[英]Docker Image with Conda Env Activated and Ready for Shell Commands
I have tried many ways through searching for a solution.通过寻找解决方案,我尝试了很多方法。
I think my problem is different.我认为我的问题是不同的。
I am wanting to have a docker image that has the environment installed and then active and ready for shell commands like: flake8, pylint, black, isort, coverage我想要一个 docker 图像,它安装了环境,然后处于活动状态并准备好执行 shell 命令,例如:flake8、pylint、black、isort、coverage
Dockerfile Dockerfile
FROM continuumio/miniconda3
# Create the environment:
COPY conda_env_unit_tests.yml .
RUN conda env create -f conda_env_unit_tests.yml
RUN echo "conda activate up-and-down-pytorch" >> ~/.bashrc
conda_env_unit_test.yml conda_env_unit_test.yml
name: up-and-down-pytorch
channels:
- defaults
- conda-forge
dependencies:
- python=3.9
- pytest
- pytest-cov
- black
- flake8
- isort
- pylint
.gitlab-ci.yml (slimmed down) .gitlab-ci.yml(精简)
stages:
- docker
- linting
- test
build_unit_test_docker:
stage: docker
tags:
- docker
image: docker:stable
services:
- docker:dind
variables:
IMAGE_NAME: "miniconda3-up-and-down-unit-tests"
script:
- cp /builds/upanddown1/mldl/up_and_down_pytorch/conda_env_unit_tests.yml /builds/upanddown1/mldl/up_and_down_pytorch/docker/unit_tests/
- docker -D login $CI_REGISTRY -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD
- docker -D build -t $CI_REGISTRY/upanddown1/mldl/up_and_down_pytorch/$IMAGE_NAME docker/unit_tests/
- docker -D push $CI_REGISTRY/upanddown1/mldl/up_and_down_pytorch/$IMAGE_NAME
rules:
- changes:
- docker/unit_tests/Dockerfile
- conda_env_unit_tests.yml
unit-test:
stage: test
# image: continuumio/miniconda3:latest
image: $CI_REGISTRY/upanddown1/mldl/up_and_down_pytorch/miniconda3-up-and-down-unit-tests
script:
# - conda env create --file conda_env.yml
# - source activate up-and-down-pytorch
- coverage run --source=. -m pytest --verbose
- coverage report
- coverage xml
coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
artifacts:
reports:
coverage_report:
coverage_format: cobertura
path: coverage.xml
The Docker Image gets uploaded to the gitlab registry and the unit test stage uses that image, however: Docker 图像被上传到 gitlab 注册表,单元测试阶段使用该图像,但是:
/bin/bash: line 127: coverage: command not found
(ultimate goal was to not have to create the conda environment every time I wanted to lint or run unit tests) (最终目标是每次我想进行 lint 或运行单元测试时都不必创建 conda 环境)
Figured it out today.今天想通了。 Dropped the duration from 8.5 minutes to 1:07 for the unit tests.将单元测试的持续时间从 8.5 分钟减少到 1:07。
Change was to source the environment in the unit-test job.更改是在单元测试作业中获取环境。 Didn't need to do that in the Dockerfile.不需要在 Dockerfile 中这样做。
Dockerfile Dockerfile
FROM continuumio/miniconda3
# Create the environment:
COPY conda_env_unit_tests.yml .
RUN conda env create -f conda_env_unit_tests.yml
conda_env_unit_tests.yml conda_env_unit_tests.yml
name: up-and-down-pytorch
channels:
- defaults
- conda-forge
dependencies:
- python=3.9
- pandas
- pytest
- pytest-cov
- black
- flake8
- isort
- pylint
.gitlab-ci.yml (slimmed down) .gitlab-ci.yml(精简)
stages:
- docker
- linting
- test
build_unit_test_docker:
stage: docker
tags:
- docker
image: docker:stable
services:
- docker:dind
variables:
IMAGE_NAME: "miniconda3-up-and-down-unit-tests"
script:
- cp /builds/upanddown1/mldl/up_and_down_pytorch/conda_env_unit_tests.yml /builds/upanddown1/mldl/up_and_down_pytorch/docker/unit_tests/
- docker -D login $CI_REGISTRY -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD
- docker -D build -t $CI_REGISTRY/upanddown1/mldl/up_and_down_pytorch/$IMAGE_NAME docker/unit_tests/
- docker -D push $CI_REGISTRY/upanddown1/mldl/up_and_down_pytorch/$IMAGE_NAME
rules:
- changes:
- docker/unit_tests/Dockerfile
- conda_env_unit_tests.yml
unit-test:
stage: test
image: $CI_REGISTRY/upanddown1/mldl/up_and_down_pytorch/miniconda3-up-and-down-unit-tests
script:
- source activate up-and-down-pytorch
- coverage run --source=. -m pytest --verbose
- coverage report
- coverage xml
coverage: '/(?i)total.*? (100(?:\.0+)?\%|[1-9]?\d(?:\.\d+)?\%)$/'
artifacts:
reports:
coverage_report:
coverage_format: cobertura
path: coverage.xml
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