GpuArrayException: Error loading library: -1
I tensorflow/stream_executor/dso_:135] successfully opened CUDA library libcurand.so.7.5 locallyīut if I try to do the same with Theano $ KERAS_BACKEND=theano python -c "from keras import backend"ĮRROR (theano.gpuarray): Could not initialize pygpu, support disabledįile "/home/darth/.miniconda/envs/foo/lib/python2.7/site-packages/theano/gpuarray/_init_.py", line 164, in įile "/home/darth/.miniconda/envs/foo/lib/python2.7/site-packages/theano/gpuarray/_init_.py", line 151, in useįile "/home/darth/.miniconda/envs/foo/lib/python2.7/site-packages/theano/gpuarray/_init_.py", line 60, in init_devįile "pygpu/gpuarray.pyx", line 614, in (pygpu/gpuarray.c:9415)įile "pygpu/gpuarray.pyx", line 566, in _init (pygpu/gpuarray.c:9106)įile "pygpu/gpuarray.pyx", line 1021, in ._cinit_ (pygpu/gpuarray.c:13468) I tensorflow/stream_executor/dso_:135] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_:135] successfully opened CUDA library libcufft.so.7.5 locally I tensorflow/stream_executor/dso_:135] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_:135] successfully opened CUDA library libcublas.so.7.5 locally Testing that it works with TensorFlow $ KERAS_BACKEND=tensorflow python -c "from keras import backend" This sets up proper Python version, MKL, CUDA-Toolkit and cudNN in just a single line, without having to download/install CUDA libraries by hand, setting up path, or installing any extra system libraries. I’ve tried installing Tensorflow with GPU support on Python 2.7 conda create -n foo python=2.7 tensorflow-gpu keras But to my surprise Miniconda/Anaconda seems to solve basically all of the issues, yes almost nobody mentions it.
#Install theano for mac windows#
This lead to incredibly frustration trying to setup everything Arch, Windows 10, Ubuntu 16.04 and OpenSUSE Tumbleweed at the same time (yeah I know my setup is complicated, but I can’t imagine everyone doing data science runs Ubuntu?). Looking over TensorFlow, Keras, Theano, libgpuarray and other sites, it surprises me that they all have instructions for Ubuntu, macOS, Windows, and almost all of them are very specific (installing pip from specific url with tons of system libraries). For this reason I kinda wanted to get to a simple way to setup everything to run locally, without as much hassle as possible. I’m not trying to criticize the course, since a lot of people use laptops that can’t be upgraded (especially macs with the Radeon cards), but a lot of people (at least from the ones I know) own a desktop computer with a decent graphics card. I mean if you’re planning on learning deep learning, you probably intend to need that GPU for more than a few months, in which case you can just buy the GPUs for 2 months worth of subscription. For example, the first video mentions how great it is that you can rent a 2x970GTX for $200 monthly, which to me feels horribly overpriced. Personally, I don’t really like doing things in the cloud unless I have to, especially since it gets really expensive. So far I’ve watched about 80% of part 1 and I love the videos, but there is one thing that I’d like to address, and that is the overall setup of everything, and especially AWS.
0 kommentar(er)
