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Comment by djleni | Hacker News Reader

Software developer that knows nothing about ML: What would it take to mess around with this?

From a skim of the README it seems like install the package, and then find some training dataset, and then ??? to use it.

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Well you most likely need a CUDA compatible GPU with a non-negligible amount of vram to load the model. And a few hours of your time to mash your head at the keyboard in frustration because of the inevitable dependencies that won't want to resolve properly.

Most "open source" models don't actully upload the final models either, just the code they used to train them (not sure if this is the case here), so the next step after that is downloading 100G of data and running your workstation for a few days to train the actual model first.

I recall at one point I attempted to run BERT or GPT-J or one of those lighter language models locally, and it was going well until I realized I needed a 24G of vRAM to even load the model hah.

Do CUDA AI projects not containerize well? I understand that AI projects often have many, many dependencies, but this seems like something containers ("docker") would be ideally suited for. Something like `docker pull dall-e` and `docker run -ie -n dalle dall-e` probably volume mount in a local folder? I'm curious what the hurdles are here for packaging something like this.

They do containerize but it’s not enough. You still need the right drivers and CUDA stuff installed on the docker host, which can be finicky to setup. Not to mention figuring out how to get docker to actually pass the GPU to the container.

I see this has not gotten any better in the last 5 years. For future reference, if you as a dev are interacting with a product that is this hard to use and there is no other option but to use it (CUDA), you should buy their stock.

I'd much rather look out for competitors.

Resting on laurels won't last. If a company stops improving its offer, a competitor might catch up.

The competition has even more broken software (AMD). At least Nvidia's works when you need it to.

I think OpenCL support has been getting better, and it may be possible to run a lot of models with it, but that just doubles your already frustrating amount of hours trying to set the damn thing up.

> Not to mention figuring out how to get docker to actually pass the GPU to the container.

Should just be passing `--gpus all` these days, shouldn't it?

> I'm curious what the hurdles are here for packaging something like this.

No hurdle, it's just that Docker is not part of the standard toolkit of ML people. ML people all use conda (or virtualenv etc.) which already solves most of the dependency problems, making learning Docker not especially appealing.

But virtually all training/inference platform (including the ones used by OpenAI) are using docker. It's not a technical limitation.

No, ML people use Docker to deploy their models. Nobody got time or patience to replicate environments for inference.

Why not have some ML-rendition of BitTorrent designed for sharing pre-trained models? At least then you're not paying for AWS egress ha

That's not the issue. You can already download weights of other models through BitTorrent or from large archive sites like Hugging Face or the-eye. OpenAI just doesn't want to release them, so that they can sell access to it through their cloud service.

An older, but similar and still impressive alternative is available here: https://github.com/CompVis/latent-diffusion

If you have a decent amount of VRAM, you can use it to start generating images with their pre-trained models. They're nowhere near as impressive as DALL-E 2, but they're still pretty damn cool. I don't know what the exact memory requirements are, but I've gotten it to run on a 1080 TI with 11gb.

EDIT: I also tried a 980 with 4GB of RAM a while back, but that failed...so you probably need more than that.

This uses several models. One of them is already trained and released, which you can use or you can train it too yourself. The other models need training. Training means having a labelled dataset and let the model run several iterations until the loss function has a good value in order to use the model. This code is quite complex as it is not one model but several ones and also you can make some changes to generate bigger images and to use better models for some parts. Once you have everything trained (you might need a GPU or a cluster of GPUs to train it for several days/weeks/months), you can use it.

I know this wont help you very much. But, either you are willing to spend a lot of time messing around with the code and have access to a good hardware to train the models, or you would have to wait until someone releases something already trained.

> Once you have everything trained (you might need a GPU or a cluster of GPUs to train it for several days/weeks/months), you can use it.

It takes a whole team to replicate an advanced model. Take a look at our open source working groups: Eleuther, LAION, BigScience. They worked for months for one release and burned millions of dollars on GPU (gracefully donated by well meaning sponsors).

https://www.eleuther.ai/

https://laion.ai/

The BigScience group is massive:

https://bigscience.notion.site/10743770aae24ff3bdc1b938cf454...

I didn't even know anything like those groups existed. That's very helpful. Thank you!

The readme says they're working on a command line interface (CLI). You might want to wait for that if you don't want to learn PyTorch.

Once that's done it might be as simple as: install the package, and then find some training dataset, and then run the training CLI (for days or weeks or more), and then run the image generation CLI. Or even: just download an already trained model and use the image generation CLI immediately.

It looks like big-sleep [1] is their CLI? I did:

    pip install big-sleep
    dream "a pyramid made of basketballs"

and after a few hours got this: https://i.imgur.com/FxdfdmV.png

Not nearly as cool as the real DALL-e, but maybe I'm missing something.

[1] https://github.com/lucidrains/big-sleep

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