djinn VS firecracker

Djinn CI is a newly launched CI platform, with the following features:

I've built a CI platform [1] that does support running your CI builds without the server using an offline runner. I wrote about it here before: https://blog.djinn-ci.com/showcase/2022/08/06/running-your-c...

[1] - https://about.djinn-ci.com/

Author of Djinn CI here. This is a CI platform that I developed, it is open source but there is also a hosted offering https://about.djinn-ci.com. Some of the features are detailed below:

* Fully virtualized Linux VMs

* GitHub/GitLab integration

* Variable masking

* Configurable artifact cleanup limits

* Multi-repository builds

* Repeatable builds with cron jobs

* Custom QCOW2 images for builds

I've written some posts demonstrating the features of the platform which I have posted here before:

* https://blog.djinn-ci.com/showcase/2022/08/06/running-your-c...

* https://blog.djinn-ci.com/showcase/2022/08/16/using-multiple...

For further reading there is also the documentation sub-site at https://docs.djinn-ci.com/.

If you have any questions don't hesitate to reach out.

Good article. Firecracker is something that has definitely piqued my interest when it comes to quickly spinning up a throwaway environment to use for either development or CI. I run a CI platform [1], which currently uses QEMU for the build environments (Docker is also supported but currently disabled on the hosted offering), startup times are ok, but having a boot time of 1-2s is definitely highly appealing. I will have to investigate Firecracker further to see if I could incorporate this into what I'm doing.

Julia Evans has also written about Firecracker in the past too [2][3].

[1] - https://about.djinn-ci.com

[2] - https://jvns.ca/blog/2021/01/23/firecracker--start-a-vm-in-l...

[3] - https://news.ycombinator.com/item?id=25883253

At this point when it came to my hobbyist development, I had moved past PHP and started learning Go, and was looking to do some serious development with this for a CI platform I had an idea for. By now, I had a firmer grasp of the software stack I wanted to work with, a better understanding of how everything pieced together. And so I went about developing that CI platform, that would later become Djinn CI. I uninstalled VirtualBox and Vagrant and fully committed to using QEMU, booting up the local machine was as simple as hitting CTRL + R in my terminal, searching for qemu and hitting enter, an elegant solution I know.

I use mcmathja/curlyq and found it pretty reliable. This is the queue I use for Djinn CI an open source CI platform I developed.

Djinn CI makes working with multiple repositoriesin a build simple via the sourcesparameter in the build manifest. This allows you to specify multiple Git respositories to clone into your build environment. Each source would be a URL that could be cloned via git clone. With most CI platforms, a build's manifest is typically tied to the source code repository itself. With Djinn CI, whilst you can have a build manifest in a source code repository, the CI server itself doesn't really have an understanding of that repository. Instead, it simply looks at the sources in the manifest that is specified, and clones each of them into the build environment.

Perhaps the one feature that sets Djinn CI out from other CI platforms is the fact that is has an offline runner. The offline runner allows for CI builds to be run without having to send them to the server. There are some limitations around this, of course, but it provides a useful mechanism for sanity checking build manifests, testing custom images, and for building software without the need for a CI server.

You mention CI being done in a distributed fashion. Could you elaborate on what you mean by this?

I'm asking as I'm someone who has developed a CI platform [1], and one of its features is the offline runner [2]. The offline runner allows you to run your CI builds on your own computer, and does not communicate with the CI server whatsoever. Is this what you had in mind?

[1] https://about.djinn-ci.com

[2] https://docs.djinn-ci.com/user/offline-runner/

Djinn CI is a newly launched CI platform, with the following features:

This architecture leverages microVMs for rapid scaling and high-density workloads. But does it work for GPU? The answer is no. You can look at the old 2019 GitHub issue and the comments to it to get the bigger picture of why it is so.

Hi, I'm the CEO of the company that built this SDK.

We're a company called E2B [0]. We're building and open-source [1] secure environments for running untrusted AI-generated code and AI agents. We call these environments sandboxes and they are built on top of micro VM called Firecracker [2].

You can think of us as giving small cloud computers to LLMs.

We recently created a dedicated SDK for building custom code interpreters in Python or JS/TS. We saw this need after a lot of our users have been adding code execution capabilities to their AI apps with our core SDK [3]. These use cases were often centered around AI data analysis so code interpreter-like behavior made sense

The way our code interpret SDK works is by spawning an E2B sandbox with Jupyter Server. We then communicate with this Jupyter server through Jupyter Kernel messaging protocol [4].

We don't do any wrapping around LLM, any prompting, or any agent-like framework. We leave all of that on users. We're really just a boring code execution layer that sats at the bottom that we're building specifically for the future software that will be building another software. We work with any LLM. Here's how we added code interpreter to Claude [5].

Our long-term plan is to build an automated AWS for AI apps and agents.

Happy to answer any questions and hear feedback!

[0] https://e2b.dev/

[1] https://github.com/e2b-dev

[2] https://github.com/firecracker-microvm/firecracker

[3] https://e2b.dev/docs

[4] https://jupyter-client.readthedocs.io/en/latest/messaging.ht...

[5] https://github.com/e2b-dev/e2b-cookbook/blob/main/examples/c...

As far as I know, Fly uses Firecracker for their VMs. I've been following Firecracker for a while now (even using it in a project), and they don't support GPUs out of the box (and have no plan to support it [1]).

I'm curious to know how Fly figured their own GPU support with Firecracker. In the past they had some very detailed technical posts on how they achieved certain things, so I'm hoping we'll see one on their GPU support in the future!

[1]: https://github.com/firecracker-microvm/firecracker/issues/11...

I pass through a GPU and USB hub to a VM running on a machine in the garage. An optical video cable and network compatible USB extender brings the interface to a different room making it my primary “desktop” computer (and an outdated laptop as a backup device). Doesn’t get more silent and cool than this. Another VM on the garage machine gets a bunch of hard drives passed through to it.

That said, hardware passthrough/VFIO is likely out of the current realistic scope for this project. VM boot times can be optimized if you never look for hardware to initialize in the first place. Though they are still likely initializing a network interface of some sort.

“MicroVM” seems to be a term used when as much as possible is stripped from a VM, such as with https://github.com/firecracker-microvm/firecracker

According to their own FAQ it is indeed: https://github.com/firecracker-microvm/firecracker/blob/main...

What about microVMs like firecracker?

Dynamic memory management - Firecracker's RAM footprint starts low, but once a workload inside allocates RAM, Firecracker will never return it to the host system. After running several workloads inside, you end up with an idling VM that consumes 32 GB of RAM on the host, even though it doesn't need any of it.

Firecracker has a balloon device you can inflate (ie: acquire as much memory inside the VM as possible) and then deflate... returning the memory to the host.

https://github.com/firecracker-microvm/firecracker/blob/main...

Very few things in those companies are being written in Rust, and half of those projects chose Rust around ideological reasons rather than technical, with plenty of 'unsafe' thrown in for performance reasons

https://github.com/firecracker-microvm/firecracker/search?q=...

The fact that 'unsafe' even exists in Rust means it's no better than C with some macros.

Don't get me wrong, Rust has it's place, like all the other languages that came about for various reasons, but it's not going to gain wide adoption.

Future of programming consists of 2 languages - something like C that has a small instruction set for adopting to new hardware, and something that is very high level, higher than Python with LLM in the background. Everything in the middle is fodder.

https://github.com/firecracker-microvm/firecracker is the one that comes to mind, but most of these are internal.