GPU Performance Test: H2O - XGBoost on GPU

In [1]:

    

import h2o
from h2o.estimators.xgboost import H2OXGBoostEstimator


    

In [2]:

    

%%capture
h2o.connect(ip="35.227.47.29")
h2o.no_progress()


    

In [3]:

    

# Import some data from Amazon S3
h2oDF = h2o.import_file(path="https://s3-us-west-1.amazonaws.com/dsclouddata/LendingClubData/LoansGoodBad.csv")


# Stratified Split into Train/Test
stratsplit = h2oDF["Bad_Loan"].stratified_split(test_frac=0.3, seed=12349453)
train = h2oDF[stratsplit=="train"]
test = h2oDF[stratsplit=="test"]


    

In [4]:

    

# Identify predictors and response
x = train.columns
y = "Bad_Loan"
x.remove(y)

# For binary classification, response should be a factor
train[y] = train[y].asfactor()
test[y] = test[y].asfactor()
train.head(5)


    

    







  RowID
  Loan_Amount
Term     
  Interest_Rate
  Employment_Years
Home_Ownership  
  Annual_Income
Verification_Status     
Loan_Purpose  
State  
  Debt_to_Income
  Delinquent_2yr
  Revolving_Cr_Util
  Total_Accounts
Bad_Loan  
  Longest_Credit_Length


      2
         2500
60 months
          15.27
               0.5
RENT            
          30000
VERIFIED - income source
car           
GA     
            1   
               0
                9.4
               4
BAD       
                     12
      3
         2400
36 months
          15.96
              10  
RENT            
          12252
not verified            
small_business
IL     
            8.72
               0
               98.5
              10
GOOD      
                     10
      4
        10000
36 months
          13.49
              10  
RENT            
          49200
VERIFIED - income source
other         
CA     
           20   
               0
               21  
              37
GOOD      
                     15
      5
         5000
36 months
           7.9 
               3  
RENT            
          36000
VERIFIED - income source
wedding       
AZ     
           11.2 
               0
               28.3
              12
GOOD      
                      7
      6
         3000
36 months
          18.64
               9  
RENT            
          48000
VERIFIED - income source
car           
CA     
            5.35
               0
               87.5
               4
GOOD      
                      4








    
Out[4]:

In [12]:

    

%%time
XGB_GPU = H2OXGBoostEstimator(model_id="XGB_on_GPU", ntrees=200, max_depth=9, learn_rate=0.05, backend="gpu", gpu_id=0)
XGB_GPU.train(x=x, y=y, training_frame=train, validation_frame=test)
print "Accuracy AUC: " + str(XGB_GPU.auc())


    

    




Accuracy AUC: 0.874694485388
CPU times: user 67.8 ms, sys: 8.89 ms, total: 76.7 ms
Wall time: 11.5 s

In [13]:

    

%%time
XGB_CPU = H2OXGBoostEstimator(model_id="XGB_on_CPU", ntrees=200, max_depth=9, learn_rate=0.05, backend="cpu")
XGB_CPU.train(x=x, y=y, training_frame=train, validation_frame=test)
print "Accuracy AUC: " + str(XGB_CPU.auc())


    

    




Accuracy AUC: 0.859213997196
CPU times: user 226 ms, sys: 21.3 ms, total: 247 ms
Wall time: 1min 24s

In [16]:

    

import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import matplotlib.pyplot as plt
 
objects = ('GPU', 'CPU')
y_pos = np.arange(len(objects))
seconds = [11,84]
 
plt.barh(y_pos, seconds, align='center', alpha=0.5)
plt.yticks(y_pos, objects)
plt.xlabel('Seconds')
plt.title('XGBoost Training Time in Seconds')
 
plt.show()


    

In [15]:

    

GPU_Cost = (0.9 / 3600) * 11
CPU_Cost = (0.7 / 3600) * 84
objects = ('GPU Cost', 'CPU Cost')
y_pos = np.arange(len(objects))
seconds = [GPU_Cost,CPU_Cost]
 
plt.barh(y_pos, seconds, align='center', alpha=0.5)
plt.yticks(y_pos, objects)
plt.xlabel('Cents')
plt.title('Cost in Cents')
 
plt.show()