orchest
QEMU
orchest | QEMU | |
---|---|---|
44 | 190 | |
4,022 | 9,350 | |
0.1% | 2.1% | |
4.5 | 10.0 | |
11 months ago | 1 day ago | |
TypeScript | C | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
orchest
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Decent low code options for orchestration and building data flows?
You can check out our OSS https://github.com/orchest/orchest
- Build ML workflows with Jupyter notebooks
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Building container images in Kubernetes, how would you approach it?
The code example is part of our ELT/data pipeline tool called Orchest: https://github.com/orchest/orchest/
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Launch HN: Patterns (YC S21) â A much faster way to build and deploy data apps
First want to say congrats to the Patterns team for creating a gorgeous looking tool. Very minimal and approachable. Massive kudos!
Disclaimer: we're building something very similar and I'm curious about a couple of things.
One of the questions our users have asked us often is how to minimize the dependence on "product specific" components/nodes/steps. For example, if you write CI for GitHub Actions you may use a bunch of GitHub Action references.
Looking at the `graph.yml` in some of the examples you shared you use a similar approach (e.g. patterns/openai-completion@v4). That means that whenever you depend on such components your automation/data pipeline becomes more tied to the specific tool (GitHub Actions/Patterns), effectively locking in users.
How are you helping users feel comfortable with that problem (I don't want to invest in something that's not portable)? It's something we've struggled with ourselves as we're expanding the "out of the box" capabilities you get.
Furthermore, would have loved to see this as an open source project. But I guess the second best thing to open source is some open source contributions and `dcp` and `common-model` look quite interesting!
For those who are curious, I'm one of the authors of https://github.com/orchest/orchest
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Argo became a graduated CNCF project
Haven't tried it. In its favor, Argo is vendor neutral and is really easy to set up in a local k8s environment like docker for desktop or minikube. If you already use k8s for configuration, service discovery, secret management, etc, it's dead simple to set up and use (avoiding configuration having to learn a whole new workflow configuration language in addition to k8s). The big downside is that it doesn't have a visual DAG editor (although that might be a positive for engineers having to fix workflows written by non-programmers), but the relatively bare-metal nature of Argo means that it's fairly easy to use it as an underlying engine for a more opinionated or lower-code framework (orchest is a notable one out now).
- Ideas for infrastructure and tooling to use for frequent model retraining?
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Looking for a mentor in MLOps. I am a lead developer.
If youâd like to try something for you data workflows thatâs vendor agnostic (k8s based) and open source you can check out our project: https://github.com/orchest/orchest
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Is there a good way to trigger data pipelines by event instead of cron?
You can find it here: https://github.com/orchest/orchest Convenience install script: https://github.com/orchest/orchest#installation
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How do you deal with parallelising parts of an ML pipeline especially on Python?
We automatically provide container level parallelism in Orchest: https://github.com/orchest/orchest
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Launch HN: Sematic (YC S22) â Open-source framework to build ML pipelines faster
For people in this thread interested in what this tool is an alternative to: Airflow, Luigi, Kubeflow, Kedro, Flyte, Metaflow, Sagemaker Pipelines, GCP Vertex Workbench, Azure Data Factory, Azure ML, Dagster, DVC, ClearML, Prefect, Pachyderm, and Orchest.
Disclaimer: author of Orchest https://github.com/orchest/orchest
QEMU
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QEMU Version 9.0.0 Released
My most-wanted QEMU feature: https://github.com/qemu/qemu/commit/a2260983c6553
Using `gic-version=3` on macOS you can now use more than 8 cores on ARM chips.
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Autoconf makes me think we stopped evolving too soon
A better solution is just to write a plain ass shell script that tests if various C snippets compile.
https://github.com/oilshell/oil/blob/master/configure
https://github.com/oilshell/oil/blob/master/build/detect-pwe...
Not an unholy mix of m4, shell, and C, all in the same file.
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These are the same style as a the configure scripts that Fabrice Bellard wrote for tcc and QEMU.
They are plain ass shell scripts, because he actually understands the code he writes.
https://github.com/qemu/qemu/blob/master/configure
https://github.com/TinyCC/tinycc/blob/mob/configure
OCamlâs configure script is also ânormalâ.
You donât have to copy and paste thousands of lines of GNU stuff that you donât understand.
(copy of lobste.rs comment)
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WASM Instructions
Related:
A fast Pascal (Delphi) WebAssembly interpreter:
https://github.com/marat1961/wasm
WASM-4:
https://github.com/aduros/wasm4
Curated list of awesome things regarding WebAssembly (wasm) ecosystem:
https://github.com/mbasso/awesome-wasm
Also, it would be nice if there was a WASM (soft) CPU for QEMU, which (if it existed!) would go here:
https://github.com/qemu/qemu/tree/master/target
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Revng translates (i386, x86-64, MIPS, ARM, AArch64, s390x) binaries to LLVM IR
> architectural registers are always updated
In tiny code, the guest registers (global TCG variables) are stored in the host's registers until you either call an helper which can access the CPU state or you return (`git grep la_global_sync`). This is the reason why QEMU is not so terribly slow.
But after a check, this also happens when you access the guest memory address space! https://github.com/qemu/qemu/blob/master/include/tcg/tcg-opc... (TCG_OPF_SIDE_EFFECTS is what matters)
But still, in the end, it's the same problem. What QEMU does, can be done in LLVM too. You could probably be more efficient in LLVM by using the exception handling mechanism (invoke and friends) to only serialize back to memory when there's an actual exception, at the cost of higher register pressure. More or less what we do here: https://rev.ng/downloads/bar-2019-paper.pdf
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State of x86-64 emulation of non-MacOS binaries
Um, in case you don't know, UTM (based on QEMU) is out for quite a while.
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Multipass: Ubuntu Virtual Machines Made Easy
Some of these tools include Oracle VM VirtualBox (that I've used since before the acquisition of Sun Microsystems by Oracle), VMWare Workstation Player, and QEMU, but last year, I found out about Multipass.
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Libsodium: A modern, portable, easy to use crypto library
For C/C++ projects that use meson as the build system, there is an excellent way to manage dependencies:
https://mesonbuild.com/Wrapdb-projects.html
https://mesonbuild.com/Wrap-dependency-system-manual.html
meson will download and build the libraries automatically and give you a variable which you pass as a regular dependency into the built target:
https://github.com/qemu/qemu/tree/005ad32358f12fe9313a4a0191...
https://github.com/harfbuzz/harfbuzz/tree/main/subprojects
https://github.com/harfbuzz/harfbuzz/blob/37457412b3212463c5...
Or, if you're using proper operating systems, they're managed by the usual package manager, just like everything else.
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Top 6 Virtual Machine Software in 2023
For all the users of the Linux platform, QEMU is the VM that you should go for. This software comes without any price tag and works as an emulator of various machines with utmost ease and completion; the software uses dynamic translations to emulate hardware peripherals and enhances its overall performance. If you are using QEMU as a virtualizer, then it will function exactly like the host system (provided you have the right set of hardware).
- Show HN: I'm 17 and wrote this guide on how CPUs run programs
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UTM for Developers
In this tutorial, we set up macOS and Windows virtual machines on UTM, a macOS application that provides a GUI wrapper for QEMU, a powerful open-source emulator and virtualizer. UTM allows you to easily manage and run virtual machines without memorizing complex commands. It also has special handling for macOS, making it simpler to install compared to other virtual machine software.
What are some alternatives?
docker-airflow - Docker Apache Airflow
UTM - Virtual machines for iOS and macOS
hookdeck-cli - Receive events (e.g. webhooks) in your development environment
TermuxArch - Experience the pleasure of the Linux command prompt in Android, Chromebook, Fire OS and Windows on smartphone, smartTV, tablet and wearable https://termuxarch.github.io/TermuxArch/
ploomber - The fastest âĄď¸ way to build data pipelines. Develop iteratively, deploy anywhere. âď¸
Unicorn Engine - Unicorn CPU emulator framework (ARM, AArch64, M68K, Mips, Sparc, PowerPC, RiscV, S390x, TriCore, X86)
n8n - Free and source-available fair-code licensed workflow automation tool. Easily automate tasks across different services.
Vagrant - Vagrant is a tool for building and distributing development environments.
label-studio - Label Studio is a multi-type data labeling and annotation tool with standardized output format
xemu - Original Xbox Emulator for Windows, macOS, and Linux (Active Development)
Node RED - Low-code programming for event-driven applications
em-dosbox - An Emscripten port of DOSBox