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| comments | description | keywords |
|---|---|---|
| true | Learn how to deploy YOLOv5 with DeepSparse to achieve exceptional CPU performance close to GPUs, using pruning, and quantization.<br> | YOLOv5, DeepSparse, Neural Magic, CPU, Production, Performance, Deployments, APIs, SparseZoo, Ultralytics, Model Sparsity, Inference, Open-source, ONNX, Server |
Welcome to software-delivered AI.
This guide explains how to deploy YOLOv5 with Neural Magic's DeepSparse.
DeepSparse is an inference runtime with exceptional performance on CPUs. 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:
- Flexible Deployments: Run consistently across cloud, data center, and edge with any hardware provider from Intel to AMD to ARM
- Infinite Scalability: Scale vertically to 100s of cores, out with standard Kubernetes, or fully-abstracted with Serverless
- Easy Integration: Clean APIs for integrating your model into an application and monitoring it in production
How Does DeepSparse Achieve GPU-Class Performance?
DeepSparse takes advantage of model sparsity to gain its performance speedup.
Sparsification through pruning and quantization is a broadly studied technique, allowing order-of-magnitude reductions in the size and compute needed to execute a network, while maintaining high accuracy. DeepSparse is sparsity-aware, meaning it skips the zeroed out parameters, shrinking amount of compute in a forward pass. Since the sparse computation is now memory bound, DeepSparse executes the network depth-wise, breaking the problem into Tensor Columns, vertical stripes of computation that fit in cache.
Sparse networks with compressed computation, executed depth-wise in cache, allows DeepSparse to deliver GPU-class performance on CPUs!
How Do I Create A Sparse Version of YOLOv5 Trained on My Data?
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.
Checkout Neural Magic's YOLOv5 documentation for more details.
DeepSparse Usage
We will walk through an example benchmarking and deploying a sparse version of YOLOv5s with DeepSparse.
Install DeepSparse
Run the following to install DeepSparse. We recommend you use a virtual environment with Python.
pip install deepsparse[server,yolo,onnxruntime]
Collect an ONNX File
DeepSparse accepts a model in the ONNX format, passed either as:
- A SparseZoo stub which identifies an ONNX file in the SparseZoo
- A local path to an ONNX model in a filesystem
The examples below use the standard dense and pruned-quantized YOLOv5s checkpoints, 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
Deploy a Model
DeepSparse offers convenient APIs for integrating your model into an application.
To try the deployment examples below, pull down a sample image and save it as basilica.jpg with the following:
wget -O basilica.jpg https://raw.githubusercontent.com/neuralmagic/deepsparse/main/src/deepsparse/yolo/sample_images/basilica.jpg
Python API
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
HTTP Server
DeepSparse Server runs on top of the popular FastAPI web framework and Uvicorn web server. With just a single CLI command, you can easily setup 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"]
Annotate CLI
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.
Benchmarking Performance
We will compare DeepSparse's throughput to ONNX Runtime's throughput on YOLOv5s, using DeepSparse's benchmarking script.
The benchmarks were run on an AWS c6i.8xlarge instance (16 cores).
Batch 32 Performance Comparison
ONNX Runtime Baseline
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
DeepSparse Dense Performance
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
DeepSparse Sparse Performance
When sparsity is applied to the model, DeepSparse's performance gains over ONNX Runtime is 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
Batch 1 Performance Comparison
DeepSparse is also able to gain a speed-up over ONNX Runtime for the latency-sensitive, batch 1 scenario.
ONNX Runtime Baseline
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
DeepSparse Sparse Performance
At batch 1, DeepSparse achieves 135 items/sec with a pruned-quantized YOLOv5s, a 2.8x performance gain over ONNX Runtime!
deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned65_quant-none -s sync -b 1 -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
Get Started With DeepSparse
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