gitlab-runner
go
gitlab-runner | go | |
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47 | 2,075 | |
- | 119,718 | |
- | 0.7% | |
- | 10.0 | |
- | 5 days ago | |
Go | ||
- | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gitlab-runner
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🦊 GitLab CI: Deploy a Majestic Single Server Runner on AWS
#!/bin/bash # ### Script to initialize a GitLab runner on an existing AWS EC2 instance with NVME disk(s) # # - script is not interactive (can be run as user_data) # - will reboot at the end to perform NVME mounting # - first NVME disk will be used for GitLab custom cache # - last NVME disk will be used for Docker data (if only one NVME, the same will be used without problem) # - robust: on each reboot and stop/start, disks are mounted again (but data may be lost if stop and then start after a few minutes) # - runner is tagged with multiple instance data (public dns, IP, instance type...) # - works with a single spot instance # - should work even with multiple ones in a fleet, with same user_data (not tested for now) # # /!\ There is no prerequisite, except these needed variables : MAINTAINER=zenika RUNNER_NAME="majestic-runner" GITLAB_URL=https://gitlab.com/ GITLAB_TOKEN=XXXX # prepare docker (re)install sudo apt-get -y install apt-transport-https ca-certificates curl gnupg lsb-release sysstat curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list >/dev/null sudo apt-get update # needed to use the docker.list # install gitlab runner curl -L "https://packages.gitlab.com/install/repositories/runner/gitlab-runner/script.deb.sh" | sudo bash sudo apt-get -y install gitlab-runner # create NVME initializer script cat </home/ubuntu/nvme-initializer.sh #!/bin/bash # # To be run on each fresh start, since NVME disks are ephemeral # so first start, start after stop, but not on reboot # inspired by https://stackoverflow.com/questions/45167717/mounting-a-nvme-disk-on-aws-ec2 # date | tee -a /home/ubuntu/nvme-initializer.log ### Handle NVME disks # get NVME disks bigger than 100Go (some small size disk may be there for root, depending on server type) NVME_DISK_LIST=\$(lsblk -b --output=NAME,SIZE | grep "^nvme" | awk '{if(\$2>100000000000)print\$1}' | sort) echo "NVME disks are: \$NVME_DISK_LIST" | tee -a /home/ubuntu/nvme-initializer.log # there may be 1 or 2 NVME disks, then we split (or not) the mounts between GitLab custom cache and Docker data export NVME_GITLAB=\$(echo "\$NVME_DISK_LIST" | head -n 1) export NVME_DOCKER=\$(echo "\$NVME_DISK_LIST" | tail -n 1) echo "NVME_GITLAB=\$NVME_GITLAB and NVME_DOCKER=\$NVME_DOCKER" | tee -a /home/ubuntu/nvme-initializer.log # format disks if not sudo mkfs -t xfs /dev/\$NVME_GITLAB | tee -a /home/ubuntu/nvme-initializer.log || echo "\$NVME_GITLAB already formatted" # this may already be done sudo mkfs -t xfs /dev/\$NVME_DOCKER | tee -a /home/ubuntu/nvme-initializer.log || echo "\$NVME_DOCKER already formatted" # disk may be the same, then already formated by previous command # mount on /gitlab-host/ and /var/lib/docker/ sudo mkdir -p /gitlab sudo mount /dev/\$NVME_GITLAB /gitlab | tee -a /home/ubuntu/nvme-initializer.log sudo mkdir -p /gitlab/custom-cache sudo mkdir -p /var/lib/docker sudo mount /dev/\$NVME_DOCKER /var/lib/docker | tee -a /home/ubuntu/nvme-initializer.log ### reinstall Docker (which data may have been wiped out) # docker (re)install sudo apt-get -y reinstall docker-ce docker-ce-cli containerd.io docker-compose-plugin | tee -a /home/ubuntu/nvme-initializer.log echo "NVME initialization succesful" | tee -a /home/ubuntu/nvme-initializer.log EOF # set NVME initializer script as startup script sudo tee /etc/systemd/system/nvme-initializer.service >/dev/null <
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Atlassian prepares to abandon on-prem server products
GitLab team member here, thanks for sharing.
> Still not a big fan of how stiff Yaml pipelines feel in Gitlab CI
Maybe the pipeline editor in "Build > Pipeline editor" can help with live linting, or more advanced features such as parent-child pipelines or merge trains.
If you need tips for optimizing the CI/CD pipeline, suggest following these tips in the docs https://docs.gitlab.com/ee/ci/pipelines/pipeline_efficiency.... or a few more tips in my recent talk "Efficient DevSecOps pipelines in cloud-native world", slides from Chemnitz Linux Days 2023 in https://docs.google.com/presentation/d/1_kyGo_cWi5dKyxi3BfYj...
> and that tickets for what seems like a simple feature [1] hang around for years, but it is nice.
Thanks for sharing. (FYI for everyone) The linked issue suggests a Docker cache cleanup script, which might be helpful. https://gitlab.com/gitlab-org/gitlab-runner/-/issues/27332#n... -> https://docs.gitlab.com/runner/executors/docker.html#clear-t...
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GitHub Actions could be so much better
If only competitors could do better...
https://gitlab.com/gitlab-org/gitlab-runner/-/issues/2797
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- Gitlab runner in-depth - communication and CI_JOB_TOKEN
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Caching of GitLab CI is too slow for rust build.
GitLab MR for the CACHE_COMPRESSION_LEVEL implementation
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The GMP library's website is under attack by a single GitHub user
And in general just making caching stuff easier. I feel like it is unnecessarily complicated for example to cache apt-get in Gitlab which I assume makes most people not do it.
https://gitlab.com/gitlab-org/gitlab-runner/-/issues/991#not...
go
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Go: the future encoding/json/v2 module
A Discussion about including this package in Go as encoding/json/v2 has been started on the Go Github project on 2023-10-05. Please provide your feedback there.
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Evolving the Go Standard Library with math/rand/v2
I like the Principles section. Very measured and practical approach to releasing new stdlib packages. https://go.dev/blog/randv2#principles
The end of the post they mention that an encoding/json/v2 package is in the works: https://github.com/golang/go/discussions/63397
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Microsoft Maintains Go Fork for FIPS 140-2 Support
There used to be the GO FIPS branch :
https://github.com/golang/go/tree/dev.boringcrypto/misc/bori...
But it looks dead.
And it looks like https://github.com/golang-fips/go as well.
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Borgo is a statically typed language that compiles to Go
I'm not sure what exactly you mean by acknowledgement, but here are some counterexamples:
- A proposal for sum types by a Go team member: https://github.com/golang/go/issues/57644
- The community proposal with some comments from the Go team: https://github.com/golang/go/issues/19412
Here are some excerpts from the latest Go survey [1]:
- "The top responses in the closed-form were learning how to write Go effectively (15%) and the verbosity of error handling (13%)."
- "The most common response mentioned Go’s type system, and often asked specifically for enums, option types, or sum types in Go."
I think the problem is not the lack of will on the part of the Go team, but rather that these issues are not easy to fix in a way that fits the language and doesn't cause too many issues with backwards compatibility.
[1]: https://go.dev/blog/survey2024-h1-results
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AWS Serverless Diversity: Multi-Language Strategies for Optimal Solutions
Now, I’m not going to use C++ again; I left that chapter years ago, and it’s not going to happen. C++ isn’t memory safe and easy to use and would require extended time for developers to adapt. Rust is the new kid on the block, but I’ve heard mixed opinions about its developer experience, and there aren’t many libraries around it yet. LLRD is too new for my taste, but **Go** caught my attention.
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How to use Retrieval Augmented Generation (RAG) for Go applications
Generative AI development has been democratised, thanks to powerful Machine Learning models (specifically Large Language Models such as Claude, Meta's LLama 2, etc.) being exposed by managed platforms/services as API calls. This frees developers from the infrastructure concerns and lets them focus on the core business problems. This also means that developers are free to use the programming language best suited for their solution. Python has typically been the go-to language when it comes to AI/ML solutions, but there is more flexibility in this area. In this post you will see how to leverage the Go programming language to use Vector Databases and techniques such as Retrieval Augmented Generation (RAG) with langchaingo. If you are a Go developer who wants to how to build learn generative AI applications, you are in the right place!
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From Homemade HTTP Router to New ServeMux
net/http: add methods and path variables to ServeMux patterns Discussion about ServeMux enhancements
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Building a Playful File Locker with GoFr
Make sure you have Go installed https://go.dev/.
- Fastest way to get IPv4 address from string
- We now have crypto/rand back ends that ~never fail
What are some alternatives?
woodpecker - Woodpecker is a simple yet powerful CI/CD engine with great extensibility.
v - Simple, fast, safe, compiled language for developing maintainable software. Compiles itself in <1s with zero library dependencies. Supports automatic C => V translation. https://vlang.io
kaniko - Build Container Images In Kubernetes
TinyGo - Go compiler for small places. Microcontrollers, WebAssembly (WASM/WASI), and command-line tools. Based on LLVM.
singularity - Singularity has been renamed to Apptainer as part of us moving the project to the Linux Foundation. This repo has been persisted as a snapshot right before the changes.
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
onedev - Git Server with CI/CD, Kanban, and Packages. Seamless integration. Unparalleled experience.
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
cockpit-podman - Cockpit UI for podman containers
Angular - Deliver web apps with confidence 🚀
machine
golang-developer-roadmap - Roadmap to becoming a Go developer in 2020