Tensorflow Cuda_visible_devices 2020

To ensure that a GPU version TensorFlow process only runs on CPU: import os os.environ["CUDA_VISIBLE_DEVICES"]="-1" import tensorflow as tf For more information on the CUDA_VISIBLE_DEVICES, have a look to this answer or to the CUDA documentation. Tensorflow establece CUDA_VISIBLE_DEVICES dentro de jupyter Tengo dos GPU y me gustaría ejecutar dos redes diferentes a través de ipynb simultáneamente, sin embargo, el primer portátil siempre asigna ambas GPU.

Just tried on tf-1.4/cuda8/windows10 and it is working as expected. But yes, the logs from @e-ka show that the tensorflow process pid 8040 has the device 0,2,3 open where it should have only seen 1 device. Kind of odd since tensorflow is reporting only 1 device. Yeah,i do another experiment that is running the python script with CUDA_VISIBLE_DEVICES is 0 and 1,at same time also running the nvidia-smi commend.There are different results between nvidia-smi and tensorflow's device_lib.I set CUDA_VISIBLE_DEVICES is 1,and nvidia-smi also show the GPU1 is working,but tensorflow show the GPU0 is working,maybe there is a bug. By default, TensorFlow maps nearly all of the GPU memory of all GPUs subject to CUDA_VISIBLE_DEVICES visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmentation.

TensorFlow will attempt to use an equal fraction of the memory of all GPU devices that are visible to it. If you want to run different sessions on different GPUs, you should do the following. Run each session in a different Python process. Start each process with a different value for the CUDA_VISIBLE_DEVICES environment variable. 15/03/2016 · CUDA_VISIBLE_DEVICES being ignored. Reply. Follow. I am using CUDA_VISIBLE_DEVICES = 0 but yet the process ends up using GPU 2 instead of GPU 0. So now i have 2 processes on GPU 2 as shown below. Note this seems to work randomly sometimes. Any clues or hints would be appreciated. Tensorflow tries to preallocate memory on the GPUs. I think the default is 70% of the GPU memory. The easiest solution would be to have the two different processes sequentially. Or you can use the CUDA_VISIBLE_DEVICES environment variables to give exclusive access to each process. 有一台服务器,服务器上有多块儿gpu可以供使用,但此时只希望使用第2块和第4块gpu,但是我们希望代码能看到的仍然是有两块gpu,分别编号为0,1,这个时候我们可以使用环境变量cuda_visible_devices来解决这个问题。 比如:. CUDA_VISIBLE_DEVICES 环境变量说明. 先看下来自 Acceleware 的说明: If you are writing GPU enabled code, you would typically use a device query to select the desired GPUs. However, a quick and easy solution for testing is to use the environment variable CUDA_VISIBLE_DEVICES to restrict the devices that your CUDA application sees.

But the CUDA_VISIBLE_DEVICES environment variable is handy for restricting execution to a specific device or set of devices for debugging and testing. You can also use it to control execution of applications for which you don’t have source code, or to launch multiple instances of a program on a single machine, each with its own environment and set of visible devices.

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