How to make GitLab Windows shared runners to build faster? - c++

Background
I have a CI pipeline for a C++ library I've been developing. So far, I can distribute this lib to Linux and Windows systems. Since I use GitLab to build, test and package my lib, I'd like to have my Windows builds running faster and I have no clue on how to do that.
Currently, I use the following script for my Windows builds:
.windows_template:
tags:
- windows
before_script:
- choco install cmake.install -y --installargs '"ADD_CMAKE_TO_PATH=System"'
- choco install python --pre -y
- choco install git -y
- $env:ChocolateyInstall = Convert-Path "$((Get-Command choco).Path)\..\.."; Import-Module "$env:ChocolateyInstall\helpers\chocolateyProfile.psm1"; refreshenv
- python -m pip install --upgrade pip
- pip install conan monotonic
The problem
Any build with the script above can take up to 10 minutes; worse: I have two stages, each one taking the same amount of time. This means that my whole CI pipeline will take 20 minutes to finish because of slowness in Windows builds.
Ideal solution
EVERYTHING in my before_script can be cached or stored as an image. I only need some hints on how to do it properly.
Additional information
I use the following tools for my builds:
CMake: to support my building process;
Python3: to test and build packages;
Conan (requires Python3): to support the creation of packages with several features, as well as distribute them;
Git: to download Googletest in CMake configuration step This is already provided in the cookbooks - I might just remove this extra installation step in my before_script;
Googletest (requires Python3): testing library;
Visual Studio DEV Tools: to compile the library This is already in the cookbooks.

Installing packages like this (whether it's OS packages though apt-get install... or pip, or anything else) is generally against best practices for CI/CD jobs because every job that runs will have to do the same thing, costing a lot of time as you run more pipelines, as you've seen already.
A few alternatives are to search for an existing image that has everything you need (possible but not likely with more dependencies), split up your job into pieces that might be solved by an image with just one or two dependencies, or create a custom docker image to use in your jobs. I answered a similar question with an example a few weeks ago here: "Unable to locate package git" when running GitLab CI/CD pipeline
But here's an example Dockerfile with Windows:
# Dockerfile
FROM mcr.microsoft.com/windows
RUN ./install_chocolatey.sh
RUN choco install cmake.install -y --installargs '"ADD_CMAKE_TO_PATH=System"'
RUN choco install python --pre -y
RUN choco install git -y
...
The FROM line says that our new image extends the mcr.microsoft.com/windows base image. You can extend any image you have access to, even if it already extends another image (in fact, that's how most images work: they start with something small, like a base OS installation, then add things needed for that package. PHP for example starts on an Ubuntu image, then installs the necessary PHP packages).
The first RUN line is just an example. I'm not a Windows user and don't have experience installing Chocolatey, but you'd do here whatever you'd normally do to install it locally. The rest are for installing whatever else you need.
Then run
docker build /path/to/dockerfile-dir -t mygroup/mytag:version
The path you supply needs to be the directory that contains the Dockerfile, not the Dockerfile itself. The -t flag sets the image's tag after it's built (though you can do that with a separate command, docker tag too).
Next, you'll have to log into whichever registry you're using (Docker Hub (https://docs.docker.com/docker-hub/repos/), Gitlab Container Registry (https://docs.gitlab.com/ee/user/packages/container_registry/), a private registry your employer may support, or any other option.
docker login my.docker.hub.com
Now you can push the image to the registry:
docker push my.docker.hub.com/mygroup/mytag:version
You'll have to review the information in the docs about telling your Gitlab runner or pipelines how to authenticate with the registry (unless it's Public on Docker Hub or you use the Gitlab Container Registry) https://docs.gitlab.com/ee/ci/docker/using_docker_images.html#define-an-image-from-a-private-container-registry
Once all that's done, you can use your new image in your CI jobs, and everything we put into the image will be ready to use:
.windows_template:
image: my.docker.hub.com/mygroup/mytag:version
tags:
- windows
...

Related

no such option --system for pip install

I'm attempting to deploy a very basic trading system to AWS using serverless (following along with this link), but I have a bit of a problem.
Prior to running the deployment command, I'm supposed to run
pip3 install -r requirements.txt -t . --system
but I am getting an error message saying 'no such option: --system'
Initially, I just tried to install the packages without the --system option, but I think that's causing the cron lamda(??) function to fail when I execute it manually through the serverless console because it's not finding the requisite modules.
I'm assuming it's because they aren't being installed properly so my question is how then should I install them so this doesn't happen?
Running
pip3 install -r requirements.txt
alone (while in the trading system directory) does not suffice.
So, what should I do?
The original author was working on an older Debian-derived system, you aren't. You can safely omit this option if it's not supported.
I don't have an authoritative link available, although this came up in a Google search. But here's my summary:
With older Debian-derived systems (eg, Ubuntu 18.04), the --user flag was enabled by default and it overrode the -t flag, so all packages would be installed in the $HOME/.local. The --system flag was nominally intended to allow installation in the system package directory, but in practice it was needed to enable -t.
This is fixed for Debian-derived systems that default to Python 3 (eg, Ubuntu 20.04).
It was never an issue for non-Debian systems (eg, EC2 Linux).
Since you don't seem to be familiar with pip, the -r argument tells it to use a file containing dependencies, and the -t argument tells it to install those dependencies in the current directory (not a great habit, but I don't want to describe virtual environments).

Is it possible to create a standalone file to import a python library created with pybind?

I hope I'm clear in my question, if not please tell me.
I am using OpenImageIO's python bindings (pybind11) for some scripts that will run on hundreds of computers. Unfortunately it took me a lot of time to install OpenImageIO and make it work with my Python2 installation. I'd like to know if there's a way to create a file/folder that I could send to other computers so they can install the Python module simply with "pip install file/folder".
Thanks ofr your help
Are you running the scripts on a compute cluster with a shared filesystem? If so, then there's no need to create separate installations of python for each machine. The simplest solution is to create ONE python environment in a location that is accessible by all of your machines. An easy way to create a Python environment in a non-system location is to use Miniconda. Install it to a shared (network) location, and create an environment for all of your machines to use.
If your machines do NOT have a shared file system, then you'll need to somehow reproduce the environment on all of them independently. In this case, there's no simple way to do that with pip.**
But if you can use conda instead, then there's a very straightforward solution. First, install everything you need into a single conda environment. Then you have a choice: You can export the list of conda packages, or simply copy the entire conda environment directory to the other machines.
OpenImageIO is available from the conda-forge channel, a community-developed repository of conda packages. The name of the package is py-openimageio. They have stopped updating the python-2.7 version, but the old versions are still available.
Here's how to do it.
Install Miniconda-2.7
Create a new environment with python 2.7, OpenImageIO, and any other packages you need:
conda create -n jao-stuff -c conda-forge py-openimageio python=2.7
conda activate jao-stuff
python -c "import OpenImageIO; print('It works!')"
Do ONE of the following:
a. Export the list of packages in your environment:
conda env export -n jao-stuff -f jao-stuff-packages.yaml
Then, on the other machines, install Miniconda, then create the environments using the package list from the previous step:
conda create -n jao-stuff --file jao-stuff-packages.yaml
OR
b. Just copy all of the files in the environment to the other machines, and run them directly. Conda environments are self-contained (except for a few low-level system libraries), so you can usually just copy the whole thing to another machine and run it without any further install step.
tar czf jao-stuff.tar.gz $(conda info --prefix)/envs/jao-stuff
On the other machine, unpack the tarball anywhere and just run the python executable it contains:
tar xzf jao-stuff.tar.gz
jao-stuff/bin/python -c "import OpenImageIO; print('It works!')"
**That's because OpenImageIO is a C++ project, with several C++ dependencies, and they don't provide binaries in the wheel format. I don't blame them -- pip is not well suited to this use-case, even with wheels. Conda, on the other hand, was designed for exactly this use-case, and works perfectly for it.

Where should c++ application be compiled in GitLab CI Docker workflow?

I’m looking to understand how to properly structure my .gitlab-ci.yml and Dockerfile such that I can build a C++ application into a Docker container.
I’m struggling with where the actual compilation and link of the C++ application should take place within the CI workflow.
What I’ve done:
My current in approach is to use Docker in Docker with a private gitlab docker registry.
My gitlab-ci.yml uses a dind docker image service I created based on the the docker:19.03.1-dind image but includes my certificates to talk securely to my private gitlab docker registry.
I also have a custom base image referenced by my gitlab-ci.yml based on docker:19.03.1 that includes what I need for building, eg cmake, build-base mariadb-dev, etc.
Have my build script added to the gitlab-ci.yml to build the application, cmake … && cmake --build .
The dockerfile then copies the final binary produced in my build step.
Having done all of this it doesn’t feel quite right to me and I’m wondering if I’m missing the intent. I’ve tried to find a C++ example online to follow as example but have been unsuccessful.
What I’m not fully understanding is the role of each player in the docker-in-docker setup: docker image, dind image, and finally the container I’m producing…
What I’d like to know…
Who should perform the build and contain the build environment, the base image specified in my .gitlab-ci.yml or my Dockerfile?
If I build with the dockerfile, how to i get the contents of the source into the docker container? Do I copy the /builds dir? Should I mount it?
Where to divide who performs work, gitlab-ci.yml or Docker file?
Reference to a working example of a C++ docker application built with Docker-in-Docker Gitlab CI.
.gitlab-ci.yml
image: $CI_REGISTRY/building-blocks/dev-mysql-cpp:latest
#image: docker:19.03.1
services:
- name: $CI_REGISTRY/building-blocks/my-dind:latest
alias: docker
stages:
- build
- release
variables:
# Use TLS https://docs.gitlab.com/ee/ci/docker/using_docker_build.html#tls-enabled
DOCKER_TLS_CERTDIR: "/certs"
CONTAINER_TEST_IMAGE: $CI_REGISTRY_IMAGE:$CI_COMMIT_REF_SLUG
CONTAINER_RELEASE_IMAGE: $CI_REGISTRY_IMAGE:latest
before_script:
- docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY
build:
stage: build
script:
- mkdir build
Both approaches are equally valid. If you look at other SO questions, one thing you'll probably notice is that Java/Docker images almost universally build a jar file on their host and then COPY it into an image, but Go/Docker images tend to use a multi-stage Dockerfile starting from sources.
If you already have a fairly mature build system and your developers already have a very consistent setup, it makes sense to do more work in the CI environment (in your .gitlab.yml file). Build your application the same way you already do, then COPY it into a minimal Docker image. This approach is also helpful if you need to ship both Docker and non-Docker artifacts. If you have a make dist style tar file and want to get a Docker image out of it, you could use a very straightforward Dockerfile like
FROM ubuntu
RUN apt-get update && apt-get install ...
ADD dist/myapp.tar.gz /usr/local # unpacking it
EXPOSE 12345
CMD ["myapp"] # /usr/local/bin/myapp
On the other hand, if your developers have a variety of desktop environments and you're really trying to standardize things, and you only need to ship the Docker image, it could make sense to centralize most things in the Dockerfile. This would have the advantage that every developer could run the exact build sequence themselves locally, rather than depending on the CI system to try simple changes. Something built around GNU Autoconf might look more like
FROM ubuntu AS build
RUN apt-get update \
&& apt-get install --no-install-recommends --assume-yes \
build-essential \
lib...-dev
WORKDIR /app
COPY . .
RUN ./configure --prefix=/usr/local \
&& make \
&& make install
FROM ubuntu
RUN apt-get update \
&& apt-get install --no-install-recommends --assume-yes \
lib...
COPY --from=build /usr/local /usr/local
CMD ["myapp"]
If you do the primary build in a Dockerfile, you need to COPY the source code in. Volume mounts don't work at this point in the sequence. CI systems should avoid bind-mounting source code into a container in any case: you want to run tests against the actual artifact you've built, and not a hybrid of a built Docker image but with all of its source code replaced.

Docker compose install requirements in a shared directory

I am having several containers, and each of my containers are having their own Dockerfile. Everytime I am building, using docker-compose build, each container runs its own requirements; either from a requirements.txt file (RUN pip install -r requirements.txt), or directly from the Dockerfile (RUN pip install Django, celery, ...). Many of the requirements are common in some of the containers (almost all).
It is working perfectly, but there is a problem with build time. It takes almost 45 minutes to build every container from scratch. (lets say after I deleted all images and containers)
Is there a way, to install all the requirements in a common directory for all containers, so that we dont install the common requirements each time a new container is building?
Docker-compose I am using is version 2.
You can define your own base image. Let's say all your containers need django and boto for instance, you can create your own Dockerfile:
FROM python:3
RUN pip install django boto
# more docker commands
Then you can build this image as arrt_dtu/envbase and publish it somewhere (dockerhub, internal docker environment of your company). Now you can create your specialized images using this one:
FROM arrt_dtu/envbase
RUN pip install ...
That's exactly the same principle we have with the ruby image, for instance. The ruby one uses a linux one. If you want a rails image, you can use the ruby one as well. Docker images are totally reusable!

How to speed jenkins build process while installing requirements using pip

I am using Jenkins CI for my django project. For Django-Jenkins integration I am using the django-jenkins app. In the build step of Jenkins I create a fresh virtualenv and install all the dependencies for each build using requirements file. However, this makes build extremely slow because a fresh copy of all the dependencies must be downloaded from a PyPI mirror, even if nothing has changed in the dependencies since the last build. So I started using the local caching built-in to pip by setting the PIP_DOWNLOAD_CACHE environment variable. But the whole build process is still painfully slow and takes more than 10 minutes. Is there any way I could speed up the whole process? Maybe by caching the compiled dependencies or something else?
Just only install a fresh virtualenv if your requirements.txt file changes. This can be done easily with some shell commands. We are doing something similar in one of our projects. In a Jenkins shell window we have (after svn up):
touch changed.txt
stat -c %Y project/requirements.txt > changed1.txt
diff -q changed.txt changed1.txt || echo "DO YOUR PIP --upgrade HERE!"
Why bother creating a fresh virtualenv each time you build? You should be able to create just one and simply activate it with . /path/to/venv/bin/activate as an 'Execute shell script' build step (assuming the use of linux here). Then, if you need to install a new dependency, you can activate the venv on your own and pip install the new package.