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Pardon the n00b question, but I'm near the bottom of the learning curve on this. It looks like this runs on GPU-less instances, as well as Gx-instances. So, h
by dbcurtis 10y ago
Pardon the n00b question, but I'm near the bottom of the learning curve on this.
It looks like this runs on GPU-less instances, as well as Gx-instances. So, how do you envision this being used? Would someone do prototyping on the cheap instances and then move up to the Gx instances for production? Is that move transparent?
- mbajkowski 10y agoYou could do precisely that. Get started on a small instance, play around with one of the frameworks (one of the reasons why we also integrated Jupyter as part of the AMIs so the you can quickly write some python code from the browser without having to ssh into the instance). And then when all checks out, migrate the image (by creating a snapshot) and booting it on a more powerful instance. For TensorFlow if an operation has both CPU and GPU implementations, the GPU devices will be given priority (if present on the instance) when the operation is assigned to a device. For Caffe we have both the GPU and CPU version installed.
- nl 10y agoOr train on the GPU and run inference on the CPU instances.