Deploy yolov5 with fastapi

[sctnightcore]

I try deploy yolov5 with fastapi.
if i send muti requests(14000+ ) to api. it slow response.

i have no idea why .

        device = torch.device('cuda:0')
        model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
        model.autoshape()
        model.to(device).eval()

        image_base64 = item.image
        image = base64.b64decode(image_base64)
        image = Image.open(BytesIO(image)).convert('RGB')

        start_time = time_synchronized()
        results = model(image, size=640)
        results_json = json.loads(results.pandas().xyxy[0].to_json(orient="records"))
        captcha_array = sorted(results_json, key=lambda k: [k['xmin']])
        captcha_array = "".join(([d['name'] for d in captcha_array]))
        end_time = time_synchronized()

[glenn-jocher]

@sctnightcore 👋 Hello, thank you for your interest in our work! This issue seems to lack the minimum requirements for a proper response, or is insufficiently detailed for us to help you. Please note that most technical problems are due to:

• Your modified or out-of-date code. If your issue is not reproducible in a new git clone version of this repo we can not debug it. Before going further run this code and verify your issue persists:

$ git clone https://github.com/ultralytics/yolov5 yolov5_new  # clone latest
$ cd yolov5_new
$ python detect.py  # verify detection

# CODE TO REPRODUCE YOUR ISSUE HERE

• Your custom data. If your issue is not reproducible in one of our 3 common datasets (COCO, COCO128, or VOC) we can not debug it. Visit our Custom Training Tutorial for guidelines on training your custom data. Examine train_batch0.jpg and test_batch0.jpg for a sanity check of your labels and images.

• Your environment. If your issue is not reproducible in one of the verified environments below we can not debug it. If you are running YOLOv5 locally, verify your environment meets all of the requirements.txt dependencies specified below. If in doubt, download Python 3.8.0 from https://www.python.org/, create a new venv, and install requirements.

If none of these apply to you, we suggest you close this issue and raise a new one using the 🐛 Bug Report template, providing screenshots and a minimum reproducible example of your issue. Thank you!

Requirements

Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:

$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

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