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What a pity Google TPUs are locked in Google Cloud - many would like to use them on their own, in already owned servers. It kind of slows the ML progress, such

What a pity Google TPUs are locked in Google Cloud - many would like to use them on their own, in already owned servers. It kind of slows the ML progress, such restriction of access to the product.

zak 8y ago
You can also use Cloud TPUs for free in your browser via Colab. Several sample notebooks are available here: https://www.tensorflow.org/tfrc/ https://www.tensorflow.org/tfrc/
- api 8y ago
  I think they mean actually buying the chips. This model of putting revolutionary new hardware in the cloud only is kind of worrisome given the future implications.
  - eanzenberg 8y ago
    "Revolutionary"
kajecounterhack 8y ago
How these chips work is pretty reliant on Google's specific infrastructure and there's a stark difference between the work needed to offer a service and selling chips. It isn't a matter of bad will or trying to slow ML progress but a matter of what's practical.
- polskibus 8y ago
  Maybe Google could just license the tech to parties that would be interested in doing the hardware for the masses? Or maybe we just have to wait to see what Nvidia is brewing?
- ori_b 8y ago
  > How these chips work is pretty reliant on Google's specific infrastructure. I find that hard to believe. Can you be more specific?
  - lawrenceyan 8y ago
    At Berkeley, we had a guest lecture where one of the major contributors in developing Google’s cloud infrastructure gave a talk on building scalability and robustness into their systems. The majority of the public facing products/services that you find on GCP actually started much earlier as internal tools for employees. There is a huge amount of work that is put into ensuring that everything works reliably, with significant underlying infrastructure needed to achieve it. The problems you face at the level of scale Google works in on a day to day basis turns even the most mundane tasks into extraordinarily difficult algorithmic challenges that have to be solved.
    - ori_b 8y ago
      ...er. So, by that reasoning, there is nothing that can exist outside of Google, because Google does things internally at scale.
      - lawrenceyan 8y ago
        I think the point is that a lot of the products and services wouldn't work outside the Google Cloud platform without a significant amount of underlying infrastructure to support it, such that it would be very difficult to just provide some ready to go package for people to buy and implement themselves.
        ori_b 8y ago
        Which is largely bullshit. There's complexity in managing failures at scale -- but strip away the scale, and you strip away the complexity in managing failures. You just get normal, bog standard server-grade hardware, with the usual downtime.
- psds2 8y ago
  Agreed. TPU will come on premise when Google launches a similar offering to AWS Outposts and ships you an appliance.
thesandlord 8y ago
Probably not what you are looking for, but: https://cloud.google.com/edge-tpu/ https://cloud.google.com/edge-tpu/ If you look at an image of a Cloud TPU board [0], I'm not sure how it would even work with off the shelf servers. It's 100% custom for a Google Datacenter. Nvidia also has the full supply chain set up, as well as things like driver support and all the things required to sell this kind of complex physical hardware. Google also has a program to give research scientists free access to TPUs: https://www.tensorflow.org/tfrc/ https://www.tensorflow.org/tfrc/ [0] https://storage.googleapis.com/gweb-uniblog-publish-prod/images/tpu-V2-hero.max-1000x1000.png https://storage.googleapis.com/gweb-uniblog-publish-prod/ima... (I work on GCP, but not on ML and TPU stuff)
- deleted 8y ago
  [deleted]

What a pity Google TPUs are locked in Google Cloud - many would like to use them on their own, in already owned servers. It kind of slows the ML progress, such restriction of access to the product.

zak 8y ago

You can also use Cloud TPUs for free in your browser via Colab. Several sample notebooks are available here: https://www.tensorflow.org/tfrc/ https://www.tensorflow.org/tfrc/

api 8y ago
I think they mean actually buying the chips. This model of putting revolutionary new hardware in the cloud only is kind of worrisome given the future implications.
- eanzenberg 8y ago
  "Revolutionary"

kajecounterhack 8y ago

How these chips work is pretty reliant on Google's specific infrastructure and there's a stark difference between the work needed to offer a service and selling chips. It isn't a matter of bad will or trying to slow ML progress but a matter of what's practical.

polskibus 8y ago
Maybe Google could just license the tech to parties that would be interested in doing the hardware for the masses? Or maybe we just have to wait to see what Nvidia is brewing?
ori_b 8y ago
> How these chips work is pretty reliant on Google's specific infrastructure. I find that hard to believe. Can you be more specific?
- lawrenceyan 8y ago
  At Berkeley, we had a guest lecture where one of the major contributors in developing Google’s cloud infrastructure gave a talk on building scalability and robustness into their systems. The majority of the public facing products/services that you find on GCP actually started much earlier as internal tools for employees. There is a huge amount of work that is put into ensuring that everything works reliably, with significant underlying infrastructure needed to achieve it. The problems you face at the level of scale Google works in on a day to day basis turns even the most mundane tasks into extraordinarily difficult algorithmic challenges that have to be solved.
  - ori_b 8y ago
    ...er. So, by that reasoning, there is nothing that can exist outside of Google, because Google does things internally at scale.
    - lawrenceyan 8y ago
      I think the point is that a lot of the products and services wouldn't work outside the Google Cloud platform without a significant amount of underlying infrastructure to support it, such that it would be very difficult to just provide some ready to go package for people to buy and implement themselves.
      - ori_b 8y ago
        Which is largely bullshit. There's complexity in managing failures at scale -- but strip away the scale, and you strip away the complexity in managing failures. You just get normal, bog standard server-grade hardware, with the usual downtime.
psds2 8y ago
Agreed. TPU will come on premise when Google launches a similar offering to AWS Outposts and ships you an appliance.

thesandlord 8y ago

Probably not what you are looking for, but: https://cloud.google.com/edge-tpu/ https://cloud.google.com/edge-tpu/ If you look at an image of a Cloud TPU board [0], I'm not sure how it would even work with off the shelf servers. It's 100% custom for a Google Datacenter. Nvidia also has the full supply chain set up, as well as things like driver support and all the things required to sell this kind of complex physical hardware. Google also has a program to give research scientists free access to TPUs: https://www.tensorflow.org/tfrc/ https://www.tensorflow.org/tfrc/ [0] https://storage.googleapis.com/gweb-uniblog-publish-prod/images/tpu-V2-hero.max-1000x1000.png https://storage.googleapis.com/gweb-uniblog-publish-prod/ima... (I work on GCP, but not on ML and TPU stuff)

deleted 8y ago
[deleted]