Run python code using GPU - python-3.x

Question

Jena Climate Code is as follows
import numpy as np
import os
from matplotlib import pyplot as plt
from numba import vectorize
f=open('jena.csv')
data=f.read()
f.close()
lines=data.split('\n')
header=lines[0].split(',')
lines=lines[1:]
print(header)
N=len(lines)
print(N)
float_data=np.zeros((len(lines),len(header)-1))
for i, line in enumerate(lines):
values=[float(x) for x in line.split(',')[1:]]
float_data[i,:]=values
mean=float_data[:200000].mean(axis=0)
float_data -=mean
std=float_data[:200000].std(axis=0)
float_data/=std
def generator(data,lookback,delay,min_index,max_index,shuffle=False,batch_size=128,step=6):
if max_index is None:
max_index=len(data)-delay-1
i=min_index+lookback
while 1:
if shuffle:
rows=np.random.randint(
min_index+lookback,max_index,size=batch_size)
else:
if i + batch_size>=max_index:
i=min_index+lookback
rows=np.arange(i,min(i+batch_size,max_index))
i+=len(rows)
samples=np.zeros((len(rows),lookback//step,data.shape[-1]))
targets=np.zeros((len(rows),))
for j, row in enumerate(rows):
indices=range(rows[j]-lookback,rows[j],step)
samples[j]=data[indices]
targets[j]=data[rows[j]+delay][1]
yield samples, targets
lookback=1440
step=6
delay=144
batch_size=128
train_gen=generator(float_data,lookback=lookback,delay=delay,min_index=0,max_index=200000,shuffle=True,step=step,batch_size=batch_size)
val_gen=generator(float_data,lookback=lookback,delay=delay,min_index=200001,max_index=300000,step=step,batch_size=batch_size)
test_gen=generator(float_data,lookback=lookback,delay=delay,min_index=300001,max_index=None,step=step,batch_size=batch_size)
val_steps=(300000-200001-lookback)
test_steps=(len(float_data)-300001-lookback)
def evaluate_naive_method():
batch_maes=[]
for step in range(val_steps):
samples,targets=next(val_gen)
mae=np.mean(np.abs(preds-targets))
batch_maes.append(mae)
print(np.mean(batch_maes))
evaluate_naive_method()
When i execute the code, it uses CPU and takes approximately 14 minutes to produce mae.
I want to use tensorflow in this section using GPU so that output can be faster.
for step in range(val_steps):
samples,targets=next(val_gen)
mae=np.mean(np.abs(preds-targets))
batch_maes.append(mae)
Should i convert the variables "samples" and "targets" into tensorflow so that I can get output faster? If so how can i convert it to tensorflow?

Answer 1

Tensorflow does this thing what you want, please have a look at below example using GPU:
https://www.tensorflow.org/guide/using_gpuhttps://www.tensorflow.org/guide/using_gpu