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Upgrade script run to SDK v2

  • Raksts

In SDK v2, "experiments" and "runs" are consolidated into jobs.

A job has a type. Most jobs are command jobs that run a command, like python main.py. What runs in a job is agnostic to any programming language, so you can run bash scripts, invoke python interpreters, run a bunch of curl commands, or anything else.

To upgrade, you'll need to change your code for submitting jobs to SDK v2. What you run within the job doesn't need to be upgraded to SDK v2. However, it's recommended to remove any code specific to Azure Machine Learning from your model training scripts. This separation allows for an easier transition between local and cloud and is considered best practice for mature MLOps. In practice, this means removing azureml.* lines of code. Model logging and tracking code should be replaced with MLflow. For more details, see how to use MLflow in v2.

This article gives a comparison of scenario(s) in SDK v1 and SDK v2.

Submit a script run

  • SDK v1

    from azureml.core import Workspace, Experiment, Environment, ScriptRunConfig

# connect to the workspace
ws = Workspace.from_config()

# define and configure the experiment
experiment = Experiment(workspace=ws, name='day1-experiment-train')
config = ScriptRunConfig(source_directory='./src',
                            script='train.py',
                            compute_target='cpu-cluster')

# set up pytorch environment
env = Environment.from_conda_specification(
    name='pytorch-env',
    file_path='pytorch-env.yml')
config.run_config.environment = env

run = experiment.submit(config)

aml_url = run.get_portal_url()
print(aml_url)

  • SDK v2

    #import required libraries
from azure.ai.ml import MLClient, command
from azure.ai.ml.entities import Environment
from azure.identity import DefaultAzureCredential

#connect to the workspace
ml_client = MLClient.from_config(DefaultAzureCredential())

# set up pytorch environment
env = Environment(
    image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04",
    conda_file="pytorch-env.yml",
    name="pytorch-env"
)

# define the command
command_job = command(
    code="./src",
    command="train.py",
    environment=env,
    compute="cpu-cluster",
)

returned_job = ml_client.jobs.create_or_update(command_job)
returned_job

Mapping of key functionality in v1 and v2

Functionality in SDK v1 Rough mapping in SDK v2
experiment.submit MLCLient.jobs.create_or_update
ScriptRunConfig() command()

Next steps

For more information, see: