[python] Can I run Keras model on gpu?

I'm running a Keras model, with a submission deadline of 36 hours, if I train my model on the cpu it will take approx 50 hours, is there a way to run Keras on gpu?

I'm using Tensorflow backend and running it on my Jupyter notebook, without anaconda installed.

I'm using Anaconda on Windows 10, with a GTX 1660 Super. I first installed the CUDA environment following this step-by-step. However there is now a keras-gpu metapackage available on Anaconda which apparently doesn't require installing CUDA and cuDNN libraries beforehand (mine were already installed anyway).

This is what worked for me to create a dedicated environment named keras_gpu:

# need to downgrade from tensorflow 2.1 for my particular setup
conda create --name keras_gpu keras-gpu=2.3.1 tensorflow-gpu=2.0

To add on @johncasey 's answer but for TensorFlow 2.0, adding this block works for me:

import tensorflow as tf
from tensorflow.python.keras import backend as K

# adjust values to your needs
config = tf.compat.v1.ConfigProto( device_count = {'GPU': 1 , 'CPU': 8} )
sess = tf.compat.v1.Session(config=config) 
K.set_session(sess)

This post solved the set_session error I got: you need to use the keras backend from the tensorflow path instead of keras itself.

Of course. if you are running on Tensorflow or CNTk backends, your code will run on your GPU devices defaultly.But if Theano backends, you can use following

Theano flags:

"THEANO_FLAGS=device=gpu,floatX=float32 python my_keras_script.py"

2.0 Compatible Answer: While above mentioned answer explain in detail on how to use GPU on Keras Model, I want to explain how it can be done for Tensorflow Version 2.0.

To know how many GPUs are available, we can use the below code:

print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))

To find out which devices your operations and tensors are assigned to, put tf.debugging.set_log_device_placement(True) as the first statement of your program.

Enabling device placement logging causes any Tensor allocations or operations to be printed. For example, running the below code:

tf.debugging.set_log_device_placement(True)

# Create some tensors
a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
c = tf.matmul(a, b)

print(c)

gives the Output shown below:

Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0 tf.Tensor( [[22. 28.] [49. 64.]], shape=(2, 2), dtype=float32)

For more information, refer this link

See if your script is running GPU in Task manager. If not, suspect your CUDA version is right one for the tensorflow version you are using, as the other answers suggested already.

Additionally, a proper CUDA DNN library for the CUDA version is required to run GPU with tensorflow. Download/extract it from here and put the DLL (e.g., cudnn64_7.dll) into CUDA bin folder (e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin).

Sure. I suppose that you have already installed TensorFlow for GPU.

You need to add the following block after importing keras. I am working on a machine which have 56 core cpu, and a gpu.

import keras
import tensorflow as tf


config = tf.ConfigProto( device_count = {'GPU': 1 , 'CPU': 56} ) 
sess = tf.Session(config=config) 
keras.backend.set_session(sess)

Of course, this usage enforces my machines maximum limits. You can decrease cpu and gpu consumption values.