Azure ML 순서

1. Azure ML 작업 영역하고 연결

import azureml.core
from azureml.core import Workspace

# check core SDK version number
print("Azure ML SDK Version: ", azureml.core.VERSION)

# load workspace configuration from the config.json file in the current folder.
ws = Workspace.from_config()
print(ws.name, ws.location, ws.resource_group, ws.location, sep = '\t')

 

2. 실험 만듬 - 불러온 작업영역 ws에 실험 이름 정하고 만듬

experiment_name = 'sklearn-mnist'

from azureml.core import Experiment
exp = Experiment(workspace=ws, name=experiment_name)

 

3. AML 컴퓨팅 만들기 or 연결

- 컴퓨팅 사용 : Azure 가상머신 클러스터에서 기계학습 모델 학습 가능 (ex) GPU지원되는 VM 있음

- 컴퓨팅 만드는데 약 5분 소요됨.

from azureml.core.compute import AmlCompute
from azureml.core.compute import ComputeTarget
import os

# choose a name for your cluster
compute_name = os.environ.get("AML_COMPUTE_CLUSTER_NAME", "cpucluster")
compute_min_nodes = os.environ.get("AML_COMPUTE_CLUSTER_MIN_NODES", 0)
compute_max_nodes = os.environ.get("AML_COMPUTE_CLUSTER_MAX_NODES", 4)

# This example uses CPU VM. For using GPU VM, set SKU to STANDARD_NC6
vm_size = os.environ.get("AML_COMPUTE_CLUSTER_SKU", "STANDARD_D2_V2")


if compute_name in ws.compute_targets:
    compute_target = ws.compute_targets[compute_name]
    if compute_target and type(compute_target) is AmlCompute:
        print('found compute target. just use it. ' + compute_name)
else:
    print('creating a new compute target...')
    provisioning_config = AmlCompute.provisioning_configuration(vm_size = vm_size,
                                                                min_nodes = compute_min_nodes, 
                                                                max_nodes = compute_max_nodes)

    # create the cluster
    compute_target = ComputeTarget.create(ws, compute_name, provisioning_config)
    
    # can poll for a minimum number of nodes and for a specific timeout. 
    # if no min node count is provided it will use the scale settings for the cluster
    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)
    
     # For a more detailed view of current AmlCompute status, use get_status()
    print(compute_target.get_status().serialize())

 

4. 클라우드에 데이터 업로드

ds = ws.get_default_datastore()
print(ds.datastore_type, ds.account_name, ds.container_name)

ds.upload(src_dir=data_folder, target_path='mnist', overwrite=True, show_progress=True)

 

5. 원격 클러스터에서 학습

6. 원격 실행 모니터링

7. 모델 등록

 

https://docs.microsoft.com/ko-kr/azure/machine-learning/service/tutorial-train-models-with-aml

이미지 분류 자습서: 모델 학습 - Azure Machine Learning service