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# check / set working directory
import os
dirpath = os.getcwd()
print("current directory is : " + dirpath)
foldername = os.path.basename(dirpath)
print("Directory name is : " + foldername)
 
# to set working directory
os.chdir('c:\\Users\\uname\\desktop\\python')

# -----

# import packages
import azureml.core
from azureml.core import Workspace

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

# --------
# connect to workspace
# load workspace configuration from the config.json file in the current folder.

# if you are using a notebook VM in the AML WS
ws = Workspace.from_config()

# from local
ws = get_or_create_workspace(
    subscription_id="<SUBSCRIPTION_ID>",
    resource_group="<RESOURCE_GROUP>",
    workspace_name="<WORKSPACE_NAME>",
    workspace_region="<WORKSPACE_REGION>"
)

# then print info - will need to authenticate
print(ws.name, ws.location, ws.resource_group, ws.location, sep='\t')

# ----------
# create experiment
experiment_name = 'sklearn-mnist'

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

# ----------
# Create or Attach existing compute resource
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", "cpu-cluster")
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())