booster VS determined

People should stop using plain passwords to protect data and switch to something better.oster/ + Yubikey

Change from mkinitcpio to booster. It's faster and produce smaller images.

Neither, use booster.

Booster is an initramfs - a type of software that runs early during the boot process and helps to setup system, e.g. perform disk unlock, load extra drivers, setup ZFS filesystem, and so on.

I don't have high hopes that this will change in mkinitcpio, but perhaps you could bring it up with booster (which copied the mkinitcpio hooks and removal scripts)

This is my personal preference, but consider switching to booster.

initramfs: booster - TPM2 support, way faster than mkinitcpio, autodiscovering root partition

Note I'm using booster, so assuming you're using the default mkinitcpio you probably want /boot/initramfs-linux.img. Also, don't forget to modify if your ESP isn't mounted at /boot like mine.

Booster initramfs generator supports TPM, Yubikey and Network binding so you can easily protect your data using the strategy you want.

GPT table detection has higher priority at booster code. But for some reason it does not identify your partition table as GPT. Filed a github bug for it https://github.com/anatol/booster/issues/119

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.