I am currently looking into running tensorflow on GPU. I was reading about CUDA pinned memory.
I was not able to find anyway to set this when using tensorflow python library.
Any idea how it can be done?
It's automatically used for any memcpy between CPU and GPU. If you want more sophisticated functionalities, you can write a kernel that explicitly uses them.
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The question is pretty straightforward but nothing has really been answered.
Pretty simple, how do I know that when I build a Sequential() model in tensorflow via Keras it's going to use my GPU?
Normally, in Torch, so easy just use 'device' parameter and can verify via nvidia-smi volatility metric. I tried it while building model in TF but nvidia-smi shows 0% usage across all GPU devices.
Tensorflow uses GPU for most of the operations by default when
It detects at least one GPU
Its GPU support is installed and configured properly. For information regarding how to install and configure it properly for GPU support: https://www.tensorflow.org/install/gpu
One of the requirements to emphasize is that specific version of CUDA library has to be installed. e.g. Tensorflow 2.5 requires CUDA 11.2. Check here for the CUDA version required for each version of TF.
To know whether it detects GPU devices:
print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))
It will also print out debug messages by default to stderr to indicate whether the GPU support is configured properly and whether it detects GPU devices.
To validate using nvidia-smi that it is really using GPU:
You have to define a sufficiently deep and complex neural network model such that the bottleneck is in the GPU side. This can be achieved by increasing the number of layers and the number of channels in each of the layers.
When doing training or inference of the model like model.fit() and model.evaluate(), the GPU utilization in the logging from nvidia-smi should be high.
To know exactly where each operation will be executed, you can add the following line in the beginning of your codes
tf.debugging.set_log_device_placement(True)
For more information: https://www.tensorflow.org/guide/gpu
I make a Project using TensorFlow Object detection.
Windows might have encountered an error because of the GPU module version. Linux is trying again now.
If you look at some review posts, it seems that GPU version has not been used. Is that right?
I am using TensorFlow1.13 GPU version (with cuda), and I do not want to use cuDNN to do the convolution due to some reasons. Anyone know how to do that plz?
You can't use tensorflow GPU without CUDA because Keras is based on it. Tensorflow packs another library inside it that is called Keras which uses this dependency.
I have previously asked if it is possible to run tensor flow with gpu support on a cpu. I was told that it is possible and the basic code to switch which device I want to use but not how to get the initial code working on a computer that doesn't have a gpu at all. For example I would like to train on a computer that has a NVidia gpu but program on a laptop that only has a cpu. How would I go about doing this? I have tried just writing the code as normal but it crashes before I can even switch which device I want to use. I am using Python on Linux.
This thread might be helpful: Tensorflow: ImportError: libcusolver.so.8.0: cannot open shared object file: No such file or directory
I've tried to import tensorflow with tensorflow-gpu loaded in the uni's HPC login node, which does not have GPUs. It works well. I don't have Nvidia GPU in my laptop, so I never go through the installation process. But I think the cause is it cannot find relevant libraries of CUDA, cuDNN.
But, why don't you just use cpu version? As #Finbarr Timbers mentioned, you still can run a model in a computer with GPU.
What errors are you getting? It is very possible to train on a GPU but develop on a CPU- many people do it, including myself. In fact, Tensorflow will automatically put your code on a GPU if possible.
If you add the following code to your model, you can see which devices are being used:
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
This should change when you run your model on a computer with a GPU.
To configure theano we create a .theanorc file in your home folder and add the following to set up theano to run on GPU.
[global]
device = gpu
floatx = float32
but sometime i save in configure that smbd put device = cuda, what's the difference?
As i understans, if you use cuda, it should work faster because cuda drivers will manage gpu more better,not getting other jobs for gpu at calculation time
Setting device=gpu in your .theanorc file instructs Theano to use the cuda backend. On the other hand, setting device=cuda instructs theano to use the libgpuarray backend. Both lead to the use of the GPU (in contrast to setting device=cpu in your .theanorc file which would lead to the use of the CPU), and the difference is the low-level API they use to communicate with it. The libgpuarray backend is the newer one of the two that is a wrapper that allows Theano to communicate with both cuda (for NVIDIA GPUs) and opencl (for non-NVIDIA GPUs).
Note that the cuda backend will be deprecated in the next release of Theano and it is recommended that you always use the libgpuarray backend henceforth.