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I'm one of the devs for some of the AWS AMIs mentioned a few comments below which have the frameworks and examples installed, and run on CPU as well as GPU inst
by mbajkowski 10y ago
I'm one of the devs for some of the AWS AMIs mentioned a few comments below which have the frameworks and examples installed, and run on CPU as well as GPU instances. We have several AMIs including one for TensorFlow:
https://aws.amazon.com/marketplace/pp/B01EYKBEQ0/ref=_ptnr_hn https://aws.amazon.com/marketplace/pp/B01EYKBEQ0/ref=_ptnr_h...
Would love to get some feedback from anyone who gives them a spin about what we could do better - or which AMIs we should add that people may find useful.
If you are not familiar with AWS, we have quick-start blog here as well:
http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/ http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-tra...
- dbcurtis 10y agoPardon 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.