Create virtual environment to run experiment

from azureml.core import Environment
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.runconfig import RunConfiguration

# Create a Python environment for the experiment
diabetes_env = Environment("diabetes-pipeline-env")

# Create a set of package dependencies
diabetes_packages = CondaDependencies.create(conda_packages=['scikit-learn','ipykernel','matplotlib','pandas','pip'],
                                             pip_packages=['azureml-defaults','azureml-dataprep[pandas]','pyarrow'])

# Add the dependencies to the environment
diabetes_env.python.conda_dependencies = diabetes_packages

# Register the environment 
diabetes_env.register(workspace=ws)
registered_env = Environment.get(ws, 'diabetes-pipeline-env')

# Create a new runconfig object for the pipeline
pipeline_run_config = RunConfiguration()

# Use the compute you created above. 
pipeline_run_config.target = pipeline_cluster

# Assign the environment to the run configuration
pipeline_run_config.environment = registered_env

print ("Run configuration created.")