I am trying to train a pytorch model on my local machine. It has the following GPUs:
As you see the second is NVIDIA and thus should be used with CUDA. In fact if I check torch.cuda.device_count() it returns 1 and torch.cuda.get_device_name() returns NVIDIA GeForce 930MX. When I run the script however the usage of the built-in Intel GPU goes up to 100% and then the program crashes with:
OSError: [WinError 1450] Insufficient system resources exist to complete the requested service
The usage (as seen from the task manager) of the targeted GPU (NVIDIA) remains 0% so it has not been called.
What configuration steps I might have messed up and what would you propose in order to run PyTorch on the proper GPU.
*Using the LTS versions of torch and CUDA as of the day of posting the question.
Related
I am doing a project which aims to reduce GPU usage reduction in video surveillance. In my project, detected objects using Yolo is delivered to pre trained LSTM model and based on detected number of objects, LSTM predicts future object occurrence and suggests to release GPU. I have combined them and run. It works well
However, my goal is to get GPU usage(number of GPUs and current memory usage) for only this process, not general GPU usage? and I should record it into .txt file
I tried some python libraries like GPUtil, gpustat and others. But I am getting general GPU usage not usage for current task. Below results:
ID GPU MEM
--------------
0 17% 89%
Below is my PC specifications:
NVIDIA-SMI 512.77 Driver Version: 512.77 CUDA Version: 11.6 NVIDIA GeForce RTX 2070 SUPER
Is there possible or relevant solution for getting GPU usage within specific process?
Any help appreciated!!!
I try to run tensorflow 1.13.1 inside a docker (the image with the wanted configuration is evariste/autodl:gpu-latest).
The docker has access to a RTX 2080 Ti GPU.
I get the following error:
2020-09-10 16:09:47.428460: F tensorflow/core/platform/cpu_feature_guard.cc:37] The TensorFlow library was compiled to use SSE4.1 instructions, but these aren't available on your machine.
SSE4.1 is an instruction set supported by CPU, not GPU. Thus you need to check if your CPU supports it; more discussions about this topic can be found here.
I've just installed a new GPU (RTX 2070) in my machine alongside the old GPU. I wanted to see if PyTorch picked up it, so following the instructions here: How to check if pytorch is using the GPU?, I ran the following commands (Python3.6.9, Linux Mint Tricia 19.3)
>>> import torch
>>> torch.cuda.is_available()
True
>>> torch.cuda.current_device()
Killed
>>> torch.cuda.get_device_name(0)
Killed
Both of the two Killed processes took some time and one of them froze the machine for half a minute or so. Does anyone have any experience with this? Are there some setup steps I'm missing?
If I understand correctly, you would like to list the available cuda devices. This can be done via nvidia-smi (not a PyTorch function), and both your old GPU and the RTX 2070 should show up, as devices 0 and 1. In PyTorch, if you want to pass data to one specific device, you can do device = torch.device("cuda:0") for GPU 0 and device = torch.device("cuda:1") for GPU 1. While running, you can do nvidia-smi to check the memory usage & running processes for each GPU.
To anyone seeing this down the line, whilst I had the nvidia driver set up I needed to get a couple of other things set up, such as CUDA and the CuDNN toolbox. The best article I found on the subject was https://hackernoon.com/up-and-running-with-ubuntu-nvidia-cuda-cudnn-tensorflow-and-pytorch-a54ec2ec907d.
I have installed Keras with gpu support in R based on Tensorflow with gpu support. This is installed with these steps:
https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781
If I run the Bosting housing example code from the book Deep learning with R, I receive this screen:
Can I conclude that the code runs on the GPU?
Or is this line from the picture above giving an error:
GPU libraries are statically linked, skip dlopen check.
During running the code the GPU is running only on 3% of capacity while the CPU is running on 20-25%.
The code is NOT running faster than while I initially did run the code without installing GPU support.
Thank you!
Yes, tensorflow is running with GPU enabled. Boston Housing is a relatively small dataset and probably does not benefit from using the GPU to a large degree. The lines below indicate it is running on the GPU. "Created tensorflow device (/job:localhost/replica:0/task:0device:GPU:0".
From the guide at Tensorflow
You can set tf.debugging.set_log_device_placement(True) in order to explicitly see where each operation is running. THE R equivalent is below.
library(tensorflow)
tf$debugging$set_log_device_placement(TRUE)
My system is ubuntu 16.04 version my laptop is dell Inspiron-5521 and it has intel graphic card but tensorflow needs nvidia graphics for cuda support.
Is there any way where i can run tensorflow with GPU(with CPU is working) on intel graphics.
During installation of tensorflow-gpu i have no error when i import i get
"
Failed to load the native TensorFlow runtime
."
Did some digging then found to install cuda downloaded the "cuda_9.1.85_387.26_linux.run" file but faces issues while running it
"Detected 4 CPUs online; setting concurrency level to 4.
The file '/tmp/.X0-lock' exists and appears to contain the process ID
'1033' of a runnning X server.
It appears that an X server is running. Please exit X before
installation. If you're sure that X is not running, but are getting
this error, please delete any X lock files in /tmp."
Deleted files from tmp folder and tried still same issue.
To run tensorflow-gpu you need nvidia card. You'll need to stick to running normal tensorflow on CPU.
Is Intel based graphic card compatible with tensorflow/GPU?
Tensorflow does not support OpenCL API that you can use with Intel or AMD, only CUDA. CUDA is a proprietary NVidia technology that only works with NVidia GPUs.
You may like to search for machine learning frameworks that utilise OpenCL, but I only find some niche projects at the moment.
I had to switch from AMD to NVidia to be able to run Tensorflow calculations on GPU.