4 ms·

That's my point. Competitors are largely moated out by high costs of TPU production and proprietary drivers.

puzzle 9y ago
Why are proprietary drivers a blocker? As long as you expose the same gRPC interface, your customers don't need to know what happens behind the scenes. You could have an FPGA or a Beowulf cluster of Raspberry Pis hiding.
- deleted 9y ago
  [deleted]
- whataretensors 9y ago
  I should clarify. I like all the individual pieces(hardware, cloud services, grpc interface) I just wish you could opt into them independently.
dgacmu 9y ago
Amazon is reportedly looking into building their own. Nvidia not only added Tensor cores to the Volta series (impressively quickly, might I add), but they're also creating the NVidia GPU Cloud. Intel has been acquiring DNN hardware startups left and right (Nervana, Movidius, MobilEye) and trying to roll those into their production series. The hardest part in DNNs is the model and data. That's basically platform-independent. My students mix and match TensorFlow and Caffe, for example, on several different models. The next part is getting the model implemented in a framework (TensorFlow? Caffe? MXNet? PyTorch?). That's work to change, particularly if you're in a production environment. But it's not the same amount of work as collecting data and building a model. The final part is running training - CPUs, GPUs, TPUs, etc. This is really fungible. The platform-specific optimizations are relatively small here. Looking at it from a customer perspective: - Can a trained model be exported (weights included) for use on another platform? (yes) - Can the code written for training be used on the customer's own hardware? (yes, absent any small tweaks needed for TPU, but they're *small*). - Might the customer not want to leave because of ease-of-use, particularly at scale, or performance, or total cost of ownership? (yes, and I think that's what the sales pitch is). (disclaimer: I worked on part of the Cloud TPU stuff. I'm funded academically by Intel. I have friends at NVidia and own a lot of their GPUs. I love everyone. :)

Why are proprietary drivers a blocker? As long as you expose the same gRPC interface, your customers don't need to know what happens behind the scenes. You could have an FPGA or a Beowulf cluster of Raspberry Pis hiding.

deleted 9y ago
[deleted]
whataretensors 9y ago
I should clarify. I like all the individual pieces(hardware, cloud services, grpc interface) I just wish you could opt into them independently.

dgacmu 9y ago

Amazon is reportedly looking into building their own. Nvidia not only added Tensor cores to the Volta series (impressively quickly, might I add), but they're also creating the NVidia GPU Cloud. Intel has been acquiring DNN hardware startups left and right (Nervana, Movidius, MobilEye) and trying to roll those into their production series. The hardest part in DNNs is the model and data. That's basically platform-independent. My students mix and match TensorFlow and Caffe, for example, on several different models. The next part is getting the model implemented in a framework (TensorFlow? Caffe? MXNet? PyTorch?). That's work to change, particularly if you're in a production environment. But it's not the same amount of work as collecting data and building a model. The final part is running training - CPUs, GPUs, TPUs, etc. This is really fungible. The platform-specific optimizations are relatively small here. Looking at it from a customer perspective: - Can a trained model be exported (weights included) for use on another platform? (yes) - Can the code written for training be used on the customer's own hardware? (yes, absent any small tweaks needed for TPU, but they're *small*). - Might the customer not want to leave because of ease-of-use, particularly at scale, or performance, or total cost of ownership? (yes, and I think that's what the sales pitch is). (disclaimer: I worked on part of the Cloud TPU stuff. I'm funded academically by Intel. I have friends at NVidia and own a lot of their GPUs. I love everyone. :)