How to force tensorflow and Keras run on GPU?

Admin 1
Viewed 2015

I have TensorFlow, NVIDIA GPU (CUDA)/CPU, Keras, & Python 3.7 in Linux Ubuntu. I followed all the steps according to this tutorial: https://www.youtube.com/watch?v=dj-Jntz-74g

when I run the following code of:

# What version of Python do you have?
import sys

import tensorflow.keras
import pandas as pd
import sklearn as sk
import tensorflow as tf

print(f"Tensor Flow Version: {tf.__version__}")
print(f"Keras Version: {tensorflow.keras.__version__}")
print()
print(f"Python {sys.version}")
print(f"Pandas {pd.__version__}")
print(f"Scikit-Learn {sk.__version__}")
gpu = len(tf.config.list_physical_devices('GPU'))>0
print("GPU is", "available" if gpu else "NOT AVAILABLE")

I get the these results:

Tensor Flow Version: 2.4.1
Keras Version: 2.4.0

Python 3.7.10 (default, Feb 26 2021, 18:47:35) 
[GCC 7.3.0]
Pandas 1.2.3
Scikit-Learn 0.24.1
GPU is available

However; I don't know how to run my Keras model on GPU. When I run my model, and I get $ nvidia-smi -l 1, GPU usage is almost %0 during the run.

from keras import layers
from keras.models import Sequential
from keras.layers import Dense, Conv1D, Flatten
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
from keras.callbacks import EarlyStopping
 
model = Sequential()
model.add(Conv1D(100, 3, activation="relu", input_shape=(32, 1)))
model.add(Flatten())
model.add(Dense(64, activation="relu"))
model.add(Dense(1, activation="linear"))
model.compile(loss="mse", optimizer="adam", metrics=['mean_squared_error'])
model.summary()

es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=70)

history = model.fit(partial_xtrain_CNN, partial_ytrain_CNN, batch_size=100, epochs=1000,\
                    verbose=0, validation_data=(xval_CNN, yval_CNN), callbacks = [es])

Do I need to change any parts of my code or add a part to force it run on GPU??

keras tensorflow deep-learning gpu python
2 Answers

To tensorflow work on GPU, there are a few steps to be done and they are rather difficult.

First of compatibility of these frameworks with NVIDIA is much better than others so you could have less problem if the GPU is an NVIDIA and should be in this list.

The second thing is that you need to install all of the requirements which are:

1- The last version of your GPU driver 2- CUDA instalation shown here 3- then install Anaconda add anaconda to environment while installing.

After completion of all the installations run the following commands in the command prompt. conda install numba & conda install cudatoolkit

Now to assess the results use this code:

from numba import jit, cuda 
import numpy as np 
# to measure exec time 
from timeit import default_timer as timer    
  
# normal function to run on cpu 
def func(a):                                 
    for i in range(10000000): 
        a[i]+= 1      
  
# function optimized to run on gpu  
@jit(target ="cuda")                          
def func2(a): 
    for i in range(10000000): 
        a[i]+= 1
if __name__=="__main__": 
    n = 10000000                            
    a = np.ones(n, dtype = np.float64) 
    b = np.ones(n, dtype = np.float32) 
      
    start = timer() 
    func(a) 
    print("without GPU:", timer()-start)     
      
    start = timer() 
    func2(a) 
    print("with GPU:", timer()-start)

Parts of this answer is from here which you can read for more.

user_108538760

I found a solution for my question. I think the problem was about the incompatibility of the NVIDIA driver, Cudnn, and TensorFlow. because I had the new NVIDIA graphic card (RTX 3060) on my laptop, and it has NVIDIA Ampere Architecture GPU, and probably it was not compatible with others.

Instead I referred to these links to download the 21.02 docker container, then I mount this docker. In this container that is provided by NVIDIA everything is tested and should give good performance.

https://docs.nvidia.com/deeplearning/frameworks/tensorflow-wheel-release-notes/tf-wheel-rel.html

https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes/rel_21-02.html#rel_21-02

Also, to install a docker in Linux you can follow the procedure explained here:

https://towardsdatascience.com/deep-learning-with-docker-container-from-ngc-nvidia-gpu-cloud-58d6d302e4b2

Admin1
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