act VS dagger

When it comes to the alternatives to tmate, there is another great debugging tool that you could check out. It is called act and it allows you to run GitHub Actions code on your local machine making debugging even easier. It has its own limitations and some learning curve but overall it is another tool you should use if you can’t fix the CI bugs by connecting directly into the running action with the tmate.

Link: https://github.com/nektos/act

Could you upload your build of GitHub's runner image to Docker Hub?

This would be quite useful for users of other GitHub Actions clones like act [0].

[0]: https://github.com/nektos/act

> These kinds of commit messages are typically an indicator of a broken process where somebody needs to commit to see something happen, like a deployment or build process, and aren't able to assert that stuff works locally.

This is one of my biggest pet peeves with services like github actions. Something running locally like "act" [1] isn't sufficient because it doesn't have everything github has and is extra friction anyway to get everyone to use it for testing.

[1] https://github.com/nektos/act

View on GitHub

If you use Github actions, act is incredibly useful. It can be used to test your GH actions, but also serves as an interface for running tasks locally.

That's something that often is difficult or basically impossible. Except for maybe GitHub actions through Act (https://github.com/nektos/act). I'd still lean to something in the yaml sphere if it eventually would be used in deployment pipelines and such. For example a solution incorporating ansible.

It also seems to me that the argument you make is mostly focused on the building step? Earthly certainly seems focused on that aspect.

I feel I'm being trolled, but I'll bite and accept the resulting downvotes

I don't think treating every mention of act as an opportunity for airing of personal grievances is helpful in a discussion when there's already ample reports of people's concrete issues with it, had one looked at the 800 issues in its repo https://github.com/nektos/act/issues?q=is%3Aissue or the 239 from gitea's for https://gitea.com/gitea/act_runner/issues or whatever is going on with Forgejo's fork https://code.forgejo.org/forgejo/act .

But, as for me specifically, there are two and a half answers: I wanted to run VSCodium's build locally, which act for sure puked about. Then, while trying to troubleshoot that, I thought I'd try something simpler and have it run the lint job from act's own repo <https://github.com/nektos/act/blob/1252e551b8672b1e16dc8835d...> to rule out "you're holding it wrong" type junk. It died with

  [checks/lint]     Failure - Main actions/setup-go@v3

https://twitter.com/mitsuhiko/status/1720410479141487099 :

> GitHub Actions currently charges $0.16 per minute* for the macOS M1 Runners. That comes out to $84,096 for 1 machine year*

GitHub Runner is written in Go; it fetches tasks from GitHub Actions and posts the results back to the Pull Request that spawned the build.

nektos/act is how Gitea Actions builds GitHub Actions workflow YAML build definition documents. https://github.com/nektos/act

https://twitter.com/MatthewCroughan/status/17200423527675700... :

> This is the macOS Ventura installer running in 30 VMs, in 30 #nix derivations at once. It gets the installer from Apple, automates the installation using Tesseract OCR and TCL Expect scripts. This is to test the repeatability. A single function call `makeDarwinImage`.

With a Multi-Stage Dockerfile/Containerfild, you can have a dev environment like xcode or gcc+make in the first stage that builds the package, and then the second stage the package is installed and tested, and then the package is signed and published to a package repo / app store / OCI container image repository.

SLSA now specifies builders for signing things correctly in CI builds with keys in RAM on the build workers.

"Build your own SLSA 3+ provenance builder on GitHub Actions" https://slsa.dev/blog/2023/08/bring-your-own-builder-github

The fact that I couldn't point to one page on the docs that shows the tl;dr or the what problem is this solving

https://docs.dagger.io/quickstart/562821/hello just emits "Hello, world!" which is fantastic if you're writing a programming language but less helpful if you're trying to replace a CI/CD pipeline. Then, https://docs.dagger.io/quickstart/292472/arguments doubles down on that fallacy by going whole hog into "if you need printf in your pipline, dagger's got your back". The subsequent pages have a lot of english with little concrete examples of what's being shown.

I summarized my complaint in the linked thread as "less cowsay in the examples" but to be honest there are upteen bazillion GitHub Actions out in the world, not the very least of which your GHA pipelines use some https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... so demonstrate to a potential user how they'd run any such pipeline in dagger, locally, or in Jenkins, or whatever by leveraging reusable CI functions that setup go or run trivy

Related to that, I was going to say "try incorporating some of the dagger that builds dagger" but while digging up an example, it seems that dagger doesn't make use of the functions yet <https://github.com/dagger/dagger/tree/v0.10.2/ci#readme> which is made worse by the perpetual reference to them as their internal codename of Zenith. So, even if it's not invoked by CI yet, pointing to a WIP PR or branch or something to give folks who have CI/CD problems in their head something concrete to map into how GHA or GitLabCI or Jenkins or something would go a long way

> GHA has "service containers", but unfortunately the feature is too basic to address real-world use cases: it assumes a container image can just … boot! … and only talk to the code via the network. Real world use cases often require serialized steps between the test & the dependencies, e.g., to create or init database dirs, set up certs, etc.)

My biased recommendation is to write a custom Dagger function, and run it in your GHA workflow. https://dagger.io

If you find me on the Dagger discord, I will gladly write a code snippet summarizing what I have in mind, based on what you explained of your CI stack. We use GHA ourselves and use this pattern to great effect.

Disclaimer: I work there :)

Dagger (https://dagger.io) is a great way to use BuildKit through language SDKs. It's such a better paradigm, I cannot imagine going back.

Dagger is by the same folks that brought us Docker. This is their fresh take on solving the problem of container building and much more. BuildKit can more than build images and Dagger unlocks it for you.

And suddenly, it's almost painfully obvious where all the pain came from. Cloud applications today are simply a patchwork of disconnected pieces. I have a compiler for my infrastructure, another for my functions, another for my containers, another for my CI/CD pipelines. Each one takes its job super seriously, and keeps me safe and happy inside each of these machines, but my application is not running on a single machine anymore, my application is running on the cloud.

That said I've been moving my CI/CD to https://dagger.io/ which has been FANTASTIC. It's code based so you can define all your pipelines in Go, Python, or Javascript and they all run on containers so I can run actions locally without any special setup. Highly recommended.

You are right make is arcane. But it gets the job done. There are new exciting things happening in this area. Check out https://dagger.io.

> but I'm not convinced it's ready to replace Gitlab CI.

The purpose of Dagger it's not to replace your entire CI (Gitlab in your case). As you can see from our website (https://dagger.io/engine), it works and integrates with all the current CI providers. Where Dagger really shines is to help you and your teams move all the artisanal scripts encoded in YAML into actual code and run them in containers through a fluent SDK which can be written in your language of choice. This unlocks a lot of benefits which are detailed in our docs (https://docs.dagger.io/).

> Dagger has one very big downside IMO: It does not have native integration with Gitlab, so you end up having to use Docker-in-Docker and just running dagger as a job in your pipeline.

This is not correct. Dagger doesn't depend on Docker. We're just conveniently using Docker (and other container runtimes) as it's generally available pretty much everywhere by default as a way to bootstrap the Dagger Engine. You can read more about the Dagger architecture here: https://github.com/dagger/dagger/blob/main/core/docs/d7yxc-o...

As you can see from our docs (https://docs.dagger.io/759201/gitlab-google-cloud/#step-5-cr...), we're leveraging the *default* Gitlab CI `docker` service to bootstrap the engine. There's no `docker-in-docker` happening there.

> It clumps all your previously separated steps into a single step in the Gitlab pipeline.

This is also not the case, we should definitely improve our docs to reflect that. You can organize your dagger pipelines in multiple functions and call them in separate Gitlab jobs as you're currently doing. For example, you can do the following:

```.gitlab-ci.yml

Check out https://dagger.io/. Write declarative pipelines in code, reproducibly run anywhere.

Is this similar to Dagger[1] ?

[1] https://dagger.io

Hey, https://dagger.io employee here. We can help with this. I'd recommend taking a look at out Python SDK (https://docs.dagger.io/sdk/python) to build your pipelines and then leverage Dagger Cloud (https://dagger.io/blog/dagger-cloud) for fast production deployments.