#Checking chuster available or create new cluster
aml_name = 'cpu-cluster'
try:
aml_compute = AmlCompute(ws, aml_name)
print('Found existing AML compute context.')
except:
print('Creating new AML compute context.')
aml_config = AmlCompute.provisioning_configuration(vm_size=vm_size,
min_nodes=min_nodes,
max_nodes=max_nodes)
aml_compute = AmlCompute.create(ws,
name=aml_name,
provisioning_configuration=aml_config)
aml_compute.wait_for_completion(show_output=True)
#list of experiment with you have created in your workspace
from azureml.core.experiment import Experiment
list_experiments = Experiment.list(ws)
list_experiments
import azurerm
token = azurerm.get_access_token_from_cli()
azurerm.list_subscriptions(token)
#Delete full workspace
ws.delete(delete_dependent_resources=True, no_wait=False)
##Delete your computor cluster
aml_compute.delete()
X=x_df,
y=y_df,
compute_target=aml_name,
run_configuration=run_config,
iterations=iterations,
iteration_timeout_minutes=iteration_timeout_minutes,
primary_metric=primary_metric,
#n_cross_validations=n_cross_validations,
preprocess=True)
from azureml.core.experiment import Experiment
experiment = Experiment(ws, 'automl_remote')
remote_run = experiment.submit(automl_config, show_output=True)
best_model, fitted_model = remote_run.get_output()
delete = ws.delete(delete_dependent_resources=False, no_wait=False)
# create a TabularDataset from multiple paths in datastore
datastore_paths = [
(datastore,
'D:/Stock_Prediction/AutoML_Azure/stocks_data/stocks_data/2018Q4PredictionTestSet101.csv'
),
(datastore,
'D:/Stock_Prediction/AutoML_Azure/stocks_data/stocks_data/2018Q4PredictionTestSet10.csv'
),
(datastore,
'D:/Stock_Prediction/AutoML_Azure/stocks_data/stocks_data/2018Q4PredictionTrainedSet10.csv'
)
]
stock_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)
