I wonder if they will apply the same terms of service as with their Cloud Machine Learning offerings (Auto ML, Cloud Vision, etc).<p>A snippet from <a href="https://cloud.google.com/terms/service-terms#12-google-cloud-platform-machine-learning-group-and-google-cloud-machine-learning-engine" rel="nofollow">https://cloud.google.com/terms/service-terms#12-google-cloud...</a>:<p><pre><code> Customer will not, and will not allow third parties to: (i) use these Services
to create, train, or improve (directly or indirectly) a similar or competing
product or service or (ii) integrate these Services with any applications for
any embedded devices such as cars, TVs, appliances, or speakers without Google's
prior written permission. These Services can only be integrated with
applications for the following personal computing devices: smartphones, tablets,
laptops, and desktops. In addition to any other available remedies, Google may
immediately suspend or terminate Customer's use of these Services based on any
suspected violation of these terms, and violation of these terms is deemed
violation of Google's Intellectual Property Rights. Customer will provide Google
with any assistance Google requests to reasonably confirm compliance with these
terms (including interviews with Customer employees and inspection of Customer
source code, model training data, and engineering documentation). These terms
will survive termination or expiration of the Agreement.</code></pre>
Cloud TPU pods are seriously amazing. I'm a researcher at Google working on speech synthesis, and they allow me to flexibly trade off resource usage vs. time to results with nearly linear scaling due to the insanely fast interconnect. TPUs are already fast (non-pods, i.e. 8 TPU cores are 10x faster for my task than 8 V100s) but having pods open up new possibilities I couldn't build easily with GPUs. As a silly example, I can easily train on a batch size of 16k (typical batch size on one GPU is 32) if I want to by using one of the larger pod sizes, and it's about as fast as my usual batch size as long as the batch size per TPU core stays constant. Getting TPU pod quota was easily the single biggest productivity speedup my team has ever had.
If you want to view their header image at larger size but right-clicking doesn't give you the option to "Open image in new tab", the direct link is below. Not a big deal, but it might save a few clicks for some:<p><a href="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/TPUpod.png" rel="nofollow">https://storage.googleapis.com/gweb-cloudblog-publish/origin...</a>
Unfortunately for Google, NVIDIA's offerings are very strong, and TPUs are a pain the rear to use and require TensorFlow, which in itself is a pain to use, making it doubly painful, to the extent that using their offering requires a significant degree of desperation or not knowing any better.