Google just released a fantastic-looking deep learning library called
TensorFlow, complete with tutorials, and model-zoo-like examples.
Fortunately, the framework is very reminiscent of
Theano, and has a Python front-end over a computation graph construction machine in C++ / CUDA (no OpenCL as far as I can tell).
These instructions are straight off Google's Installation page, but work-for-me :
Create a VirtualEnv
virtualenv --system-site-packages ~/tensorflow . ~/tensorflow/bin/activate
CPU Version (11Mb download)
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
GPU Version (50Mb download)
(a 1 character difference...)
pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
Test it on MNIST
NB : This downloads about 13Mb of MNIST data files, if they're missing (likely on first run) :
GPU Issues : TensorFlow really wants
cuDNN v6.5 (not v7.0)
If you get something like :
... I tensorflow/stream_executor/cuda/cuda_dnn.cc:1062] Unable to load cuDNN DSO. ...
... you haven't got
cuDNN installed like
- Go to the Nvidia cuDNN legacy library download site and download the v6.5 library
Uncompress and copy the
cudnn files into the toolkit directory. Assuming the toolkit is installed in
tar xvzf cudnn-6.5-linux-x64-v2.tgz sudo cp cudnn-6.5-linux-x64-v2/cudnn.h /usr/local/cuda/include sudo cp cudnn-6.5-linux-x64-v2/libcudnn* /usr/local/cuda/lib64