fairseq VS ohmyzsh

> Is there really no way to partition the workload to run with 16gb memory per card?

It really depends and this can get really complicated really fast. I'll give a tldr and then a longer explanation.

TLDR:

Yes, you can easily split networks up. If your main bottleneck is batch size (i.e. training) then there aren't huge differences in spreading across multiple GPUs assuming you have good interconnects (GPU direct is supported). If you're running inference and the model fits on the card you're probably fine too unless you need to do things like fancy inference batching (i.e. you have LOTS of users)

Longer version:

You can always split things up. If we think about networks we recognize some nice properties about how they operate as mathematical groups. Non-residual networks are compositional, meaning each layer can be treated as a sub network (every residual block can be treated this way too). Additionally, we may have associative and distributive properties depending on the architecture (some even have commutative!). So we can use these same rules to break apart networks in many different ways. There are often performance hits for doing this though, as it practically requires you touching the disk more often but in some more rare cases (at least to me, let me know if you know more) they can help.

I mentioned the batching above and this can get kinda complicated. There are actually performance differences when you batch in groups of data (i.e. across GPUs) compared to batching on a single accelerator. This difference isn't talked about a lot. But it is going to come down to how often your algorithm depends on batching and what operations are used, such as batch norm. The batch norm is calculated across the GPU's batch, not the distributed batch (unless you introduce blocking). This is because your gradients AND inference are going to be computed differently. In DDP your whole network is cloned across cards so you basically run inference on multiple networks and then do an all reduce on the loss then calculate the gradient and then recopy the weights to all cards. There is even a bigger difference when you use lazy regularization (don't compute gradients for n-minibatches). GANs are notorious for using this and personally I've seen large benefits to distributed training for these. GANs usually have small batch sizes and aren't getting anywhere near the memory of the card anyways (GANs are typically unstable so large batch sizes can harm them), but also pay attention to this when evaluating papers (of course as well as how much hyper-parameter tuning has been done. This is always tricky when comparing works, especially between academia and big labs. You can easily be fooled by which is a better model. Evaluating models is way tougher than people give credit to and especially in the modern era of LLMs. I could rant a lot about just this alone). Basically in short, we can think of this as an ensembling method, except our models are actually identical (you could parallel reduce lazily too and that will create some periodic divergence between your models but that's not important for conceptually understanding, just worth noting).

There is are also techniques to split a single model up called model sharding and checkpointing. Model sharding is where you split a single model across multiple GPUs. You're taking advantage of the compositional property of networks, meaning that as long as there isn't a residual layer between your split location you can actually treat one network as a series of smaller networks. This has obvious drawbacks as you need to feed one into another and so the operations have to be synchronous, but sometimes this isn't too bad. Checkpointing is very similar but you're just doing the same thing on the same GPU. Your hit here is in I/O, but may or may not be too bad with GPU Direct and highly depends on your model size (were you splitting because batch size or because model size?).

This is all still pretty high level but if you want to dig into it more META developed a toolkit called fairseq that will do a lot of this for you and they optimized it

https://engineering.fb.com/2021/07/15/open-source/fsdp/

https://github.com/facebookresearch/fairseq

TLDR: really depends on your use case, but it is a good question.

How do they do the text to voice conversion so fast? https://github.com/facebookresearch/fairseq/tree/main (open source takes sub-minute to do text to voice.

It might be worth checking out Meta's TTS tho, I haven't gotten the chance to fiddle around with it but it looks somewhat promising https://github.com/facebookresearch/fairseq/tree/main/examples/mms

They have instructions on how to use it in command line and a notebook on how to use it as a python library.

Github - https://github.com/facebookresearch/fairseq/tree/main/examples/mms Paper - https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/

Meta released a new open-source model, Massively Multilingual Speech (MMS) that can do both speech-to-text and text-to-speech in 1,107 languages and can also recognize 4,000+ spoken languages. Existing speech recognition models only cover approximately 100 languages out of the 7,000+ known spoken languages. [Details | Research Paper | GitHub].

For Linux users, your default terminal is just fine. The only thing I would install is oh-my-zsh with the autocomplete plugin. For my Mac friends out there, iTerm is an amazing software that works well with oh-my-zsh as well.

If you are not using oh-my-zsh, you are missing out on some amazing plugins. One feature most people wish the terminal had is autocompletion. With the zsh-autosuggestions plugin, your terminal will autocomplete most commands and remember previous ones.

That’s the minimum terminal setup. You can modify the look and add plugins such as autocompletion to your terminal by installing ohmyzsh and using themes such as powerlevel10k. I am already using them.

Somewhat related is "Oh My ZSH!" which is basically zsh on steroids, it's always one of the first things I install on a new computer. It gives things like new colors, themes, plugins, and more. Highly recommend you check it out.

https://ohmyz.sh/

I had just gone through a fun tutorial for setting up oh-my-zsh with a nice color scheme from iterm2colorschemes.com and a decent prompt and I was wondering: can I make my oblique strategy look nice? how can you actually use the colors from your scheme in the output in your cli?

After this you going to visit Oh-My-Zsh which is where the magic will happen.

after automating my dotfiles, I want to automate my installations, after that I want to make my terminal easier to use so I add OMZ with many plugins, after that, I try to automate the backup of my setting on my Gnome but failed, then try using git-lfs for my big files but it turned out to be idiotic moves, bla bla bla many try and fail.