Hi I have two GPU cards: '0' for NVIDIA getforce and '1' for AMD radeon.
I am running a deep learning model using pytorch and installed pytorch with cuda 11.7 for NVIDIA card and installed pytorch with Rocm5.2 for AMD card.
However, all the calculations are still happening on the NVIDIA card.
Could you help to split my calculations on both cards or when NVIDIA is about to be full, switch to AMD card?
What I tried to do is to make both my cards visible using
os.environ["CUDA_VISIBLE_DEVICES"]="0,1"
Related
I am looking to upgrade an older machine we have at our lab to use for deep learning (PyTorch) in addition to my personal work station. Its an older Dell work station but the relevant specs are as follows:
PSU: 950W
RAM: 64 GB DDR4 ECC
CPU: Xeon Bronze 3104 #1.7 GHz
It even has an older NVIDIA GPU I can use for display output when the A4000 is fully loaded like I currently do on my personal setup.
Through the university we can acquire a RTX A4000 (I know not best price to performance), which is basically a 3070ti with more VRAM. I am concerned that the low clock speeds may cause a bottle neck. Does anyone have experience with a similar configuration?
Thank you for the help!
I have installed tensorflow-gpu to train my models on GPU and have confirmed the installation from below.
import tensorflow as tf
tf.config.list_physical_devices()
#[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
# PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
I started training an image classification model and I hope it runs on GPU automatically until and unless specified to run manually on a device. But while training the model I could see in my task manager that there were 2 GPU's and Intel Graphics card was GPU 0 and NVIDIA GeForce GTX1660Ti was GPU1. Does that mean tensorflow didn't detect my NVIDIA card or is it the actual GPU that was detected?
While training the model I could see that my NVIDIA GPU utilization was very low. Not sure on which device my model was trained.
Can someone clarify please.
Further version details. tf.__version__ (2.6.0), python 3.7, CUDA 11.4, cudnn 8.2
Try to enable debug:
tf.debugging.set_log_device_placement(True)
I think your Intel GPU is ignored by tf.config.list_physical_devices().
I am learning ML and trying to run the model(Pytorch) on my Nvidia GTX 1650.
torch.cuda.is_available() => True
model.to(device)
Implemented the above lines to run the model on GPU, but the task manager shows two GPU
1. Intel Graphics
2. Nvidia GTX 1650
The fluctuation in CPU usage is shown on Intel and not on Nvidia.
How I can run it on Nvidia GPU?
NOTE: The code is working fine and is getting executed on the Intel one with around 90-100s time of epoch.
Just do
device = torch.device("cuda:0")
Try this, hope it works
device = 'cuda'
check = torch.cuda.current_device()
print(torch.cuda.get_device_name(check))
#model.to(device)
I am very beginner in CUDA. I just want to know how to read an image using CUDA programming? I am having NVidia Geforce 860 M, Installed Visual Studio 2012, CUDA 7.5.
Thanks in advance!!!
I think you can only do it via a CPU and then transfer the image to the CUDA board's memory. CUDA boards do not provide access to the file system.
You will need to read the file using C++ functions and then pass the pointer of the image pixels Array to CUDAMEMCPY function.This will transfer the image from the CPU memory to the GPU Global memory.
I have an nvidia GTX 750 Ti card, which is advertised as having 640 CUDA cores. Indeed, the nvidia settings application also reports this.
I'm trying to use this card to do OpenCL development on Linux. Now, I have reported from the OpenCL environment (through PyOpenCL if it makes a difference) that the number of compute units is 5. My understanding is that one compute unit on an nvidia device maps to one multiprocessor, which I understand to be 32 SIMD units (which I assume is the a CUDA core).
Clearly, 5 * 32 is not 640 (rather a quarter of what is expected).
Am I missing something as regards the meaning of a a work unit on nvidia? The card is also driving the graphics output which will be using some of the computational capability - is a proportion of the processing capability reserved for graphics use? (if so, can I change this?).
NVIDIA have a whitepaper for the NVIDIA GeForce GTX 750 Ti, which is worth a read.
An OpenCL compute unit translates to a streaming multiprocessor in NVIDIA GPU terms. Each Maxwell SMM in your GPU contains 128 processing elements ("CUDA cores") - and 128*5 = 640. The SIMD width of the device is still 32, but each compute unit (SMM) can issue instructions to four different warps at once.