January 5, 2023
Empower Ultralytics YOLOv5 model training & deployment with Neural Magic's DeepSparse for GPU-class performance on CPUs. Achieve faster, scalable YOLOv5 deployments.
Want to accelerate the training and deployment of your YOLOv5 models? We’ve got you covered! Introducing our newest partner, Neural Magic. As Neural Magic provides software tools that emphasize peak model performance and workflow simplicity, it’s only natural that we’ve come together to offer a solution to make the YOLOv5 deployment process even better.
DeepSparse is Neural Magic’s CPU inference runtime, which takes advantage of sparsity and low-precision arithmetic within neural networks to offer exceptional performance on commodity hardware. For instance, compared to the ONNX Runtime baseline, DeepSparse offers a 5.8x speed-up for YOLOv5s running on the same machine!
For the first time, your deep learning workloads can meet the performance demands of production without the complexity and costs of hardware accelerators. Put simply, DeepSparse gives you the performance of GPUs and the simplicity of software:
DeepSparse takes advantage of model sparsity to gain its performance speedup.
Sparsification through pruning and quantization allows order-of-magnitude reductions in the size and compute needed to execute a network while maintaining high accuracy. DeepSparse is sparsity-aware, skipping the multiply-adds by zero and shrinking the amount of compute in a forward pass. Since sparse computation is memory-bound, DeepSparse executes the network depth-wise, breaking the problem into Tensor Columns, which are vertical stripes of computation that fit in cache.
Sparse networks with compressed computation, executed depth-wise in cache, allow DeepSparse to deliver GPU-class performance on CPUs!
Neural Magic's open-source model repository, SparseZoo, contains pre-sparsified checkpoints of each YOLOv5 model. Using SparseML, which is integrated with Ultralytics, you can fine-tune a sparse checkpoint onto your data with a single CLI command.
Run the following to install DeepSparse. We recommend you use a virtual environment with Python.
pip install deepsparse[server,yolo,onnxruntime]
DeepSparse accepts a model in the ONNX format, passed either as:
We will compare the standard dense YOLOv5s to the pruned-quantized YOLOv5s, identified by the following SparseZoo stubs:
zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none
zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none
DeepSparse offers convenient APIs for integrating your model into an application.
To try the deployment examples below, pull down a sample image for the example and save as basilica.jpg with the following command:
wget -O basilica.jpg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolo/sample_images/basilica.jpg
Pipelines wrap pre-processing and output post-processing around the runtime, providing a clean interface for adding DeepSparse to an application. The DeepSparse-Ultralytics integration includes an out-of-the-box Pipeline that accepts raw images and outputs the bounding boxes.
Create a Pipeline and run inference:
from deepsparse import Pipeline
# list of images in local filesystem
images = ["basilica.jpg"]
# create Pipeline
model_stub = "zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none"
yolo_pipeline = Pipeline.create(
task="yolo",
model_path=model_stub,
)
# run inference on images, receive bounding boxes + classes
pipeline_outputs = yolo_pipeline(images=images, iou_thres=0.6, conf_thres=0.001)
print(pipeline_outputs)
If you are running in the cloud, you may get an error that open-cv cannot find libGL.so.1. Running the following on Ubuntu installs it:
apt-get install libgl1-mesa-glx
DeepSparse Server runs on top of the popular FastAPI web framework and Uvicorn web server. With just a single CLI command, you can easily set up a model service endpoint with DeepSparse. The Server supports any Pipeline from DeepSparse, including object detection with YOLOv5, enabling you to send raw images to the endpoint and receive the bounding boxes.
Spin up the Server with the pruned-quantized YOLOv5s:
deepsparse.server \
--task yolo \
--model_path zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none
An example request, using Python's requests package:
import requests, json
# list of images for inference (local files on client side)
path = ['basilica.jpg']
files = [('request', open(img, 'rb')) for img in path]
# send request over HTTP to /predict/from_files endpoint
url = 'http://0.0.0.0:5543/predict/from_files'
resp = requests.post(url=url, files=files)
# response is returned in JSON
annotations = json.loads(resp.text) # dictionary of annotation results
bounding_boxes = annotations["boxes"]
labels = annotations["labels"]
You can also use the annotate command to have the engine save an annotated photo on disk. Try --source 0 to annotate your live webcam feed!
deepsparse.object_detection.annotate --model_filepath zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none --source basilica.jpg
Running the above command will create an annotation-results folder and save the annotated image inside.
Using DeepSparse's benchmarking script, we will compare DeepSparse's throughput to ONNX Runtime's throughput on YOLOv5s.
The benchmarks were run on an AWS c6i.8xlarge instance (16 cores).
At batch 32, ONNX Runtime achieves 42 images/sec with the standard dense YOLOv5s:
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none -s sync -b 32 -nstreams 1 -e onnxruntime
> Original Model Path: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none
> Batch Size: 32
> Scenario: sync
> Throughput (items/sec): 41.9025
While DeepSparse offers its best performance with optimized sparse models, it also performs well with the standard dense YOLOv5s.
At batch 32, DeepSparse achieves 70 images/sec with the standard dense YOLOv5s—a 1.7x performance improvement over ORT!
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none -s sync -b 32 -nstreams 1
> Original Model Path: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none
> Batch Size: 32
> Scenario: sync
> Throughput (items/sec): 69.5546
When sparsity is applied to the model, DeepSparse's performance gains over ONNX Runtime are even stronger.
At batch 32, DeepSparse achieves 241 images/sec with the pruned-quantized YOLOv5s—a 5.8x performance improvement over ORT!
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none -s sync -b 32 -nstreams 1
> Original Model Path: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none
> Batch Size: 32
> Scenario: sync
> Throughput (items/sec): 241.2452
DeepSparse is also able to gain a speed-up over ONNX Runtime for the latency-sensitive, batch 1 scenario.
At batch 1, ONNX Runtime achieves 48 images/sec with the standard, dense YOLOv5s.
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none -s sync -b 1 -nstreams 1 -e onnxruntime
> Original Model Path: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/base-none
> Batch Size: 1
> Scenario: sync
> Throughput (items/sec): 48.0921
When sparsity is applied to the model, DeepSparse's performance gains over ONNX Runtime are even stronger.
At batch 1, DeepSparse achieves 135 images/sec with the pruned-quantized YOLOv5s—a 2.8x performance improvement over ONNX Runtime!
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none -s sync -b 32 -nstreams 1
> Original Model Path: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none
> Batch Size: 1
> Scenario: sync
> Throughput (items/sec): 134.9468
Since c6i.8xlarge instances have VNNI instructions, DeepSparse's throughput can be pushed further if weights are pruned in blocks of 4.
At batch 1, DeepSparse achieves 180 items/sec with a 4-block pruned-quantized YOLOv5s—a 3.7x performance gain over ONNX Runtime!
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned35_quant-none-vnni -s sync -b 1 -nstreams 1
> Original Model Path: zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned35_quant-none-vnni
> Batch Size: 1
> Scenario: sync
> Throughput (items/sec): 179.7375
And voila! You’re ready to optimize your YOLOv5 deployment with DeepSparse.
To get in touch with us, join our community and leave us your questions and comments. Check out the Ultralytics YOLOv5 repository and full Neural Magic documentation for deploying YOLOv5.
At Ultralytics, we commercially partner with other startups to help us fund the research and development of our awesome open-source tools, like YOLOv5, to keep them free for everybody. This article may contain affiliate links to those partners.
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