Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by ml_hardware on Friday September 10, 2021 @05:07PM
(#28486900)
T4 is a gpu :) NVIDIA Tesla T4: https://www.nvidia.com/en-us/data-center/tesla-t4/
Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by carbocation on Saturday September 11, 2021 @08:58AM
(#28491620)
Yes, I’m using “T4” as a shorthand for “instances otherwise matched to the CPU-based instances but which also have a T4 GPU.”
Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by mkaic on Friday September 10, 2021 @07:15PM
(#28487926)
OP was most likely referring to the AWS EC2 instance type T4, which runs on Amazon Graviton processors IIRC.
Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by mkl on Friday September 10, 2021 @08:06PM
(#28488238)
> GCP lists a T4
GCP not AWS, or are you talking about a different OP?
Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by 37ef_ced3 on Friday September 10, 2021 @07:04PM
(#28487849)
Many businesses/services can't saturate the hardware you describe. It's just too much compute power. With CPUs you can scale down to fit your actual needs: all the way down to a single AVX-512 core doing maybe 24 inferences per second (costing a few dollars PER MONTH).
Also, your cost/inference results will change if you use a fast CPU inference engine, instead of something slow like PyTorch (which you appear to be using).
Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by carbocation on Saturday September 11, 2021 @09:01AM
(#28491641)
Thanks - this is something I wasn’t familiar with. Do you have any pointers for CPU inference engines that you’ve had good experience with or that I can look into further?
Re:YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
by markurtz on Saturday September 11, 2021 @09:27AM
(#28491805)
Disclosure: I work for Neural Magic.
Hi carbocation, we'd love to see what you think of the performance using the DeepSparse engine for CPU inference: https://github.com/neuralmagic/deepsparse
Take a look through our getting started pages that walk through performance benchmarking, training, and deployment for our featured models: https://sparsezoo.neuralmagic.com/getting-started
On 26 million images using a pytorch model that had 41 million parameters, T4 instances were about 32% cheaper than CPU instances, and took about 45% of the time even after accounting for extra GPU startup time.