itself if a tag is not found |
FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:{{latest-image-tag}} |
{{latest-image-tag:<some_regex>}} |
Dockerfile |
The most recent (descending lexicographical sort) tag that matches the provided regular expression | FROM mcr.microsoft.com/azureml/aifx/stable-ubuntu2004-cu113-py38-torch1110:{{latest-image-tag:monthly\.\d{6}}} |
{{latest-pypi-version}} |
requirements.txt or conda_dependencies.yaml
|
The latest version of a package in PyPI | azureml-core=={{latest-pypi-version}} |
{{latest-pypi-version:pre}} |
requirements.txt or conda_dependencies.yaml
|
The latest version of a package in PyPI, including prerelease ones | azureml-core=={{latest-pypi-version:pre}} |
Any of the above tags are resolved if they appear in any of the files listed in an environment config's template_files
property.
The tags
dictionary contains a list of asset tag names/values that are displayed in the UI. Empty values result in just the tag name being shown. Asset tags that apply to environments are:
Name | Example value | Description | Required |
---|---|---|---|
Cuda | 11.1.1 | CUDA version | |
CuDnn | 8.0.5.39 | cuDNN version | |
DeepSpeed | 0.7.3 | DeepSpeed version | |
Inferencing | <empty> | Supports AzureML inferencing | |
Nccl | 2.8.4 | NCCL version | |
OnnxRuntime | 1.12 | ONNX Runtime version | |
OpenMpi | 4.1.0 | Open MPI version | |
OS | Ubuntu20.04 | Operating system name and version | X |
Preview | <empty> | Public preview | |
Python | 3.9 | Python version | X |
PyTorch | 1.12 | PyTorch version | |
ScikitLearn | 1.1 | Scikit-learn version | |
TensorFlow | 2.8 | TensorFlow version |
To test your environment image builds locally, you can do something like the following:
cd <build_context_dir>
docker build . --file <dockerfile>
This isn't going to work if you're using template tags (and you should). An approach that handles template tags is:
cd <azureml-assets_repo>
pip install -e scripts/azureml-assets
cp -r <build_context_dir> /tmp/context
scripts/azureml-assets/azureml/assets/environment/pin_versions.py -i /tmp/context/<dockerfile>
# Repeat the above step for other files you need to update
docker build /tmp/context --file <dockerfile>