Function Compute:Best practices for image processing

Scenarios and benefits

This section describes the benefits of GPU-accelerated instances compared with the instances with no GPU acceleration in Function Compute:

• Real-time and quasi-real-time application scenarios

  • GPU-accelerated instances can process graphics and images several times faster and push production content to users in a more efficient manner.

• Cost-prioritized image processing scenarios

  • Reserve GPU-accelerated instances based on their business requirements. This way, GPU-accelerated instances of Function Compute offer much higher cost efficiency than self-built GPU clusters.

  • Use GPU resources in 1/2 or exclusive mode through the GPU virtualization technology. This way, GPU-accelerated instances can be configured in a fine-grained manner.

• Efficiency-prioritized image processing scenarios

  • Focus on code development and the business objectives without the need to perform O&M on GPU clusters, such as driver and CUDA version management, machine operation management, and faulty GPU management.

For more information about GPU-accelerated instances, see Instance types and instance modes.

Tutorial description

The following table shows an example of performing edge detection on a source image (left) to generate a new image (right) in Function Compute.

Preparations

• Compile OpenCV.

  OpenCV must be compiled before you can use GPU acceleration. The following items describe how to compile OpenCV:

  • (Recommended) Use compiled OpenCV by using Docker. Download address: opencv-cuda-docker or cuda-opencv.

  • Manually compile OpenCV. For more information, see the compilation guide.

• Upload the audio and video resources that you want to process to an Object Storage Service (OSS) bucket in the region where the GPU-accelerated instances are located. Make sure that you have the read and write permissions on the objects in the bucket. For more information about how to upload audio and video resources, see Upload objects. For more information about permissions, see Modify the ACL of a bucket.

Deploy a GPU application by using Serverless Devs

Before you start

• Perform the following operations in the region where the GPU-accelerated instances reside:

  • Create a Container Registry Enterprise Edition instance or Personal Edition instance. We recommend that you create an Enterprise Edition instance. For more information, see Step 1: Create a Container Registry Enterprise Edition instance.

  • Create a namespace and an image repository. For more information, see Step 2: Create a namespace and Step 3: Create an image repository.

• Install Serverless Devs and Docker

• Configure Serverless Devs

Procedure

1. Create a project.

   s init devsapp/start-fc-custom-container-event-python3.9 -d fc-gpu-prj

   The following sample code shows the directory of the created project:

   fc-gpu-prj
   ├── code
   │   ├── app.py        # Function code.
   │   └── Dockerfile    # Dockerfile: The image Dockerfile that contains the code.
   ├── README.md
   └── s.yaml            # Project configurations, which specify how the image is deployed in Function Compute

2. Go to the project directory.

   cd fc-gpu-prj

3. Modify the configurations of the directory file based on your business requirements.

   • Edit the s.yaml file.

     For more information about the parameters in the YAML file, see YAML specifications.

     edition: 1.0.0
     name: container-demo
     access: default
     vars:
       region: cn-shenzhen
     services:
       customContainer-demo:
         component: devsapp/fc
         props:
           region: ${vars.region}
           service:
             name: tgpu_opencv_service
             internetAccess: true
           function:
             name: tgpu_opencv_func
             description: test gpu for opencv
             handler: not-used
             timeout: 600
             caPort: 9000
             instanceType: fc.gpu.tesla.1
             gpuMemorySize: 8192
             cpu: 4
             memorySize: 16384
             diskSize: 512
             runtime: custom-container
             customContainerConfig:
               #1. Make sure that the namespace:demo namespace and the repo:gpu-opencv-canny_s repository are created in advance in Alibaba Cloud Container Registry. 
               #2. Change the tag from v0.1 to v0.2 when you update the function later and run s build && s deploy again. 
               image: registry.cn-shenzhen.aliyuncs.com/demo/gpu-opencv-canny_s:v0.1
             codeUri: ./code
           triggers:
             - name: httpTrigger
               type: http
               config:
                 authType: anonymous
                 methods:
                   - GET
                                     

   • Edit the app.py file.

     Example:

     # -*- coding: utf-8 -*-
     # python2 and python3
     
     from __future__ import print_function
     from http.server import HTTPServer, BaseHTTPRequestHandler
     import json
     import sys
     import logging
     import os
     import numpy as np
     import cv2
     import urllib.request
     
     class MyRequest(BaseHTTPRequestHandler):
         def download(self, url, path):
             print("enter download:", url)
             f = urllib.request.urlopen(url)
             with open(path, "wb") as local_file:
                 local_file.write(f.read())
     
         def upload(self, url, path):
             print("enter upload:", url)
             headers = {
                 'Content-Type': 'application/octet-stream',
                 'Content-Length': os.stat(path).st_size,
             }
             req = urllib.request.Request(url, open(path, 'rb'), headers=headers, method='PUT')
             urllib.request.urlopen(req)
     
         def core(self):
             msg = ""
             mode = ""
             if not cv2.cuda.getCudaEnabledDeviceCount():
                 msg = "No CUDA-capable device is detected |"
                 mode = "Mat"
             else:
                 msg = "CUDA-capable device supported |"
                 mode = "UMat"
     
             # Use the path of the OSS object in your Alibaba Cloud account. You must have the read and write permissions on the object. 
             # Download the source image from your OSS bucket. 
             path = "/tmp/target.jpg"
             self.download("https://your_public_oss/f.png", path)
     
             img = cv2.imread(path)
     
             if mode=='UMat':
                 img = cv2.UMat(img)
             img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
             img = cv2.GaussianBlur(img, (7, 7), 1.5)
             img = cv2.Canny(img, 0, 50)
             if type(img) == 'cv2.UMat':
                 img = cv2.UMat.get(img)
             cv2.imwrite(path, img)
             # Use the path of the OSS object in your Alibaba Cloud account. You must have the read and write permissions on the object. 
             # Upload the new image after edge detection to the OSS bucket. Note that the name of the new image cannot be the same as the name of the source image. 
             self.upload("https://your_public_oss/target.jpg", path)
     
             msg = msg + " process image ok!"
             data = {'result': msg}
             self.send_response(200)
             self.send_header('Content-type', 'application/json')
             self.end_headers()
             self.wfile.write(json.dumps(data).encode())
     
         def pong(self):
             data = {"function":"opencv_edge_canny"}
             self.send_response(200)
             self.send_header('Content-type', 'application/json')
             self.end_headers()
             self.wfile.write(json.dumps(data).encode())
     
         def dispatch(self):
             mode = self.headers.get('RUN-MODE')
     
             if mode == "ping":
                 self.pong()
             elif mode == "normal":
                 self.core()
             else:
                 self.pong()
     
         def do_GET(self):
             self.dispatch()
     
         def do_POST(self):
             self.dispatch()
     
     if __name__ == '__main__':
         host = ('0.0.0.0', 9000)
         server = HTTPServer(host, MyRequest)
         print("Starting server, listen at: %s:%s" % host)
         server.serve_forever()

   • Edit the Dockerfile file.

     Example:

     FROM registry.cn-shanghai.aliyuncs.com/serverless_devs/opencv-cuda:cuda-10.2-opencv-4.2
     WORKDIR /usr/src/app
     RUN apt-get install -y build-essential
     RUN apt-get install -y python3
     COPY . .
     CMD [ "python3", "-u", "/usr/src/app/app.py" ]
     EXPOSE 9000

4. Build an image.

   s build --dockerfile ./code/Dockerfile

5. Deploy the code to Function Compute.

   s deploy

   Note

   If you run the preceding command repeatedly and service name and function name remain unchanged, run the use local command to use local configurations.

6. Configure provisioned instances.

   s provision put --target 1 --qualifier LATEST

7. Check whether the provisioned instances are ready.

   s provision get --qualifier LATEST

   If the value of current is 1, the provisioned mode of GPU-accelerated instances is ready. Example:

   [2021-11-01T13:43:28.308] [INFO ] [S-CLI] - Start ...
   [2021-11-01T13:43:28.521] [INFO ] [FC] - Getting provision: tgpu_opencv_service.LATEST/tgpu_opencv_func
   customContainer-demo:
    serviceName: tgpu_opencv_service
    functionName: tgpu_opencv_func
    qualifier: LATEST
    resource: 188077086902****#tgpu_opencv_service#LATEST#tgpu_opencv_func
    target: 1
    current: 1
    scheduledActions: []
    targetTrackingPolicies: []

8. Invoke the function.

   • View the online version

     FC Invoke Result:
     {"function": "opencv_edge_canny"}

   • Perform edge detection on the image

     s invoke -e '{"method":"GET","headers":{"RUN-MODE":"normal"}}'
     
     enter download: https://your_public_oss/c1.png
     enter upload: https://your_public_oss/target.jpg # View the file to check the result of edge detection. 
     FC Invoke Result:
     {"result": "CUDA-capable device supported | process image ok!"}

9. Release the GPU-accelerated instances.

   s provision put --target 0 --qualifier LATEST

Use the Function Compute console to deploy a GPU application

1. Deploy an image.

   1. Create a Container Registry Enterprise Edition instance or Container Registry Personal Edition instance.

      We recommend that you create an Enterprise Edition instance. For more information, see Create a Container Registry Enterprise Edition instance.

   2. Create a namespace and an image repository.

      For more information, see the Step 2: Create a namespace and Step 3: Create an image repository sections of the "Use Container Registry Enterprise Edition instances to build images" topic.

   3. Perform operations on Docker as prompted in the Container Registry console. Then, push the preceding sample app.py and Dockerfile to the instance image repository. For more information about the files, see app.py and Dockerfile in the /code directory when you deploy a GPU application by using Serverless Devs.

      

2. Create a service. For more information, see the "Create a service“ section of the Manage services section.

3. Create a function. For more information, see Create a custom container function.

   Note

   Select GPU Instance for Instance Type and Process HTTP Requests for Request Handler Type.

4. Change the execution timeout period of the function.

   1. Find the function that you want to manage and click Configure in the Actions column.

   2. In the Environment Variables section, change the value of Execution Timeout Period and click Save.

      

   Note

   Transcoding duration by using CPU exceeds the default value of 60 seconds. Therefore, we recommend that you set the value of Execution Timeout Period to a larger value.

5. Configure a provisioned GPU-accelerated instance.

   1. On the function details page, click the Auto Scaling tab and click Create Rule.

   2. On the page that appears, configure the following parameters to provision GPU-accelerated instances and click Create.

      For more information about how to configure provisioned instances, see Configure provisioned instances and auto scaling rules.

   After the configuration is complete, you can check whether the provisioned GPU-accelerated instances are ready in the rule list. Specifically, check whether the value of Current Reserved Instances is the specified number of provisioned instances.

6. Use cURL to test the function.

   1. On the function details page, click the Triggers tab to view trigger configurations and obtain the trigger endpoint.

   2. Run the following command in the CLI to invoke the GPU-accelerated function:

      • View online function versions

        curl "https://tgpu-op-console-tgpu-op-console-ajezokddpx.cn-shenzhen.fcapp.run"
        {"function": "opencv_edge_canny"}

      • Perform edge detection on the image

        curl "https://tgpu-op-console-tgpu-op-console-ajezokddpx.cn-shenzhen.fcapp.run" -H "RUN-MODE: normal"
        {"result": "CUDA-capable device supported | process image ok!"}