I'm trying to run tensorflow app using GPU. But, I'm not sure that GPU is automatically making process of threading. Could I control the GPU threads through tensorflow framework? How can I sure that GPU is working efficiently means nearly all threads are working?
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When multiple PyTorch process is running inference on the same Nvidia GPU. I would like to know what happens when two kernel requests(cuLaunchKernel) from different contexts are handled by CUDA? Can CUDA GPU have a FIFO queue for those kernel requests?
I have no idea about measuring the state of CUDA when running my PyTorch program. Any advice on how to profile a Nvidia GPU when running multiple concurrent jobs is helpful!
Kernels from different contexts never run at the same time. They run in time-sharing way. (Unless MPS is used)
Within the same CUDA context, kernels launched on the same CUDA stream never run at the same time. Instead, they are serialized by the launch order and GPU executes them one at a time. So CUDA stream is similar to a queue in the CUDA context. Kernels launched on different CUDA streams (in the same context) have the potential to run concurrently.
Pytorch by default uses one CUDA stream. You can use APIs to manipulate multiple streams: https://pytorch.org/docs/stable/notes/cuda.html#cuda-streams
I am looking into how to do point-to-point communication with multiple GPUs on separate nodes in PyTorch.
As of version 1.10.0, the documentation page for PyTorch says question marks for send and recv for GPU with the MPI backend. What does this mean? If anyone has successfully set up PyTorch so that torch.distributed allows point-to-point communication on multiple GPUs, please let me know and how you set it up. Specifically, which MPI are you using? What about the versions of pyTorch and Cuda?
I guess I'll post what I have learned so far.
Pytorch does seem to support p-to-p communication with MPI on GPU. However, this requires you to have a Cuda-aware MPI. (If your MPI isn't Cuda-aware, you'll need to build MPI from source with a specific parameter). In addition, if your Pytorch doesn't have MPI enabled, you need to compile Pytorch from source with MPI installed. This seems a very complicated route to go.
However, it seems the documentation I linked to is misleading. Looking at the release note, Pytorch supports send/recv in NCCL backend since 1.8.0... That being said, I have tried doing send/recv with NCCL but it throws errors saying NCCL are getting invalid arguments. I'm not sure if it's my problem or there are still bugs in pytorch distributed code.
I have been using Brain.js to train a Neural network, and it has been working just fine, except it seems that it is only using CPU to train the neural net.
I am using Windows, and the Task Manager shows the Node process using ~25% CPU (I assume it is maxing out a single thread). Looking at MSI Afterburner, the GPU is not being utilized at all.
The GPU is an Nvidia RTX 2060 super.
What can I do to make Brain.js use my GPU? I have searched around but have not been able to find much info at all so far...
I am trying to set up Keras in order to run models using my GPU. I have a Radeon RX580 and am running Windows 10.
I saw realized that CUDA only supports NVIDIA GPUs and was having difficulty finding a way to get my code to run on the GPU. I tried downloading and setting up plaidml but afterwards from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
only printed that I was running on a CPU and there was not a GPU available even though the plaidml setup was a success. I have read that PyOpenCl is needed but have not gotten a clear answer as to why or to what capacity. Does anyone know how to set up this AMD GPU to work properly? any help would be much appreciated. Thank you!
To the best of my knowledge, PlaidML was not working because I did not have the required prerequisites such as OpenCL. Once I downloaded the Visual Studio C++ build tools in order to install PyopenCL from a .whl file. This seemed to resolve the issue
Is Keras by default utilizes all the GPUs on my machine? and if not, How to force Keras to utilize all the GPUs? and Finally, How can I check the GPU utilization per application?