EfficientZero
DeepSpeed
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EfficientZero | DeepSpeed | |
---|---|---|
9 | 51 | |
825 | 32,550 | |
- | 3.2% | |
0.0 | 9.8 | |
4 months ago | 3 days ago | |
Python | Python | |
GNU General Public License v3.0 only | Apache License 2.0 |
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.
EfficientZero
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[D] GPT-3T: Can we train language models to think further ahead?
Here's an algorithm that is more sample efficient : https://github.com/YeWR/EfficientZero
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MuZero learns to play Teamfight Tactics
Use multiprocessing to have more GPU workers could help. My code based on EfficientZero https://github.com/YeWR/EfficientZero is utilizing CPUs and GPUs to 90%. It uses Ray for multiprocessing and splits Reanalyze into CPU and GPU workers to maximize resource utilization. By the way, it's not converging to optimal policy well: it gets stuck at 50% optimal episode return at with a small amount of training. Have you had this issue before?
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[R] Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning - Epochai Pablo Villalobos et al - Trend of ever-growing ML models might slow down if data efficiency is not drastically improved!
Found relevant code at https://github.com/YeWR/EfficientZero + all code implementations here
- Anyone found any working replication repo for MuZero?
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
Mastering Atari Games with Limited Data – EfficientZero ( Human sample -efficiency! ) Paper: https://arxiv.org/abs/2111.00210 Lesswrong article about the paper: https://www.lesswrong.com/posts/mRwJce3npmzbKfxws/efficientzero-how-it-works Github: https://github.com/YeWR/EfficientZero
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Waymo To Use Chinese Geely Robotaxi Body. This Should Send Shivers Into Western OEMs
Have you seen https://github.com/YeWR/EfficientZero EfficientZero yet? This agent is able to solve problems with unknown rules, where the agent starts only with information about the shape of the inputs and reward feedback. With superhuman ability - it needs less training data than humans do - and SoTA trumping results on the problems it has been tried on. (various atari/Go/chess/etc)
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Why does EfficientZero use SimSiam for temporal consistency instead of MAE / MSE?
Open-source codebase for EfficientZero - am I missing something or the repo is empty?
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[D] Paper Explained - EfficientZero: Mastering Atari Games with Limited Data (Full Video Analysis)
Code: https://github.com/YeWR/EfficientZero
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"EfficientZero: Mastering Atari Games with Limited Data", Ye et al 2021 (beating humans on ALE-100k/2h by adding self-supervised learning to MuZero-Reanalyze)
Code for https://arxiv.org/abs/2111.00210 found: https://github.com/YeWR/EfficientZero
DeepSpeed
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
DeepSpeed can handle parallelism concerns, and even offload data/model to RAM, or even NVMe (!?) . I'm surprised I don't see this project used more.
- [P][D] A100 is much slower than expected at low batch size for text generation
- DeepSpeed-FastGen: High-Throughput for LLMs via MII and DeepSpeed-Inference
- DeepSpeed-FastGen: High-Throughput Text Generation for LLMs
- Why async gradient update doesn't get popular in LLM community?
- DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models (r/MachineLearning)
- [P] DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
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A comprehensive guide to running Llama 2 locally
While on the surface, a 192GB Mac Studio seems like a great deal (it's not much more than a 48GB A6000!), there are several reasons why this might not be a good idea:
* I assume most people have never used llama.cpp Metal w/ large models. It will drop to CPU speeds whenever the context window is full: https://github.com/ggerganov/llama.cpp/issues/1730#issuecomm... - while sure this might be fixed in the future, it's been an issue since Metal support was added, and is a significant problem if you are actually trying to actually use it for inferencing. With 192GB of memory, you could probably run larger models w/o quantization, but I've never seen anyone post benchmarks of their experiences. Note that at that point, the limited memory bandwidth will be a big factor.
* If you are planning on using Apple Silicon for ML/training, I'd also be wary. There are multi-year long open bugs in PyTorch[1], and most major LLM libs like deepspeed, bitsandbytes, etc don't have Apple Silicon support[2][3].
You can see similar patterns w/ Stable Diffusion support [4][5] - support lagging by months, lots of problems and poor performance with inference, much less fine tuning. You can apply this to basically any ML application you want (srt, tts, video, etc)
Macs are fine to poke around with, but if you actually plan to do more than run a small LLM and say "neat", especially for a business, recommending a Mac for anyone getting started w/ ML workloads is a bad take. (In general, for anyone getting started, unless you're just burning budget, renting cloud GPU is going to be the best cost/perf, although on-prem/local obviously has other advantages.)
[1] https://github.com/pytorch/pytorch/issues?q=is%3Aissue+is%3A...
[2] https://github.com/microsoft/DeepSpeed/issues/1580
[3] https://github.com/TimDettmers/bitsandbytes/issues/485
[4] https://github.com/AUTOMATIC1111/stable-diffusion-webui/disc...
[5] https://forums.macrumors.com/threads/ai-generated-art-stable...
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Microsoft Research proposes new framework, LongMem, allowing for unlimited context length along with reduced GPU memory usage and faster inference speed. Code will be open-sourced
And https://github.com/microsoft/deepspeed
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April 2023
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)
What are some alternatives?
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
ColossalAI - Making large AI models cheaper, faster and more accessible
flash-attention-jax - Implementation of Flash Attention in Jax
Megatron-LM - Ongoing research training transformer models at scale
flash-attention - Fast and memory-efficient exact attention
fairscale - PyTorch extensions for high performance and large scale training.
RHO-Loss
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
CodeRL - This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
msn - Masked Siamese Networks for Label-Efficient Learning (https://arxiv.org/abs/2204.07141)
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.