If my GPU runs out of memory while running tensorflow is there any way to use in parallel the computer memory, for example while compiling tensorflow is there any configuration.
I read in this link the section of "Allowing GPU memory growth" but didn't saw anything about share resources.
Thanks in advance
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Every time I need to train a 'large' deep learning model I do it from Google Collab, as it allows you to use GPU acceleration.
My pc has a dedicated GPU, I was wondering if it is possible to use it to run my notebooks locally in a fast way. Is it possible to train models using my pc GPU? In that case, how?
I am open to work with DataSpell, VSCode or any other IDE.
Nicholas Renotte has a great 'Getting Started' video that goes through the entire process of setting up GPU accelerated notebooks on your PC. The stuff you're interested starts around the 12 minute mark.
Yes, it is possible to run .ipynb notebooks locally using GPU acceleration. To do so, you will need to install the necessary libraries and frameworks such as TensorFlow, PyTorch, or Keras. Depending on the IDE you choose, you will need to install the relevant plugins and packages for GPU acceleration.
In terms of IDEs, DataSpell, VSCode, PyCharm, and Jupyter Notebook are all suitable for running notebooks locally with GPU acceleration.
Once the necessary libraries and frameworks are installed, you will then need to install the appropriate drivers for your GPU and configure the environment for GPU acceleration.
Finally, you will need to modify the .ipynb notebook to enable GPU acceleration and specify the number of GPUs you will be using. Once all the necessary steps have been taken, you will then be able to run the notebook locally with GPU acceleration.
Is there a way to run RAPIDS without a GPU? I usually develop on a small local machine without a GPU, then push my code to a powerful remote server for real use. Things like TensorFlow allow switching between the CPU and GPU depending on if they're available. Can an equivalent thing be done with RAPIDS? Even if it's slow, being able to test things on a machine without a GPU would be extremely helpful.
There isn't a way to use RAPIDS without a GPU, and part of the reason for that is we're following the APIs the community has adopted in CPU packages across Pandas, Numpy, SKLearn, NetworkX, etc. This way it should be as easy as swapping an import statement to get something working on the CPU vs the GPU.
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.
I am using Windows 7. After i tested my GPU in tensorflow, which was awkwardly slowly on a already tested model on cpu, i switched to cpu with:
tf.device("/cpu:0")
I was assuming that i can switch back to gpu with:
tf.device("/gpu:0")
However i got the following error message from windows, when i try to rerun with this configuration:
The device "NVIDIA Quadro M2000M" is not exchange device and can not be removed.
With "nvida-smi" i looked for my GPU, but the system said the GPU is not there.
I restarted my laptop, tested if the GPU is there with "nvida-smi" and the GPU was recogniced.
I imported tensorflow again and started my model again, however the same error message pops up and my GPU vanished.
Is there something wrong with the configuration in one of the tensorflow configuration files? Or Keras files? What can i change to get this work again? Do you know why the GPU is so much slower that the 8 CPUs?
Solution: Reinstalling tensorflow-gpu worked for me.
However there is still the question why that happened and how i can switch between gpu and cpu? I dont want to use a second virtual enviroment.
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.