TensorFlow 2.0 with GPU on Debian/sid
Some time ago I have been written about how to get Tensorflow (1.x) running on current Debian/sid back then. It turned out that this isn’t correct anymore and needs an update, so here it is, getting the most uptodate TensorFlow 2.0 running with nVidia support running on Debian/sid.
Step 1: Install CUDA 10.0
Follow more or less the instructions here and do
wget -O- https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub | sudo tee /etc/apt/trusted.gpg.d/nvidia-cuda.asc echo "deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /" | sudo tee /etc/apt/sources.list.d/nvidia-cuda.list sudo apt-get update sudo apt-get install cuda-libraries-10-0
Warning! Don’t install the 10-1 version since the TensorFlow binaries need 10.0.
This will install lots of libs into
/usr/local/cuda-10.0 and add the respective directory to the ld.so path by creating a file
Step 2: Install CUDA 10.0 CuDNN
One difficult to satisfy dependency are the CuDNN libraries. In our case we need the version 7 library for CUDA 10.0. To download these files one needs to have a NVIDIA developer account, which is quick and painless. After that go to the CuDNN page where one needs to select
Download cuDNN v7.N.N (xxxx NN, YYYY), for CUDA 10.0 and then
cuDNN Runtime Library for Ubuntu18.04 (Deb).
At the moment (as of today) this will download a file
libcudnn7_184.108.40.206-1+cuda10.0_amd64.deb which needs to be installed with
dpkg -i libcudnn7_220.127.116.11-1+cuda10.0_amd64.deb.
Step 3: Install Tensorflow for GPU
This is the easiest one and can be done as explained on the TensorFlow installation page using
pip3 install --upgrade tensorflow-gpu
This will install several other dependencies, too.
Step 4: Check that everything works
Last but not least, make sure that TensorFlow can be loaded and find your GPU. This can be done with the following one-liner, and in my case gives the following output:
$ python3 -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))" ....(lots of output) 2019-10-04 17:29:26.020013: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3390 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1) tf.Tensor(444.98087, shape=(), dtype=float32) $
I haven’t tried to get R working with the newest TensorFlow/Keras combination, though. Hope the above helps.