Path4.Mod1.b - Training Models with Scripts - Specifying an Environment for a Command Job

Question 1

When specifying an Environment to your Command job, you can use any one of these

Answer 1

• Workspace Environments: Pre-built Environments that come with every Azure ML Workspace
• Docker Environments: Existing Docker Images that already contain packages needed to run your script
• Custom Environments (conda.yml): Same concept as noted before

Question 2

Describe the Workspace Environment

Answer 2

Workspaces come with prebuilt Environments. To use one for a job, set the environment parameter to your desired Environment’s name as a string value.

Question 3

Code to get all Workspace Environments

Answer 3

To see of list of those Environments:

envs = ml_client.environments.list()

Question 4

Describe the Docker Environment

Answer 4

You can create an Environment instance using a Docker image containing pre-installed packages/dependencies. Ideal when the built-in ones don’t have what you need.

Question 5

Describe code for creating a Docker Environment in your Workspace

Answer 5

from azure.ai.ml.entities import Environment

// Create an Environment using a public Docker image URI
env_docker_image = Environment(
    image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04",
    name="docker-image-example_env",
    description="Environment created from a Docker image.",
)
ml_client.environments.create_or_update(env_docker_image)

Question 6

Describe why a Docker Enviroment may not be suitable for your needs and the alternative

Answer 6

The Docker image might lack dependencies your training process or pipeline may need. In that case you’d need to create a custom Environment.

Question 7

Describe Custom Environments and how they are defined

Answer 7

A Custom Environment can be built by specifying a conda.yml file, which lists all your requirements for running yoru pipeline. An example from our Jupyter Notebook (by way of the %%writefile command):

%%writefile src/conda-env.yml
name: basic-env-cpu
channels:
  - conda-forge
dependencies:
  - python=3.10
  - scikit-learn
  - pandas
  - numpy
  - matplotlib