DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. (by microsoft)
Megatron-LM
Ongoing research training transformer models at scale (by NVIDIA)
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| DeepSpeed | Megatron-LM | |
|---|---|---|
| 41 | 14 | |
| 25,088 | 5,137 | |
| 61.0% | 16.4% | |
| 9.6 | 6.1 | |
| 2 days ago | 5 days ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DeepSpeed
Posts with mentions or reviews of DeepSpeed.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-11.
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Using --deepspeed requires lots of manual tweaking
Filed a discussion item on the deepspeed project: https://github.com/microsoft/DeepSpeed/discussions/3531
Solution: I don't know; this is where I am stuck. https://github.com/microsoft/DeepSpeed/issues/1037 suggests that I just need to 'apt install libaio-dev', but I've done that and it doesn't help.
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Whether the ML computation engineering expertise will be valuable, is the question.
There could be some spectrum of this expertise. For instance, https://github.com/NVIDIA/FasterTransformer, https://github.com/microsoft/DeepSpeed
- FLiPN-FLaNK Stack Weekly for 17 April 2023
- DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-Like Models
- DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-Like Models
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12-Apr-2023 AI Summary
DeepSpeed Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales (https://github.com/microsoft/DeepSpeed/tree/master/blogs/deepspeed-chat)
- Microsoft DeepSpeed
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Apple: Transformer architecture optimized for Apple Silicon
I'm following this closely, together with other efforts like GPTQ Quantization and Microsoft's DeepSpeed, all of which are bringing down the hardware requirements of these advanced AI models.
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Facebook LLAMA is being openly distributed via torrents
- https://github.com/microsoft/DeepSpeed
Anything that could bring this to a 10GB 3080 or 24GB 3090 without 60s/it per token?
Megatron-LM
Posts with mentions or reviews of Megatron-LM.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-26.
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Why Did Google Brain Exist?
GPU cluster scaling has come a long way. Just checkout the scaling plot here: https://github.com/NVIDIA/Megatron-LM
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I asked ChatGPT to rate the intelligence level of current AI systems out there.
Google's PaLM, Facebook's LLaMA, Nvidia's Megatron, I am missing some surely and Apple sure has something cooking as well but these are the big ones, of course none of them are publicly available, but research papers are reputable. All of the ones mentioned should beat GPT-3 although GPT-3.5 (chatGPT) should be bit better and ability to search (Bing) should level the playing field even further, but Google's PaLM with search functionality should be clearly ahead. This is why people are excited about GPT-4, GPT-3 was way ahead of anyone else when it came out but others were able to catch up since, we'll see if GPT-4 will be another bing jump among LLMs.
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Nvidia Fiscal Q3 2022 Financial Result
Described a collaboration involving NVIDIA Megatron-LM and Microsoft DeepSpeed to create an efficient, scalable, 3D parallel system capable of combining data, pipeline and tensor-slicing-based parallelism.
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Microsoft and NVIDIA AI Introduces MT-NLG: The Largest and Most Powerful Monolithic Transformer Language NLP Model
Microsoft and NVIDIA present the Megatron-Turing Natural Language Generation model (MT-NLG), powered by DeepSpeed and Megatron, the largest and robust monolithic transformer language model trained with 530 billion parameters.
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[R] Data Movement Is All You Need: A Case Study on Optimizing Transformers
Nvidia's own implementation of Transformers, i.e, Megatron on NVIDIA's Selene supercomputer (where GPT-3 is possible too) -https://github.com/NVIDIA/Megatron-LM
What are some alternatives?
When comparing DeepSpeed and Megatron-LM you can also consider the following projects:
ColossalAI - Making large AI models cheaper, faster and more accessible
fairscale - PyTorch extensions for high performance and large scale training.
TensorRT - NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for inference applications.
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
mesh-transformer-jax - Model parallel transformers in JAX and Haiku
llama - Inference code for LLaMA models
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
text-generation-webui - A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
Finetune_LLMs - Repo for fine-tuning GPTJ and other GPT models
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration