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...
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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 not even sure what I am asking but I would like to virtualize 3 servers with 8 GTX Nvidia GPUs each (right now each server is used on its own).
Is it possible to virtualize all 3 of them as a cluster to train a PyTorch model?
Thank you in advance!
I am running the same CNN model on the same dataset (with 50000 training examples) with exactly same parameters on both Google Colab (I think it has K80 GPU) and my own system (with GTX 1080 GPU and 8700K CPU). I am using the batch_size=32 on both but I am surprised to see that while training, Google Colab shows me;
while my own system (using PyCharm), shows me
I can understand that the difference in accuracies is may be due to different random initializations but why on Google Colab it shows the training during each epoch in terms of number of batches 1563/1563 while on my machine, it shows me in terms of number of examples in the training set i.e. 50000/50000
In both cases I am using tf.keras.
Has it anything to do with the version. On my (Windows) machine, the tensorflow-gpu version is 2.1.0 whereas, on the Google Colab (probably Linux) machine, it is 2.2.0. I cannot upgrade the version on my windows machine from 2.1.0 to 2.2.0, probably they are same as can be seen here;
Please correct me if I am wrong.
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?
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?