mirror of https://github.com/microsoft/autogen.git
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
import time
|
|
from azureml.core import Workspace, Experiment, ScriptRunConfig, Environment
|
|
from azureml.core.runconfig import RunConfiguration, DockerConfiguration
|
|
|
|
ws = Workspace.from_config()
|
|
ray_environment_name = "aml-ray-cpu"
|
|
ray_environment_dockerfile_path = "./Docker/Dockerfile-cpu"
|
|
|
|
# Build CPU image for Ray
|
|
ray_cpu_env = Environment.from_dockerfile(
|
|
name=ray_environment_name, dockerfile=ray_environment_dockerfile_path
|
|
)
|
|
ray_cpu_env.register(workspace=ws)
|
|
ray_cpu_build_details = ray_cpu_env.build(workspace=ws)
|
|
|
|
while ray_cpu_build_details.status not in ["Succeeded", "Failed"]:
|
|
print(
|
|
f"Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}"
|
|
)
|
|
time.sleep(10)
|
|
|
|
command = ["python distribute_tune.py"]
|
|
env = Environment.get(workspace=ws, name=ray_environment_name)
|
|
compute_target = ws.compute_targets["cpucluster"]
|
|
aml_run_config = RunConfiguration(communicator="OpenMpi")
|
|
aml_run_config.target = compute_target
|
|
aml_run_config.docker = DockerConfiguration(use_docker=True)
|
|
aml_run_config.environment = env
|
|
aml_run_config.node_count = 2
|
|
config = ScriptRunConfig(
|
|
source_directory="ray/",
|
|
command=command,
|
|
run_config=aml_run_config,
|
|
)
|
|
|
|
exp = Experiment(ws, "distribute-tune")
|
|
run = exp.submit(config)
|
|
print(run.get_portal_url()) # link to ml.azure.com
|
|
run.wait_for_completion(show_output=True)
|