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Except that cloud-ified V100s are significantly less powerful than if you have direct access to the hardware. Last time I checked, in AWS they're actually exter
by fxtentacle 6y ago
Except that cloud-ified V100s are significantly less powerful than if you have direct access to the hardware. Last time I checked, in AWS they're actually external devices mapped in over GBit ethernet, which is significantly slower than the 8GB/s that PCIe x4 has.
- p1esk 6y agoI routinely switch between AWS 8x V100 instances and on-premise 8x V100 servers and I observe no difference in speed (time per epoch).
- Reelin 6y agoPresumably that depends on maximum PCIe bandwidth consumption before your workload bottlenecks elsewhere? A 2018 benchmark (https://www.pugetsystems.com/labs/hpc/PCIe-X16-vs-X8-with-4-x-Titan-V-GPUs-for-Machine-Learning-1167/ https://www.pugetsystems.com/labs/hpc/PCIe-X16-vs-X8-with-4-...) seems to indicate that x8 isn't generally a bottleneck for common (at the time) workloads. x8 is a far cry from the claimed gigabit ethernet though!
- p1esk 6y agoAWS is tricky in terms of how storage is provisioned - I don't remember details, but it's easy to put your datasets on storage that is connected to your GPU servers over 1Gb link. That could easily become a bottleneck. Datasets should live on Elastic Block Storage or something like that, over high speed links. Again, it's been a while since I looked into that, so I don't remember the details.
- Reelin 6y agoThe earlier comment claimed that the GPUs (!!!) were located elsewhere on the network; I suspect that the scenario you describe is what they intended to refer to. (IIRC AWS offers compute optimized instances with a volume that's guaranteed to be backed by blocks on a local NVMe drive.)
- nl 6y agoI think they are confused with AWS Elastic Inference. That is a different thing which does have network attached accelerators: Amazon Elastic inference accelerators are GPU-powered hardware devices that are designed to work with any EC2 instance, Sagemaker instance, or ECS task to accelerate deep learning inference workloads at a low cost. When you launch an EC2 instance or an ECS task with Amazon Elastic Inference, an accelerator is provisioned and attached to the instance over the network. https://aws.amazon.com/machine-learning/elastic-inference/faqs/ https://aws.amazon.com/machine-learning/elastic-inference/fa...
- marcinzm 6y agoCitation? AWS GPU instances are cpu+gpu+memory in fixed ratio packages so I don't see why it'd be over ethernet.
- nl 6y agoThis isn't true. AWS V100s are standard V100s. I think you are confusing this with AWS Elastic Inference. If you use AWS Elastic Inference, then you get networked attached devices. But these are Amazon's own (non-NVidia) devices and only used for inference, so it's not really comparable. https://aws.amazon.com/machine-learning/elastic-inference/faqs/ https://aws.amazon.com/machine-learning/elastic-inference/fa...