I have been searching for this on the internet but got confused. Some said certain dlib models are not possible to run in GPU and some said dlib models can be configured to run on GPU only by using the right CUDA settings.
May I ask if I am given a dlib model, how to check if a dlib model can be run on GPU only? Rather than testing it out through actually configuring it with CUDA? If no, can you explain why?
By the way the model I am trying to understand its GPU supports is shape_predictor_68_face_landmarks.dat from https://pyimagesearch.com/2018/04/02/faster-facial-landmark-detector-with-dlib/
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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.
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?
Is it possible to run Tensorboard on a machine without CUDA support?
I'm working at a computation center (via ssh) which has two major clusters:
CPU-Cluster which is a general workhorse without CUDA support (no dedicated GPU)
GPU-Cluster with dedicated GPUs e.g. for running neural networks with tensorflow-gpu.
The access to the GPU-cluster is limited to Training etc. such that I can't afford to run Tensorboard on a machine with CUDA-support. Instead, I'd like to run Tensorboard on the CPU-Cluster.
With the TF bundled Tensorboard I get import errors due to missing CUDA support.
It seems reasonable that the official Tensorboard should have a mode for running with CPU-only. Is this true?
I've also found an inofficial standalone Tensorboard version (github.com/dmlc/tensorboard), does this work without CUDA-support?
Solved my problem: just install tensorflow instead of tensorflow-gpu.
Didn't work for me for a while due to my virtual environment (conda), which didn't properly remove tensorflow-gpu.
Tensorboard is not limited by whether a machine has GPU or not.
And as far as I know, what Tensorboard do is parsing events pb files and display them on web. There is not computing, so it doesn't need 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.