def setUp(self):
self.workspace = Workspace(
settings.workspace.id,
settings.workspace.token,
settings.workspace.endpoint
)
authorization_token = ws.authorization_token
print(workspace_id)
print(authorization_token)
# COMMAND ----------
from azureml import services
# COMMAND ----------
# set up web service
workspace_id = '9fe8f2f9e9f94677abc6d076340fcbf7'
authorization_token = 'ghRbG40tVVy8IUGRYaPAo7cam6Nkv6Bn9qha4guFm0rhaiPHFGOaBD3xajad8o+h+baNRCFXaXT5qz9M1VpbGg=='
ws = Workspace(
workspace_id='fa817a79ba3b4cac86eb053ed001a566',
authorization_token=
'/KQWsvqg0dtfp3vOjiazPEWABxFni41TXHjYcvRMhqRquP2hqO36uZ4Q/tqHaq+G7nMDJB668hmOvMle4lm/Ew==',
endpoint='https://studioapi.azureml-int.net')
from azureml import services
@services.publish(workspace_id, authorization_token)
@services.types(a=float, b=float, c=float, d=float)
@services.returns(float)
def demoservice(a, b, c, d):
# predict the label
feature_vector = [a, b, c, d]
return clf.predict(feature_vector)
# COMMAND ----------
#import workspace from Azure, set up the data frame
from azureml import Workspace
ws = Workspace()
experiment = ws.experiments['aad1200b3a5a422d8e75a41f9fcc1299.f-id.a8cddcb61b594851ad21466102f6e92f']
ds = experiment.get_intermediate_dataset(
node_id='21e15cc1-15b7-4bc4-8f93-923ea9fe55e3-207',
port_name='Results dataset',
data_type_id='GenericCSV'
)
frame = ds.to_dataframe()
#visualize the data to plot every point against every point
%matplotlib inline
import seaborn as sns
num_cols = ["mood (num)","sleep","sleep (2)","miles","total cal"]
sns.pairplot(frame[num_cols], size=2)
"""
AzureML Python client library wrapper
Reference: https://github.com/Azure/Azure-MachineLearning-ClientLibrary-Python
"""
import os
import pandas as pd
from azureml import Workspace, AzureMLConflictHttpError
from azureml.serialization import DataTypeIds
ws = Workspace(workspace_id='a960dea614c04cf4a758c6321b857eb8',
authorization_token='f527e8b37a58455494c08be5831119aa',
endpoint='https://europewest.studio.azureml.net/')
def symbol_to_path(symbol, base_dir=""):
"""Return CSV file path given ticker symbol"""
return os.path.join(base_dir, "{}.csv".format(str(symbol)))
def read_ds(symbol):
print 'Reading ' + symbol + ' from server'
ds = ws.datasets[symbol_to_path(symbol)]
df_temp = ds.to_dataframe()
df_temp = df_temp.loc[:, ['Date', 'Adj Close']].rename(
columns={'Adj Close': symbol})
df_temp.set_index('Date', inplace=True)
df_temp.fillna('nan')
return df_temp
#!pip install azureml-sdk
#Azure Machine Learning Extension for VS Code
from azureml import Workspace, Datastore
ws = Workspace.create(name='aml-workspace',
subscription_id='123456-abc-123...',
resource_group='aml-resources',
create_resource_group=True,
location='eastus',
sku='enterprise'
)
ws = Workspace()
ws = Workspace.from_config()
datastore = ws.get_default_datastore()
blobstore = Datastore(ws,'name')
filestore = Datastore(ws,'name')
blobstore.get_default(ws).upload_files(["array of files"])
ds = ws.datasets['*.csv']
df = ds.to_dataframe()
for compute_name in ws.compute_targets:
compute = ws.compute_targets[compute_name]
print(compute.name, ":", compute.type)
#ws.name