hadoop@unsw-ThinkPad-T490:~/NVIDIA_CUDA-.1_Samples/bin/x86_64/linux/release$ nvidia-smi

Thu Nov  ::

+-----------------------------------------------------------------------------+

| NVIDIA-SMI 440.31       Driver Version: 440.31       CUDA Version: 10.2     |

|-------------------------------+----------------------+----------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|===============================+======================+======================|

|     GeForce MX250       Off  | :3C:00.0 Off |                  N/A |

| N/A   58C    P0    N/A /  N/A |    390MiB /  2002MiB |      %      Default |

+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+

| Processes:                                                       GPU Memory |

|  GPU       PID   Type   Process name                             Usage      |

|=============================================================================|

|                G   /usr/lib/xorg/Xorg                           190MiB |

|                G   /usr/bin/gnome-shell                         136MiB |

|                G   ...uest-channel-token=    59MiB |

+-----------------------------------------------------------------------------+

import os

import time

from sklearn.linear_model import MultiTaskLasso, Lasso

from sklearn.datasets import load_svmlight_file

from sklearn.metrics import r2_score

from sklearn.metrics import mean_squared_error

import h2o4gpu

import h2o4gpu.util.import_data as io

import h2o4gpu.util.metrics as metrics

import pandas as pd

import numpy as np

#from joblib import Memory

#mem = Memory("./mycache")

# This maybe a tricky way to load files.

##@mem.cache

def get_data():

    data = load_svmlight_file("/home/hadoop/YearPredictionMSD")

    return data[0], data[1]

print("Loading data.")

train_x, train_y = load_svmlight_file("/home/hadoop/YearPredictionMSD")

train_x = train_x.todense()

test_x, test_y = load_svmlight_file("/home/hadoop/YearPredictionMSD.t")

test_x = test_x.todense()

for max_iter in [100, 500, 1000, 2000, 4000, 8000]:

    print("="*80)

    print("Setting up solver, msx_iter is {}".format(max_iter))

    model = h2o4gpu.Lasso(alpha=0.01, fit_intercept=False, max_iter=max_iter)

    #model = Lasso(alpha=0.1, fit_intercept=False, max_iter=500)

    time_start=time.time()

    model.fit(train_x, train_y)

    time_end=time.time()

    print('train totally cost {} sec'.format(time_end-time_start))

    time_start=time.time()

    y_pred_lasso = model.predict(test_x)

    y_pred_lasso = np.squeeze(y_pred_lasso)

    time_end=time.time()

    print('test totally cost {} sec'.format(time_end-time_start))

    print(y_pred_lasso.shape )

    print(test_y.shape )

    print(y_pred_lasso[:10])

    print(test_y[:10])

    mse = mean_squared_error(test_y, y_pred_lasso)

    print("mse on test data : %f" % mse)

    r2_score_lasso = r2_score(test_y, y_pred_lasso)

    print("r^2 on test data : %f" % r2_score_lasso)