fairseq
Flutter
fairseq | Flutter | |
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89 | 1,203 | |
29,262 | 161,934 | |
0.7% | 0.5% | |
6.0 | 10.0 | |
10 days ago | about 8 hours ago | |
Python | Dart | |
MIT License | BSD 3-clause "New" or "Revised" License |
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fairseq
- Sequence-to-Sequence Toolkit Written in Python
- Unsupervised (Semi-Supervised) ASR/STT training recipes
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Nvidia's 900 tons of GPU muscle bulks up server market, slims down wallets
> 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.
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Talk back and forth with AI like you would with a person
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.
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Voice generation AI (TTS)
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
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Translation app with TTS (text-to-speech) for Persian?
They have instructions on how to use it in command line and a notebook on how to use it as a python library.
- Why no work on open source TTS (Text to speech) models
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Meta's Massively Multilingual Speech project supports 1k languages using self supervised learning
Github - https://github.com/facebookresearch/fairseq/tree/main/examples/mms Paper - https://research.facebook.com/publications/scaling-speech-technology-to-1000-languages/
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AI — weekly megathread!
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].
- Meta's MMS: Scaling Speech Technology to 1000+ languages (How to Run colab)
Flutter
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Show HN: Shorebird 1.0, Flutter Code Push
[3]: https://github.com/flutter/flutter/tree/master/packages/flut...
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3D and 2D: Testing out my cross-platform graphics engine
Thanks - that link does not appear to be open access, anyways I don't think I've seen it. I'm familiar with Flutter at a high-level (Kevin Moore gave a great talk on it at Wasm I/O), and I think other than requiring users to work in Dart, it is probably one of the most powerful ways to do cross-platform UI today.
Worth noting that their original GPU backend was Skia, and now they are retooling around Flutter GPU (Impeller)[0], which is kind of designed similarly as an abstract rendering interface over platform-specific GPU APIs.
[0]https://github.com/flutter/flutter/wiki/Flutter-GPU
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Python dev considering Electron vs. Kivy for desktop app UI
If you are considering Electron/React then I would suggest adding Flutter to your list of technologies to consider. It uses Dart (a language similar to C#) and has a lot going for it… relatively quick to get up to speed with, fantastic developer experience (e.g., hot reload, great IDE support, good development tools) and very strong cross-platform support: it generates native iOS, Android, MacOS, Windows and Linux executables. Check it out: https://flutter.dev/
- Lançamento do App Edudu
- Android 12+: Changing wallpaper or dark theme breaks Flutter and Jetpack Apps
- Android 12: Changing wallpaper or dark theme breaks Flutter and Jetpack Compose
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React Native and Flutter: A Developer's Dilemma
You can find the React Native documentation here and Flutter Documentation here.
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Ente: Open-Source, E2E Encrypted, Google Photos Alternative
[1]https://github.com/flutter/flutter/issues/55092#issuecomment...
- Reusing state logic is either too verbose or too difficult #51752
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React Labs: What We've Been Working On – February 2024 – React Compiler
> There is actually a great issue thread on the Flutter GitHub that explains exactly why other solutions do not work correctly when compared to hooks [0]
Interesting. I assume you are referring to this comment in particular -> https://github.com/flutter/flutter/issues/51752#issuecomment... ?
What are some alternatives?
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
Introducing .NET Multi-platform App UI (MAUI) - .NET MAUI is the .NET Multi-platform App UI, a framework for building native device applications spanning mobile, tablet, and desktop.
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
flet - Flet enables developers to easily build realtime web, mobile and desktop apps in Python. No frontend experience required.
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
WPF - WPF is a .NET Core UI framework for building Windows desktop applications.
text-to-text-transfer-transformer - Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"
Uno Platform - Build Mobile, Desktop and WebAssembly apps with C# and XAML. Today. Open source and professionally supported.
espnet - End-to-End Speech Processing Toolkit
kivy - Open source UI framework written in Python, running on Windows, Linux, macOS, Android and iOS
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Quasar Framework - Quasar Framework - Build high-performance VueJS user interfaces in record time