def test_aws_true(self):
"""
Tests a situation when benchmark_runner is run on AWS.
Uses mock to simulate the situation where aws = True
"""
mock_instancetype_region = \
{
'instancetype': 'someinstancetype', 'region': 'someregion'
}
mocked_aws_metadata = \
mock.patch('awsforyou.aws_metadata.get_instance',
return_value=mock_instancetype_region)
mocked_aws_metadata.start()
runtime = bench.run_benchmark(aws=True)
scorecard = pd.read_csv('./aws-scorecard.csv')
dict_scorecard = scorecard.to_dict(orient='records')[0]
dict_scorecard = \
{
'instancetype': dict_scorecard['instancetype'],
'region': dict_scorecard['region']
}
mocked_aws_metadata.stop()
self.assertEqual(mock_instancetype_region, dict_scorecard)
def test_for_complete_df_empty(self):
"""
test to see if added times dataframe is empty
"""
benchmark_df = rc.get_benchmark_data()
times, percents = [2, 4, 6], [1, 5, 10]
est_time_user = tt.find_total_time(times, percents)
user_benchmark = br.run_benchmark()
est_time_aws = benchmark_df[['runtime']] \
/ user_benchmark * est_time_user[0]
benchmark_df["estimated_time_aws"] = est_time_aws
self.assertGreater(benchmark_df.shape[0], 0)
def add_estimated_time_aws(dataframe, python_call, module_name):
"""
This function estimates the time required to run the users algorithim on
each instance and adds it to the dataframe
:param python_call: str python string calling the algorithm to be timed
:param module_name: str name of module from which function is called
:param dataframe: the benchmark dataframe output from get_benchmark_data()
:return: dataframe with added estimated times
"""
times, percents = ar.run_algo(python_call, module_name)
est_time_user = tt.find_total_time(times, percents)
user_benchmark = br.run_benchmark()
est_time_aws = dataframef[['runtime']]/user_benchmark * est_time_user[0]
dataframe["estimated_time_aws"] = est_time_aws
return dataframe
def test_add_estimated_price(self):
"""
This function tests adding the spot and on-demand pricing
to the dataframe
"""
benchmark_df = rc.get_benchmark_data()
times, percents = [2, 4, 6], [1, 5, 10]
est_time_user = tt.find_total_time(times, percents)
user_benchmark = br.run_benchmark()
est_time_aws = benchmark_df[['runtime']] \
/ user_benchmark * est_time_user[0]
benchmark_df["estimated_time_aws"] = est_time_aws
instance_types = benchmark_df["instance_type"].tolist()
price = ap.get_instance_pricing(instance_types)
complete_df = pd.merge(benchmark_df, price, on="instance_type")
complete_df["est_cost_spot_price"] = \
complete_df["estimated_time_aws"] \
* complete_df["spot_price"] / 3600
complete_df["est_cost_on_demand_price"] = \
complete_df["estimated_time_aws"] \
* complete_df["on_demand_price"] / 3600
self.assertGreater(complete_df.shape[0], 0)
def test_runtime(self):
"""
Check if output is indeed number of seconds
"""
runtime = bench.run_benchmark(aws=False)
self.assertIsInstance(runtime, float)
