How to run Tensorflow on CPU

Question

I have installed the GPU version of tensorflow on an Ubuntu 14 04   I am on a GPU server where tensorflow can access the available GPUs   I want to run tensorflow on the CPUs   Normally I can use env CUDA VISIBLE DEVICES 0 to run on GPU no  0   How can I pick between the CPUs instead   I am not intersted in rewritting my code with with tf device   cpu 0

User · Answer

In some systems one have to specify   import os os environ  CUDA DEVICE ORDER    PCI BUS ID  os environ  CUDA VISIBLE DEVICES         or even  -1    BEFORE importing tensorflow

User · Answer

In my case  for tensorflow 2 4 0  none of the previous answer works unless you install tensorflow-cpu pip install tensorflow-cpu

User · Answer

Another possible solution on installation level would be to look for the CPU only variant  https   www tensorflow org install pip package-location In my case  this gives right now  pip3 install https   storage googleapis com tensorflow windows cpu tensorflow cpu-2 2 0-cp38-cp38-win amd64 whl  Just select the correct version  Bonus points for using a venv like explained eg in this answer

User · Answer

The environment variable solution doesn t work for me running tensorflow 2 3 1  I assume by the comments in the github thread that the below solution works for versions  gt  2 1 0  From tensorflow github  import tensorflow as tf    Hide GPU from visible devices tf config set visible devices      GPU    Make sure to do this right after the import with fresh tensorflow instance  if you re running jupyter notebook  restart the kernel   And to check that you re indeed running on the CPU    To find out which devices your operations and tensors are assigned to tf debugging set log device placement True     Create some tensors and perform an operation 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   Expected output  2 3 1 Executing op MatMul in device  job localhost replica 0 task 0 device CPU 0 tf Tensor    22  28     49  64     shape  2  2   dtype float32

User · Answer

You could use tf config set visible devices  One possible function that allows you to set if and which GPUs to use is   import tensorflow as tf  def set gpu gpu ids list       gpus   tf config list physical devices  GPU       if gpus          try              gpus used    gpus i  for i in gpu ids list              tf config set visible devices gpus used   GPU               logical gpus   tf config experimental list logical devices  GPU               print len gpus    Physical GPUs    len logical gpus    Logical GPU           except RuntimeError as e                Visible devices must be set before GPUs have been initialized             print e    Suppose you are on a system with 4 GPUs and you want to use only two GPUs  the one with id   0 and the one with id   2  then the first command of your code  immediately after importing the libraries  would be   set gpu  0  2     In your case  to use only the CPU  you can invoke the function with an empty list   set gpu       For completeness  if you want to avoid that the runtime initialization will allocate all memory on the device  you can use tf config experimental set memory growth  Finally  the function to manage which devices to use  occupying the GPUs memory dynamically  becomes   import tensorflow as tf  def set gpu gpu ids list       gpus   tf config list physical devices  GPU       if gpus          try              gpus used    gpus i  for i in gpu ids list              tf config set visible devices gpus used   GPU               for gpu in gpus used                  tf config experimental set memory growth gpu  True              logical gpus   tf config experimental list logical devices  GPU               print len gpus    Physical GPUs    len logical gpus    Logical GPU           except RuntimeError as e                Visible devices must be set before GPUs have been initialized             print e

User · Answer

Just using the code below   import os os environ  CUDA DEVICE ORDER      PCI BUS ID  os environ  CUDA VISIBLE DEVICES      -1

User · Answer

As recommended by the Tensorflow GPU guide    Place tensors on the CPU with tf device   CPU 0      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        Any additional tf code placed in this block will be executed on the CPU

User · Answer

For me  only setting CUDA VISIBLE DEVICES to precisely -1 works   Works   import os import tensorflow as tf  os environ  CUDA VISIBLE DEVICES      -1   if tf test gpu device name        print  GPU found   else      print  No GPU found      No GPU found   Does not work   import os import tensorflow as tf  os environ  CUDA VISIBLE DEVICES             if tf test gpu device name        print  GPU found   else      print  No GPU found      GPU found

User · Answer

You can apply device count parameter per tf Session   config   tf ConfigProto          device count     GPU   0        sess   tf Session config config    See also protobuf config file    tensorflow core framework config proto

User · Answer

You can also set the environment variable to   CUDA VISIBLE DEVICES      without having to modify the source code

User · Answer

If the above answers don t work  try   os environ  CUDA VISIBLE DEVICES      -1

[python] How to run Tensorflow on CPU

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