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.
Related
I've been recently using a TF object detection model I created for a ML project I'm working on. But I feel like I'm getting subpar performance from my GPU running inference, 3060ti. I am using TF 2.4, Cuda 11.2, Cudnn 8.04. I was wondering if there was a way I could benchmark my install to other users to compare speeds. I am running with Windows 10 currently, but soon want to try running TF in container with Ubuntu.
I wanted to teach an image classification CNN, and use Keras for it.
The image dimensions are 300x300x3.
I have trained a CNN with 2M parameters, I used MobileNet of Keras for transfer learning, however I freeze last 63 layers and add dense layers at the bottom, the last layer has 2 unit and Softmax activation.
To make predictions, I load the h5 file and use OpenCV video capture to get video frames, for each frame I use model.predict(img_array).
When i look to the Task Manager of Windows 10 , I see that the Python script uses %80 of my processor but %2 of GPU. This CPU usage causes Lags on my laptop.
How can I reduce the CPU usage and force Keras to make computations with GPU?
I have Nvidia Rtx 2060 4GB and Intel Core i7-9750H on my laptop.
Tensorflow 2.1 and Keras 2.3.1
OpenCV 4.1
I have tried, but actually nothing changes.
tf.config.threading.set_inter_op_parallelism_threads(12)
tf.config.threading.set_intra_op_parallelism_threads(12)
with tf.device(\gpu:0):
model.predict(img_array)
Best regards.
Edit:
I reduce the CPU usage to %20 with declaring steps parameter in the predict method.
Please check your pip list or conda list.
Sometimes, we mistakenly install both tensorflow and tensorflow-gpu.
If you have both, the system will automatically go for tensorflow, which is the CPU one.
If that is the case, DELETE "tensorflow", keeping only "tensorflow-gpu".
If you do not see tensorflow-gpu in the first place, try installing it on conda using the following commands:
conda create -n [EnvironmentName] python=3.6
conda activate [EnvironmentName]
conda install -c conda-forge tensorflow-gpu==1.14
it will assess which version (CUDA,CUDNN, etc.) you require and download and install it directly to your environment. Then run your python file from this environment. Good luck ^_^
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)
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'm using the Mask RCNN library which is based on tenserflow and I can't seem to get it to run on my GPU (1080TI). The inference time is 4-5 seconds, during which I see a usage spike on my cpu but not my gpu. Any possible fixes for this?
It is either because that GPU_COUNT is set to 0 in config.py or you don't have tensorflow-gpu installed (which is required for tensorflow to run on GPU)