determined VS QEMU

17. Determined AI | Github | tutorial

Use Determined if you want a nice UI https://github.com/determined-ai/determined#readme

I looked up and found [Determined Platform](determined.ai), tho it looks a very young project that I don't know if it's reliable enough.

- Developer Support Engineer (~1/3 client facing, triaging feature requests and bug reports, etc; 2/3 debugging/troubleshooting)

We are developing enterprise grade artificial intelligence products/services for AI engineering teams and fortune 500 companies and need more software devs to fill the increasing demand.

Find out more at https://determined.ai/. If AI piques your curiosity or you want to interface with highly skilled engineers in the community, apply within (search "determined ai" at careers.hpe.com and drop me a message at asnell AT hpe PERIOD com).

Check out Determined https://github.com/determined-ai/determined to help manage this kind of work at scale: Determined leverages Horovod under the hood, automatically manages cloud resources and can get you up on spot instances, T4's, etc. and will work on your local cluster as well. Gives you additional features like experiment management, scheduling, profiling, model registry, advanced hyperparameter tuning, etc.

Full disclosure: I'm a founder of the project.

These are some of the problems we are trying to solve with the Determined training platform. Determined can be run with or without k8s - the k8s version inherits some of the scheduling problems of k8s, but the non-k8s version uses a custom gang scheduler designed for large scale ML training. Determined offers a priority scheduler that allows smaller jobs to run while being able to schedule a large distributed job whenever you need, by setting a higher priority.

Ah I see - I think we're pretty much on the same page in terms of timetables. Although if you include TPU, I think it's fair to say that custom accelerators are already a moderate success.

Updated my profile. I've been working on DL training platforms and distributed training benchmarking for a bit so I've gotten a nice view into the GPU/TPU battle.

Shameless plug: you should check out the open-source training platform we are building, Determined[1]. One of the goals is to take our hard-earned expertise on training infrastructure and build a tool where people don't need to have that infrastructure expertise. We don't support TPUs, partially because a lack of demand/TPU availability, and partially because our PyTorch TPU experiments were so unimpressive.

[1] GH: https://github.com/determined-ai/determined, Slack: https://join.slack.com/t/determined-community/shared_invite/...

Take a look at Determined https://github.com/determined-ai/determined

determined is an open-source deep learning training platform that makes building models fast and easy. This project provides a CloudFormation template to bootstrap you into AWS and then has a number of tutorials covering how to manage your data, train and then deploy inference endpoints. If you are looking to explore more open source machine learning projects, then check this one out.

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.

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.

---

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

OCaml’s configure script is also “normal”.

You don’t have to copy and paste thousands of lines of GNU stuff that you don’t understand.

(copy of lobste.rs comment)

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

> 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

Um, in case you don't know, UTM (based on QEMU) is out for quite a while.

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.

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.

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).

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.