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)
Related
I'm not familiar with using GPUs with Tensorflow Keras. My deep learning model is taking an excessive amount of time to train. I have a strong feeling that there is a serious problem with executing my program on GPUs. Let me try to explain the issues:
To use the GPU I copied the following line code from the internet and paste at the start of my program after importing some libraries.
fromtensorflow.compat.v1importConfigProto
fromtensorflow.compat.v1importInteractiveSession
fromkerasimportbackendasK
config=ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction=0.555
config.gpu_options.allow_growth=True
session=InteractiveSession(config=config)
K.set_session(session)
To Verify that tensorflow is running with GPU. I use the following code
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
output will be something like this:
[
name: "/cpu:0"device_type: "CPU",
name: "/gpu:0"device_type: "GPU"
]
However, My output:
[name: "/device:CPU:0" device_type: "CPU" memory_limit: 268435456
locality { } incarnation: 14526302269534055562 xla_global_id: -1 ]
It means my program is not utilizing any GPU. right?
Secondly, I can view the following lines when I run that above code:
22-04-08 22:36:29.246258: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
2022-04-08 22:36:29.250802: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1850] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...
It seems Problem to run the program on GPU. I do not know why its showing these lines?
Aside from that, when I view invidia-smi. My program appears to be running on GPU, but there are three copies running with the same PID: 45313, as shown in the screenshot . What does it imply?
Secondly, I feel that my model size is big, but the consumed memory (see in the above image PID: 45313) on the GPU is too small. I am not exactly sure, but that makes my model training too slow. It seems there is some problem. How can I check my model size and how I identify why the model occupation size is too small?
On one side, where I can view from the invidia-smi that my program is running on the GPU. Where on the other side when I see (by using: tf.debugging.set_log_device_placement(True) ) which lines of code is executed by GPU. I can see the following output throughout the processing:
2022-04-08 23:29:36.539534: I tensorflow/core/common_runtime/eager/execute.cc:1289] Executing op DestroyResourceOp in device /job:localhost/replica:0/task:0/device:CPU:0
2022-04-08 23:29:36.539567: I tensorflow/core/common_runtime/eager/execute.cc:1289] Executing op DestroyResourceOp in device /job:localhost/replica:0/task:0/device:CPU:0
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.
I'm trying to get started with tensorflow using the python interface. I'm building an image classification system explained Here. But when running the code, epochs take too much time, almost 2 minutes for 1 epoch, and if number of steps are increased, epoch running time increases exponentially.
My system configurations are:
and software configurations are:
Python 3.7
Spyder 4
Tensorflow 2.2.0
I found similar thread Here but in my case, basic operations are fast enough.
How can I improve performance of tensorflow
Sadly, both TensorFlow and PyTorch use only Cuda as a backend for GPU acceleration and they don't support any of the Mac'a GPUs - Intel's or AMD's. This means that your TensorFlow code would run only on CPU.
I have converted a tensorflow inference graph to tflite model file (*.tflite), according to instructions from https://www.tensorflow.org/lite/convert.
I tested the tflite model on my GPU server, which has 4 Nvidia TITAN GPUs. I used the tf.lite.Interpreter to load and run tflite model file.
It works as the former tensorflow graph, however, the problem is that the inference became too slow. When I checked out the reason, I found that the GPU utilization is simply 0% when tf.lite.Interpreter is running.
Is there any method that I can run tf.lite.Interpreter with GPU support?
https://github.com/tensorflow/tensorflow/issues/34536
CPU is kind of good enough for tflite, especially multicore.
nvidia GPU likely not updated for tflite, which is for mobile GPU platform.
Conspiracy: they (TF-NVIDIA) hand-shake to not let TFlite working on GPU ? oo easy to make one.
Steve
I have a little knowledge of using GPU to train model.
I am using K-means from scikit-learn to train my model.
Since my data is very large, is it possible to train this model using GPU to reduce computation time?
or could you please suggest any methods to use GPU power?
The other question is if I use TensorFlow to build the K-means as shown in this blog.
https://blog.altoros.com/using-k-means-clustering-in-tensorflow.html
It will use GPU or not?
Thank you in advance.
To check if your GPU supports CUDA: https://developer.nvidia.com/cuda-gpus
Scikit-learn hasn't supported CUDA so far. You may want to use TensorFlow: https://www.tensorflow.org/install/install_linux
I hope this helps.
If you have CUDA enabled GPU with Compute Capability 3.0 or higher and install GPU supported version of Tensorflow, then it will definitely use GPU for training.
For additions information on NVIDIA requirements to run TensorFlow with GPU support check the following link:
https://www.tensorflow.org/install/install_linux#nvidia_requirements_to_run_tensorflow_with_gpu_support