YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
neuralmagic.com
neuralmagic.com
To get your money's worth, you must saturate the processor. It's easy, in practice, with an AVX-512 CPU (e.g., see https://NN-512.com or Fabrice Bellard's https://bellard.org/libnc), and almost impossible with a GPU.
A GPU cloud instance costs $1000+ per month (vs. $10 per month for an AVX-512 CPU). A bargain GPU instance (e.g., Linode) costs $1.50 per hour (and far more on AWS) but an AVX-512 CPU costs maybe $0.02 per hour.
That said, GPUs are essential for training.
Note that Neural Magic's engine is completely closed-source, and their python library communicates with the Neural Magic servers. If you use their engine, your project survives only as long as Neural Magic's business survives.
Could be totally off here, but have a feeling this team is going to get scooped up by one of the big co's. Having deja vu of XNOR.ai (purchased by Apple), when partners got the short end of the stick (see Wyze cam saga: https://www.theverge.com/2019/11/27/20985527/wyze-person-det...)
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.
GCP not AWS, or are you talking about a different OP?
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).
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
You use the expensive GPU for building the AI, then dumb it down for mass-deployment on cheap CPUs.
http://learningsys.org/neurips19/assets/papers/18_CameraRead...
> Even without sparsification, AVX-512 CPUs are far more cost-effective for inference.
> A GPU cloud instance costs $1000+ per month (vs. $10 per month for an AVX-512 CPU). A bargain GPU instance (e.g., Linode) costs $1.50 per hour (and far more on AWS) but an AVX-512 CPU costs maybe $0.02 per hour.
This is a little confused. A T4 on GCP is $.35/hr at on-demand rates. A single thread of a Skylake+ CPU is in the 2-3c/hr range [2] (so $15-20/month w/o any committed or sustained use discounts. Your $10 is close enough).
So roughly the T4 GPU itself is ~10 threads. Both of these are before adding memory, storage, etc., but the T4 is a great inference part and hard to beat.
Comparing a single thread of a CPU to training-optimized GPU parts (like the A100 or V100) is sort of apples and oranges.
If you're not holding it for a full month, it's $0.015 per hour.
Here are the $1000 per month ($1.50 per hour) cloud GPUs I use, the cheapest that Linode provides: https://www.linode.com/pricing/
I would like to see a comparison of Linode vs Google Cloud, taking into account network bandwidth costs, etc. Maybe the Google Cloud T4's total cost of ownership is lower? I doubt it: for example, the Linode plan includes 16 terabytes of fast network traffic at no additional cost, and Google Compute charges $0.085 per gigabyte.
And the T4 is still 23x more expensive than an AVX-512 core, hourly.
EDIT: please stop it with the ninja edits.
Hi 37ef_ced3, AVX-512 has certainly helped close the gap for CPUs vs GPUs. Even then, though, most inference engines on CPUs are still very compute bound for networks. Unstructured sparsity enables us to cut the compute requirements down for CPUs leading to most layers becoming memory bound. This then allows us to focus on the unique cache architectures within the CPUs to remove the memory bottlenecks through proprietary techniques. The combination is what's truly unique for Neural Magic and enables the DeepSparse engine to compete with GPUs while leveraging the flexibility and deployability of highly available CPU servers.
Also as boulos said, the numbers quoted here are a bit lopsided. There are more affordable GPUs and GCP has enabled some very cost effective T4 instances. Our performance blog on YOLOv3 walks through a direct comparison to T4s and V100s on GCP for performance and cost: https://neuralmagic.com/blog/benchmark-yolov3-on-cpus-with-d...
Additionally, I'd like to clarify that our open sourced products do not communicate with any of our servers currently. These are stand alone products that do not require Neural Magic to be in the loop for use.
If you see here (https://github.com/ultralytics/yolov5/blob/master/README.md), the speed of inference on a V100 for YOLOv5s should be 2 ms per image, or 500 imgs/s, not the 44.6 img/s being reported here.
This is important as it is more than an order of magnitude off.
For inference workloads you usually batch incoming requests together and run once on GPU (though this increases latency). A latency/throughput tradeoff curve at different batch sizes would tell the whole story.
Also, they are using INT8 on CPU and neglect to measure the same on GPU. All the GPU throughputs would 2x.
tl;dr just use GPUs
Edit: to the comments below, I agree low-latency can be important to some workloads, but that's exactly why I think we need to see a latency-throughput tradeoff curve.
Unfortunately, I'm pretty sure that modern GPUs (A10/A30/A40/A100) basically dominate CPUs even when latency is constrained, and the MLPerf results give a good (fair!) comparison of this:
https://developer.nvidia.com/blog/extending-nvidia-performan...
The GPU throughputs are much, much higher than the CPU ones, and I don't think even NM's software can overcome this gap. Not to mention they degrade the model quality...
The last question is whether CPUs are more cost-effective despite being slower, and the answer is still... no. The instances used in this blog post cost:
- C5 CPU (c5.12xlarge): $2.04/hr - T4 GPU (g4dn.2xlarge): $0.752/hr
NM's best result @ batch-size-1 costs more, at lower throughput, at lower model quality, at ~same latency, than a last-gen GPU operating at half it's capacity. A new A10 GPU using INT8 will widen the perf/$ gap by another ~4x.
Also full disclosure I don't work at NVIDIA or anything like that so I'm not trying to shill :) I just like ML hardware a lot and want to help people make fair comparisons.
Also, your CPU cost numbers are way off, using an expensive provider like AWS instead of, say, Vultr (https://www.vultr.com)
And 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).
I can agree with you that on super, super small inference deployments, maybe you can lower monthly spend by using CPUs. But i must ask.. who is the target customer that is both spending <$100 / month and also trying to optimize this? I feel like big players will have big workloads that will be most cost-effective on GPUs.
[1]: https://docs.nvidia.com/deeplearning/performance/mixed-preci...
Hi ml_hardware, we report results for both throughput and latency in the blog. As you noted, the throughput performance for GPUs does beat out our implementations by a bit, but we did improve the throughput performance on CPUs by over 10x. Our goal is to enable better flexibility and performance for deployments through more commonly available CPU servers.
For throughput costs, this flexibility becomes essential. The user could scale down to even one core if they wanted to, with a much more significant increase in the cost performance. We walk through these comparisons in more depth in our YOLOv3 blog: https://neuralmagic.com/blog/benchmark-yolov3-on-cpus-with-d...
INT8 wasn't run on GPUs because we have issues with operator support on the conversion from PyTorch graphs to TensorRT (PyTorch currently doesn't have support for INT8 on GPU). We are actively working on this, though, so stay tuned as we run those comparisons!
The models we're shipping will see performance gains on the A100s, as well, due to their support for semi-structured sparsity. Note, though, A100s are priced more expensive than the commonly available V100s and T4s, which will need to be considered. We generally keep our current benchmarks limited to what is available in the top cloud services to represent what is deployable for most people on servers. This usability is why we don't consider ML Perf a great source for most users. ML Perf has done a great job in standardizing benchmarking and improving numbers across the industry. Still, the systems submitted are hyper-engineered for ML Perf numbers, and most customers cannot realize these numbers due to the cost involved.
Finally, note that the post-processing for these networks is currently limited to CPUs due to operator support. This limitation will become a bottleneck for most deployments (it already is for GPUs and us for the YOLOv5s numbers). We are actively working on speeding up the post-processing by leveraging the cache hierarchy in the CPUs through the DeepSparse engine, and are seeing promising early results. We'll be releasing those sometime soon in the future to show even better comparisons.
Hi deepnotderp, as noted by others the speeds listed here are combining throughput for GPU from Ultralytics to latency for GPU from Neural Magic. We did also include throughput measurements, though, where YOLOv5s was around 3 ms per image on a V100 at fp16 in our testing. All benchmarks were run on AWS instances for repeatability and availability and is likely where the 2 ms vs 3 ms discrepancy comes from (slower memory transfer on the AWS machine vs the one Ultralytics used). Note, though, a slower overall machine will also affect CPU results as well.
We benchmarked using the available PyTorch APIs mimicking what was done for Ultralytics benchmarking. This code is open sourced for viewing and use here: https://github.com/neuralmagic/deepsparse/blob/main/examples...
A good structurally sparse model can usually be carefully pruned as well, but the gains are a bit smaller once you've already settled into a 'small enough' model architecture.
Edit: it's in C++ and has rust bindings [2]
It'll be exciting to see what they can do with the a10 gpus when they're available.
It looks like they used torch.load instead of torch.jit.load so I don’t think it’s scripted here, but I didn’t check the model file itself so I’m not entirely sure
It's great to see CPU optimizations, but I have a few nits:
> As of this writing, the new A100s do have hardware support for semi-structured sparsity but are not readily available.
A100s have been readily available on all major cloud providers for a while. This benchmark would only require one A100, anywhere in the world.
Echoing 37ef_ced3's, I would argue that what matters is not raw qps (the A100 will trounce this) but rather qps at a given latency per dollar. The c5.12xlarge is $2.04/hr (and has a sad 12 Gbps NIC...) while the g4dn.2xlarge is is $.752/hr.
So the T4 setup is about 20% more qps, but almost 3x better inference/$!
"Based on the amount of work for support, it's on our medium to long-term roadmap currently."
> Open sourcing of the backend runtime is something we're not planning for the near to medium term. It is something we're actively reevaluating, though, as the company and the industry as a whole grows to see what works best for the needs of our users.
CPUs keep throwing specialised hardware into the garbage bag every time few years after mass adoption of something big, as the computer science improves.
People were once telling that real time audio was physically impossible to do on the CPU, now you can do it even on a smartphone CPU.