tokio-uring VS firecracker

While Mio will probably not implement uring in its current design, there's https://github.com/tokio-rs/tokio-uring if you want to use io_uring in Rust.

It's still in development, but the Tokio team seems intent on getting good io_uring support at least!

As the README states, the Rust implementation requires a kernel newer than the one that shipped with Ubuntu 20.04 so I think it'll be a while before we'll see significant development among major libraries.

That's what the pool is for: https://github.com/tokio-rs/tokio-uring/blob/master/src/buf/fixed/pool.rs

Tokio supports io_uring (https://github.com/tokio-rs/tokio-uring), so perhaps when it's mature and battle-tested, it'd be easier to transition to it if Cloudflare aren't using it already.

- Tokio suffers from a similar problem

Eg via tokio-uring.

I strongly recommend you to look into io-uring and use async executors that take advantages of it: - tokio-uring (not recommended as it is still undergoing development) - monoio - glommio

This is my thinking as well. Specifically, I realized that if you don’t use tasks, but rather futures and join, than structured concurrency just works out (at the cost of less efficient poll). In a single-threaded/thread-per-core runtime, tasks could have the same semantics as futures. Somewhat elaborated here: https://github.com/tokio-rs/tokio-uring/issues/81

There's a new API on Linux called io_uring that has performance benefits, but most executors don't use it yet, except executors meant specifically to harness the power of io_uring like tokio-uring and Glommio

Breaking it down a bit further- Rust's async is zero-cost, and there's no way to write faster equivalent code to the language construct in Rust (and presumably other LLVM languages). Tokio introduces abstractions over OS APIs (indirectly) and provides a runtime. The runtime isn't zero cost, but it is likely to be better optimized for "standard" situations than a homebrewed solution, and its primary competition is in the form of other large async runtimes. On the other hand, Tokio's IO routines are (AFAIK) about as well written as one can get with blocking OS APIs, and the only competitors in that space are projects like tokio-uring that use APIs more well suited for asynchronous usage.

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.