SolexCV-AI Development environment with opencv4.1, cuda11.2, mnn2.2.0, protobuf3.6, python3.8.
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A dedicated development/deployment environment image for the SolexCV-AI project.
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by tunmx, solexcv_ai_develop_cuda/1.0a
Software version of the built-in environment
Pull this image.
docker pull tunmx/solexcv_ai_develop_cuda:latest
You can use the image as an operating system for software development by using the -it command to access the interactive page:
docker run -it IMAGE_ID
In addition, you can use it to build the Dockerfile file and compile the SolexCV-AI-related projects and dependencies that you need to compile. We put the tripartite dependencies in the /3rdparty location:
FROM tunmx/solexcv_ai_develop_cuda:latest
MAINTAINER jingyuyan<[email protected]>
WORKDIR /workspace
COPY . /workspace
RUN rm -rf 3rdparty
RUN ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime
RUN echo 'Asia/Shanghai' >/etc/timezone
RUN ln -s /3rdparty .
# push you c/c++ project build script
The advantage of using docker-compose to build and deploy projects is that you can build multiple tasks using the services configuration in a single description file, and you can flexibly compose and compose multiple containers for compiling, building, testing, and deploying projects. If you use docker-compose, you need to create docker-compose.yml file for description file writing. Currently, it is recommended to use the compile, test, and runtime deployment styles as shown in the following example:
version: "3"
services:
build_cuda: # Compile backend for cuda
container_name: YOUR_CONTAINER_NAME # The container name of the compilation work
image: tunmx/solexcv_ai_develop_cuda:latest
volumes:
- .:/work
working_dir: /work
command: bash command/build_linux_cuda.sh /3rdparty # Custom compiled bash scripts
tty: true
run_test_cuda: # Execute the cuda version of the test case
container_name: YOUR_CONTAINER_NAME
image: tunmx/solexcv_ai_develop_cuda:latest
volumes:
- .:/work
working_dir: /work
command: bash command/test.sh cuda
tty: true
server_runtime_cuda: # Run CUDA-based runtime services
container_name: YOUR_CONTAINER_NAME
image: tunmx/solexcv_ai_develop_cuda:latest
volumes:
- .:/work
working_dir: /work
command: bash command/run_server.sh cuda
tty: true
After completing the description file, you can execute the build script to perform the build:
# Compile backend for cuda
docker-compose up build_cuda
# The container name of the compilation work
docker-compose up run_test_cuda
# Execute the cuda version of the test case
docker-compose up server_runtime_cuda
In addition to development, you can use the image for rapid deployment of SolexCV-AI-related projects. The image provides C/C++ and Python3.8 development environments. You can build your software deployment requirements in the Dockerfile and quickly build deployment services. The advantage of using this mirroring deployment is that the configuration is consistent with the development environment, reducing the error rate during deployment.
Content type
Image
Digest
sha256:86c2570e3…
Size
6.4 GB
Last updated
almost 3 years ago
docker pull tunmx/solexcv_ai_develop_cuda