os.environ[“CUDA_VISIBLE_DEVICES”] =“1,2”, only GPU 1 is used. At least, such a line in Python has its own effect. It can control the use of GPUs. However, It is supposed to make GPU 1 and 2 available for the task, but the result is that only GPU 1 is available. Even when GPU 1 is out of memory, GPU 2 is not used.
CUDA Pro Tip: Control GPU Visibility with CUDA_VISIBLE …
CUDA Pro Tip: Control GPU Visibility with CUDA_VISIBLE …
cuda – How do I select which GPU to run a job on? – Stack …
You can set environment variables in the notebook using os.environ. Do the following before initializing TensorFlow to limit TensorFlow to first GPU. import os os.environ[CUDA_DEVICE_ORDER]=PCI_BUS_ID # see issue #152 os.environ[CUDA_VISIBLE_DEVICES]=0 You can double check that you have the correct devices.
For those who have only one GPU device, this line should be changed to os.environ[CUDA_VISIBLE _DEVICES] = 0 by default. Thanks for pointing this out : ) weizhepei closed this Sep 15, 2020, 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.
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. PDF – Download tensorflow for free. Previous Next.
12/8/2020 · I have a total of 4 GPUs. I want to use the gpu no. 2 for my experiments. on the top of the code I set os.environ[CUDA_VISIBLE _DEVICES]=’2′ but I see that I am still using GPU no. 0. Also torch.cuda.device_count() re…, OpenCL, OpenMP, PhysX, Microsoft Windows, GeForce 256
