goofys VS determined

The author needs to ask themselves: in this cloud technology stack, is there POSIX involved somewhere lower down, where I can't access it? The answer is, of course, "yes". The sort of cloud storage systems described all run on top of POSIX APIs. They provide convenience (cost efficiency is more debatable) compared to the POSIX alternative, but that's because they exist at an entirely different conceptual layer (hence the presence of POSIX anyway, just buried).

Your point about surfacing a POSIX that's actually there but hidden and thus visible to low-level Amazon employees building the S3 service which makes it invisible to S3 end customers is true but isn't the the point of the article. The author is saying there are motivations for a POSIX-like api visible also the end user.

So your explanation of stack looks like 2 layers: POSIX api <-- AWS S3 built on top of that

Author's essay is actually talking about 3 layers: POSIX <-- AWS S3 <-- POSIX

That's why the blog post has the following links to POSIX-on-top-of-S3-objects :

https://github.com/s3fs-fuse/s3fs-fuse

https://github.com/kahing/goofys

https://www.cuno.io/

How is this different than these other solutions?

https://github.com/kahing/goofys

https://github.com/s3fs-fuse/s3fs-fuse

But now I ask.. why not s3fs? Is it the GPL licensing? Or even goofys that also have Apache2 licensing and seems to hit similar goals (non fully POSIX compliant)? Why build your own?

If your 20TB is in S3, use a staging box with goofys (https://github.com/kahing/goofys) to mount the commercial S3 bucket(s) into a folder, then use s3 sync to copy to your bucket(s) in GovCloud.

So, we just need to change the dataloader function a bit to make this work then. Did you try just mounting S3 using https://github.com/kahing/goofys. In this case, we need not even change the dataloader code. Not sure of the performance though.

We're using goofys as the mounting utility. It's a "high-performance, POSIX-ish Amazon S3 file system written in Go" based on FUSE (file system in user space) technology.

will probably experiment with https://github.com/kahing/goofys and https://litestream.io/ to make services more easily moved between the devices :) Also, will continue working on https://synpse.net/ to make the operations easier.

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.