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9e88f22167
* fix a bug when using ray & update ray on aml When using with_parameters(), the config argument must be the first argument in the trainable function. * make training function runnable standalone
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import time
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from azureml.core import Workspace, Experiment, ScriptRunConfig, Environment
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from azureml.core.runconfig import RunConfiguration, DockerConfiguration
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ws = Workspace.from_config()
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ray_environment_name = "aml-ray-cpu"
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ray_environment_dockerfile_path = "./Docker/Dockerfile-cpu"
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# Build CPU image for Ray
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ray_cpu_env = Environment.from_dockerfile(
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name=ray_environment_name, dockerfile=ray_environment_dockerfile_path
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)
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ray_cpu_env.register(workspace=ws)
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ray_cpu_build_details = ray_cpu_env.build(workspace=ws)
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while ray_cpu_build_details.status not in ["Succeeded", "Failed"]:
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print(
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f"Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}"
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)
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time.sleep(10)
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command = ["python distribute_automl.py"]
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env = Environment.get(workspace=ws, name=ray_environment_name)
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compute_target = ws.compute_targets["cpucluster"]
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aml_run_config = RunConfiguration(communicator="OpenMpi")
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aml_run_config.target = compute_target
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aml_run_config.docker = DockerConfiguration(use_docker=True)
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aml_run_config.environment = env
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aml_run_config.node_count = 2
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config = ScriptRunConfig(
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source_directory="ray/",
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command=command,
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run_config=aml_run_config,
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)
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exp = Experiment(ws, "distribute-automl")
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run = exp.submit(config)
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print(run.get_portal_url()) # link to ml.azure.com
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run.wait_for_completion(show_output=True)
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