luigi VS abi-aa

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

Maybe if your use case is “smallish” and doesn’t require the whole studio suite you could check out apscheduler for doing python “tasks” on a schedule and luigi to build pipelines.

What are you trying to do? Distributed scheduler with a single instance? No database? Are you sure you don't just mean "a scheduler" ala Luigi? https://github.com/spotify/luigi

It's good to know what Airflow is not the only one on the market. There are Dagster and Spotify Luigi and others. But they have different pros and cons, be sure that you did a good investigation on the market to choose the best suitable tool for your tasks.

MLOps is a HUGE area to explore, and not surprisingly, there are many startups showing up in this space. If you want to get it on the latest trends, then I would look at workflow orchestration frameworks such as Metaflow (started off at Netflix, is now spinning off into its own enterprise business, https://metaflow.org/), Kubeflow (used at Google, https://www.kubeflow.org/), Airflow (used at Airbnb, https://airflow.apache.org/), and Luigi (used at Spotify, https://github.com/spotify/luigi). Then you have the model serving itself, so there is Seldon (https://www.seldon.io/), Torchserve (https://pytorch.org/serve/), and TensorFlow Serving (https://www.tensorflow.org/tfx/guide/serving). You also have the actual export and transfer of DL models, and ONNX is the most popular here (https://onnx.ai/). Spark (https://spark.apache.org/) still holds up nicely after all these years, especially if you are doing batch predictions on massive amount of data. There is also the GitFlow way of doing things and Data Version Control (DVC, https://dvc.org/) is taken a pole position there.

At Wonderflow we're doing a lot of ML / NLP using Python and recently we are enjoying writing data pipelines using Spotify's Luigi.

💡【About Luigi】 https://github.com/spotify/luigi Luigi was built at Spotify since 2012, it's open source and mainly used for getting data insights by showing recommendations, toplists, A/B test analysis, external reports, internal dashboards, etc.

So to sum that up, I went from having nothing for my side-project set up in AWS to having a Kubernetes cluster with the basic metrics and dashboard, a proper IAM-linked ServiceAccount support for a smooth IAM experience in K8s, and Luigi deployed so that I could then run a Luigi workflow using an ad-hoc run of a CronJob. That's quite remarkable to me. All that took hours to figure out and define when I first did it, over six months ago.

As far as I know, kernel modules are ordinary relocatable ELF executables, so the best resource will be the ELF specifications. The ARM-specific parts can be found here.

Yes, that would be the case when your code's entry-point executes, and from then on it is your responsibility to maintain the alignment. It has nothing to do with AHB. This advisory has some examples of what can go wrong if your stack isn't 8-byte aligned. The alignment does not make much of a difference in your little function, but it's something to keep in mind as you write more complex code.

Normally you'd save more than just PC as AAPCS (https://github.com/ARM-software/abi-aa/blob/main/aapcs32/aap...) mandates stack to be aligned to 8 bytes for "public interface" functions. But this is is not a "public" function so it's fine to only save lr here.

"bx lr" is only used on it's own when the function doesn't call another function (altering lr), and doesn't need to save any registers.

If you see pop {lr}; bx lr then that's code that's being compiled to explicitly support Armv4 (e.g. Arm7TDMI)

The Arm Procedure Call Standards have "Arm C and C++ Language Mappings" sections that all say char is an "unsigned byte".

eabi: many pages in this official ARM repository define it as "An ABI suited to the needs of embedded, and deeply embedded (sometimes called free standing), applications." It seems to be the name of an ABI, or maybe the ABI, that code compiled for ARM chips is expected to use? Except there's also AEABI, the first A stands for ARM, and that's something different? ARM's naming conventions confuse me endlessly.

Latest versions of the ABI specifications linked in the Machine Specific section

ARM: https://github.com/ARM-software/abi-aa/releases

x86-64: https://gitlab.com/x86-psABIs/x86-64-ABI (go to most recent CI job and download artifacts for a compiled PDF)

> Apple changed some things that impact PyPy, like the register uses and ffi calling conventions.

I thought everyone who used 64-bit ARM used ARM's AAPCS64 (https://github.com/ARM-software/abi-aa/blob/master/aapcs64/a...), so the register usage and FFI calling convention should be the same as on Linux and Windows. What did Apple do that would affect the PyPy JIT?