I don't get your excitement. How is this different from using 8xGPU box? If you use eight Quadro 8000 cards you have access to 384GB of memory to train your models.
the TPU equivalent of 8x quadro 8000 would be something between tpu v2-32 and tpu v3-32, and the monthly cost of tpu v2-32 is ~$8k. Plus the cost of a beefy VM. Assuming the GPU build sets you back ~$60k, it will start saving you $8k/mo after 6 months.
In the link I posted: tpu v2-8 has 64GB of total memory, v2-32 has 256GB.
As for the beefy vm - can you do heavy data preprocessing on tpus? For example elastic distortions or scaling for images? Probably not, because usually it involves OpenCV or similar libraries.
The link is talking about per-core memory. A TPUv2-8 has 300GB system memory, which you can use for training. You can verify this using the notebooks above.
(If a TPUv2-8 has 64GB memory, how can it fine tune GPT-2 1.5B using Adam with batch size 4? That requires almost 300GB.)
A TPUv3 pod is actually a bunch of individual TPUv3-8's linked together. There's 8 cores per device, so a TPUv3-512 has 512 cores divided by 8 cores per device = 64 individual TPUs. (You can get each individual TPU's IP address using `gcloud compute tpus list`: https://imgur.com/Qym4l17)
The big question is, since there are 64 individual TPUs, does that mean we have access to 300GB * 64 = 19.2 TB of memory?
I haven't tested that, but I would bet the answer is yes, for two reasons. 1. I've seen allocations of up to 7TB according to memory usage logs, so 19TB doesn't seem far fetched in comparison. 2. If you create 64 individual TPUv3-8's, then you definitely will have access to 300GB of memory on each TPU, so it's the same engineering problem either way.
Right now, people only seem to use the TPU's CPU for infeed processing / input pipeline transformations. But the CPU is quite fast – it's almost as fast as an actual TPU core.
Also, if you want to play around with a few TPUv3-8's and you have a GCE project, feel free to DM me on twitter. We just figured out how to forward TPUs to VMs in different projects: https://twitter.com/theshawwn/status/1221241517626445826
Is there an official specification clarifying this somewhere?
So you are saying the system memory is 300GB and you can train your model on the cpu instead? Well yeah you can always do that but training will be slow because your model is not trained on the GPU. What’s the point?
If that were the case I am wondering why anyone would buy GPUs? I invite you to retrain a state of the art model of your choice on a CPU and see how far you get.
