Honestly, there are enough issues with TensorFlow right now due to CUDA 3.0 that using it with AWS is highly problematic. I appreciate the author's attempt, but there's no way the five lines of code he changed to allow CUDA 3.0 has fixed any of the issues found in [1], such as NaNs during training, equally slow training on a g2.2xlarge as a g2.8xlarge, etc ...<p>If you're just interested in playing around, then your laptop will do fine - TensorFlow is happy with just about any hardware you throw at it. Hell, your modern Android phone will run it =]<p>If you're interested in a more involved experiment, develop and debug your task locally on your laptop. By the time you're ready for large scale training, there might be a stable and battle tested AMI such that people are no longer reporting issues in [1] about it.<p>Again, if you're interested, follow the CUDA 3.0 issue on GitHub[1] - this is nowhere near a solved problem and will only cause headaches if you're using it for education.<p>[1]: <a href="https://github.com/tensorflow/tensorflow/issues/25" rel="nofollow">https://github.com/tensorflow/tensorflow/issues/25</a>
Top link points to a non-existent AMI so this "recipe" -- like almost every "here's how to ..." recipe on GitHub that gets posted on HN -- doesn't work. I swear, it's like 100% fail on these types of posts.<p>Also this "real GPU" is explicitly called out in the Google docs as unsupported.