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&#x27;s attempt, but there&#x27;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&#x27;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&#x27;re interested in a more involved experiment, develop and debug your task locally on your laptop. By the time you&#x27;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&#x27;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&#x27;re using it for education.<p>[1]: <a href="https:&#x2F;&#x2F;github.com&#x2F;tensorflow&#x2F;tensorflow&#x2F;issues&#x2F;25" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;tensorflow&#x2F;tensorflow&#x2F;issues&#x2F;25</a>