If tensorflow is using GPU, you’ll notice a sudden jump in memory usage, temperature etc. Thanks for contributing an answer to Stack Overflow! Please be sure to answer the question.
One more query we ran across in our research was “How to check tensorflow gpu is working?”.
Here is what I researched. to check whether Tensor. Flow has access to the GPU support, open Python console (through Anaconda Powershell Prompt for my case), and then run the following code one line at a time: print (tf. test. is_built_with_cuda ()): Returns whether Tensor. Flow was built with CUDA (GPU) support. True if CUDA is installed properly.
, and with tf. Device (‘/gpu:0’):a = tf. Constant (1)b = tf. Constant (2)c = tf. Add (a, b)with tf. Session () as sess: print (sess. run (c)).
The next thing we wanted the answer to was, how to configure TensorFlow to use a specific GPU?
Another answer was, and with tf. Session (config=tf. Config. Proto (allow_soft_placement=True, log_device_placement=True)): # Run your graph here 1) Setup your computer to use the GPU for Tensor. Flow (or find a computer to lend if you don’t have a recent GPU). 2) Try running the previous exercise solutions on the GPU.
How to get current available GPUs in TensorFlow?
, and from tensorflow., and python. Client import device_lib def get_available_gpus(): local_device_protos = device_lib. List_local_devices() return [x. Name for x in local_device_protos if x. Device_type == ‘GPU’] Note that (at least up to Tensor. Flow 1.4), calling device_lib. List_local_devices() will run some initialization code that, by default, will allocate all of the GPU memory on all of the devices ( Git. Hub issue ).
How to use TensorFlow with GPU?
Instead you should use the following function: In your case both the cpu and gpu are available, if you use the cpu version of tensorflow the gpu will not be listed. In your case, without setting your tensorflow device ( with tf. device (“..”) ), tensorflow will automatically pick your gpu !
Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards., tensor Flow GPU support requires an assortment of drivers and libraries. To simplify installation and avoid library conflicts, we recommend using a Tensor. Flow Docker image with GPU support (Linux only).
Is TensorFlow-GPU compatible with CUDA 10?
Strangely, even though the tensorflow website 1 mentions that CUDA 10.1 is compatible with tensorflow-gpu-1.13.1, it doesn’t work so far. Tensorflow-gpu gets installed properly though but it throws out weird errors when running. So far, the best configuration to run tensorflow with GPU is CUDA 9.0 with tensorflow_gpu-1.12.0 under python3.6.
How to check if GPU is working or not?
If you want device device_name you can type : tf., and test., and gpu_device_name(). You can check if you are currently using the GPU by running the following code : If the output is ”, it means you are using CPU only; If the output is something like that /device: GPU:0, it means GPU works .
How to find out which device is used in TensorFlow?
For tensorflow1, to find out which device is used, you can enable log device placement like this: Check your console for this type of output. Show activity on this post.