Can I run Keras model on gpu

Question

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

User · Answer

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

User · Answer

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

User · Answer

Yes you can run keras models on GPU  Few things you will have to check first   your system has GPU  Nvidia  As AMD doesn t work yet  You have installed the GPU version of tensorflow You have installed CUDA installation instructions Verify that tensorflow is running with GPU check if GPU is working  sess   tf Session config tf ConfigProto log device placement True   for TF  gt  v2 0 sess   tf compat v1 Session config tf compat v1 ConfigProto log device placement True    Thanks  nbro and  Ferro for pointing this out in the comments  OR from tensorflow python client import device lib print device lib list local devices     output will be something like this      name   quot  cpu 0 quot device type   quot CPU quot     name   quot  gpu 0 quot device type   quot GPU quot     Once all this is done your model will run on GPU  To Check if keras  gt  2 1 1  is using GPU  from keras import backend as K K tensorflow backend  get available gpus    All the best

User · Answer

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

User · Answer

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

User · Answer

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

[python] Can I run Keras model on gpu?

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