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Scenario: You have multiple GPUs on a single machine running Linux, but you want to use just one. By default, Keras allocates memory to all GPUs unless you specify otherwise. You use a Jupyter Notebook to run Keras with the Tensorflow backend.
Run this bit of code in a cell right at the start of your notebook (before importing tensorflow or keras).
On a Ubuntu laptop, with a NVIDIA GPU, when trying to open Mendeley, you get this rather unhelpful error:
The application Mendeley Desktop has closed unexpectedly.
I’m sure there are many causes for this error, but one unexpected reason you might get this error is related to your graphics card.
If you have a NVIDIA GPU on your laptop, try to switch to your Intel graphics card instead of NVIDIA..
To switch to your Intel graphics card, open your terminal and type:
sudo prime-select intel
Then restart Mendeley. Like magic and deep learning, it just seems to work.
(if you need to switch back to your NVIDIA card, just type sudo prime-select nvidia)
Scenario: You’re trying to get your GPU to work in TensorFlow on a Ubuntu Laptop. You’ve already installed Tensorflow, Cuda, and Nvidia drivers.
You run python and import TensorFlow:
import tensorflow as tf
And you see encouraging messages like: "successfully opened CUDA library libcublas.so locally"
But in Python, when you run,
tf.Session()
You get this cryptic error:
failed call to cuInit: CUDA_ERROR_UNKNOWN
Here’s how to fix this.
Continue reading “TensorFlow – failed call to cuInit: CUDA_ERROR_UNKNOWN”
I’m running Ubuntu on my Lenovo Y50 laptop, with a Nvidia GPU. And every time I do an update (or restart it?), I see the Ubuntu logo, hear the chime to log in, and then see a blank black screen, or a small white dot in the upper corner.
Other times, after a reboot, I get to the login screen, enter my username and password, then everything flickers violently, and it loops back to asks me to enter in my info again.
Today this post is not about how to permanently fix this (although that would be nice), but rather how to get your GUI back (until you update/restart your machine again).
It seems that on some laptops, the Nvidia drivers and Ubuntu do not always nicely play together. Why? I am not sure.
But anyways, here’s how to get fix your laptop when Ubuntu has a black screen on login (assuming your problem is related to the Nvidia drivers).
Continue reading “ubuntu – black screen on ubuntu laptop after installing nvidia drivers”
You are using PyCaffe (Python interface for Caffe) and training a deep neural network directly within Python (although I think the same command holds for MATLAB).
You are on a machine with 2 GPUs and you want to specify which GPU to use for training. This is useful so you can train two different models at the same time on each GPU. Note that here we refer to training two different models on two different GPUs on the same machine, not a single model on two GPUs.
(side note: it seems to me that running two different jobs on the same GPU drastically slows GPU training. It’s so much slower that I only train a single model on a single GPU at a time. Running two different jobs on two different GPUs seems to be okay though)
Continue reading “CAFFE – how to specify which GPU to use in PyCaffe”
