The best Tensorflow is the one you have on your machine.
In my opinion, the bottleneck on a DNN solution is not training, but data preparation and iterating your model to the point where it’s reasonable to start investing kilowatt-hours of electricity to the training. So I have Tensorflow on all my machines, including my Macs, even though as of Tensorflow 1.2 GPU support is simply not available for Tensorflow on the Mac. (I’m not sure what’s going on, but suspect it may have something to do with licensing NVidia’s CuDNN library.)
Having said that, GPU support for TensorFlow is much faster than CPU-only Tensorflow (in some quick tests on my Windows laptops, ~8x). With GPU-supported Tensorflow, it’s that much easier to iterate your model until your training and validation curves start to look encouraging. At that point, in my opinion it makes sense to move your training to the cloud. There’s a little more friction in terms of moving data and starting and stopping runs and you’re paying for processing, but hopefully you’ve gotten to the point where training time is the bottleneck.
Mac Tensorflow GPU: I’d like to think this will change in the future, but as of August 2017: Nope.
There are a very few people who seem to have figured out how to build Tensorflow with GPU support on the Mac from sources, but the hoop-jumping and yak shaving that seems necessary seems very high to me.
Windows Tensorflow GPU: Yes, but it’s a little finicky. Here are some install notes:
- Install NVidia Cuda 8 (not the Cuda 9 RC)
- Install NVidia CuDNN 5.1 (not the CuDNN 7!)
- Copy the CuDNN .dll to your Cuda /bin directory (probably /Program Files/NVidia GPU Computing Toolkit/Cuda/v8.0/bin/)
- Create an Anaconda environment from an administrative shell. Important: use –python=3.5
- Install tensorflow using:
pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.2.1-cp35-cp35m-win_amd64.whl
I think the “cp35” is the hint that you have to use Python 3.5, so if the page at https://www.tensorflow.org/install/install_windows changes to show a different .whl file, you’d have to set the python in your Anaconda environment differently.
– Validate that you’ve got GPU capability:
import tensorflow as tf tf.Session().run(tf.constant('hello'))
This should result in a cascade of messages, many of which say that Tensorflow wasn’t compiled with various CPU instructions, but most importantly, towards the end you should see a message that begins:
Creating Tensorflow device (/gpu:0)
which indicates that, sure enough, Tensorflow is going to run on your GPU.
Hope this helps!