systemdeets



In [1]:

    
import datetime

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import shijian

%matplotlib inline
shijian.setup_Jupyter()



In [2]:

    
df = pd.read_csv("gpudeets.csv")
df = shijian.add_time_variables(df = df)
df.tail()









    Out[2]:







  
    
      
      datetime
      temperature_C
      power_draw_W
      utilization_MiB
      memory_used_MiB
      memory_total_percentage
      month
      month_name
      weekday
      weekday_name
      time_through_day
      fraction_through_day
      hour
      hours_through_day
      days_through_week
      days_through_year
    
    
      datetime
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      2018-03-08 14:21:24.041169
      2018-03-08 14:21:24.041169
      91
      94.81
      2774
      8114
      100
      3
      March
      3
      Thursday
      14:21:24.041169
      0.598195
      14
      14.356678
      3.598195
      67
    
    
      2018-03-08 14:21:34.085132
      2018-03-08 14:21:34.085132
      91
      95.20
      2774
      8114
      100
      3
      March
      3
      Thursday
      14:21:34.085132
      0.598311
      14
      14.359468
      3.598311
      67
    
    
      2018-03-08 14:21:44.127469
      2018-03-08 14:21:44.127469
      91
      93.69
      2774
      8114
      100
      3
      March
      3
      Thursday
      14:21:44.127469
      0.598427
      14
      14.362258
      3.598427
      67
    
    
      2018-03-08 14:21:54.155904
      2018-03-08 14:21:54.155904
      91
      95.43
      2774
      8114
      100
      3
      March
      3
      Thursday
      14:21:54.155904
      0.598543
      14
      14.365043
      3.598543
      67
    
    
      2018-03-08 14:22:04.192842
      2018-03-08 14:22:04.192842
      91
      93.65
      2774
      8114
      100
      3
      March
      3
      Thursday
      14:22:04.192842
      0.598660
      14
      14.367831
      3.598660
      67

temperature



In [3]:

    
df.plot(y = ["temperature_C"]);

power draw



In [4]:

    
df.plot(y = ["power_draw_W"]);

histograms



In [5]:

    
plt.rcParams["figure.figsize"] = (17, 8)
df[["temperature_C", "power_draw_W"]].hist(bins = 50);



In [ ]:

	datetime	temperature_C	power_draw_W	utilization_MiB	memory_used_MiB	memory_total_percentage	month	month_name	weekday	weekday_name	time_through_day	fraction_through_day	hour	hours_through_day	days_through_week	days_through_year
datetime
2018-03-08 14:21:24.041169	2018-03-08 14:21:24.041169	91	94.81	2774	8114	100	3	March	3	Thursday	14:21:24.041169	0.598195	14	14.356678	3.598195	67
2018-03-08 14:21:34.085132	2018-03-08 14:21:34.085132	91	95.20	2774	8114	100	3	March	3	Thursday	14:21:34.085132	0.598311	14	14.359468	3.598311	67
2018-03-08 14:21:44.127469	2018-03-08 14:21:44.127469	91	93.69	2774	8114	100	3	March	3	Thursday	14:21:44.127469	0.598427	14	14.362258	3.598427	67
2018-03-08 14:21:54.155904	2018-03-08 14:21:54.155904	91	95.43	2774	8114	100	3	March	3	Thursday	14:21:54.155904	0.598543	14	14.365043	3.598543	67
2018-03-08 14:22:04.192842	2018-03-08 14:22:04.192842	91	93.65	2774	8114	100	3	March	3	Thursday	14:22:04.192842	0.598660	14	14.367831	3.598660	67