Run an experiment in Azure Machine Learning

Benefits of using Experiments in Azure Machine Learning

Azure Machine Learning is an all-encompassing machine learning environment for data science teams. It is useful when running everything from small tests to large production systems.

One concept that is central to Azure Machine Learning is that of the 'experiment'. This is the way that you can run a script which will train your model and log key metrics.

Code example for running Experiments in Azure ML

In the below example I show how you can run an experiment on a remote compute instance. This example assumes that you have already created a workspace within Azure as well as a compute instance.

from azureml.core import (
    Workspace,
    Experiment,
    Environment,
    RunConfiguration,
    ScriptRunConfig,
)

#Define the name of your created compute instance
compute_name = 'my-compute'
#Define the name of your new experiment and environment
experiment_name = 'my-experiment'
environment_name = 'my-environment'
#Define the directory you want to run the experiment from
source_directory = '.'
#Define the entry script for the experiment
script_path = 'code/train.py'
#Define the location of the machine learning workspace
subscription_id = 'subscription-id'
resource_group  = 'resource-group'
workspace_name  = 'my-workspace'

#Connect to your workspace
ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)

#Use the workspace to create an experiment
exp = Experiment(workspace=ws, name=experiment_name)

#Create an environment with the packages you need
env = Environment(name=environment_name)

for pip_package in ['numpy==1.19.2','pandas==1.0.5']:
    env.python.conda_dependencies.add_pip_package(pip_package)


#Create a run configuration to connect our environment and compute
run_config = RunConfiguration()
run_config.target = compute_name
run_config.environment = env

#Create a script run config to tie all the elements together
config = ScriptRunConfig(
    source_directory=source_directory, script=script_path, run_config=run_config
)

#Submitting the experiment will start it
run = exp.submit(config)

#Wait for the completion and show the output of the experiment as we go
run.wait_for_completion(show_output=True, wait_post_processing=True)

Related articles

Schedule pipelines and experiments
Register model in Azure Machine Learning
Deploy machine learning model to ACI in Azure Machine Learning

References

Azure Machine Learning
Experiment documentation