accelerate
stoke
| accelerate | stoke | |
|---|---|---|
| 18 | 2 | |
| 6,996 | 63 | |
| 2.9% | - | |
| 9.7 | 0.0 | |
| 1 day ago | 9 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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accelerate
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Can we discuss MLOps, Deployment, Optimizations, and Speed?
accelerate is a best-in-class lib for deploying models, especially across multi-gpu and multi-node.
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Code Llama - The Hugging Face Edition
In the coming days, we'll work on sharing scripts to train models, optimizations for on-device inference, even nicer demos (and for more powerful models), and more. Feel free to like our GitHub repos (transformers, peft, accelerate). Enjoy!
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What are the current fastest multi-gpu inference frameworks?
So I rent a cloud server today to try out some of the recent LLMs like falcon and vicuna. I started with huggingface's generate API using accelerate. It got about 2 instances/s with 8 A100 40GB GPUs which I think is a bit slow. I was using batch size = 1 since I do not know how to do multi-batch inference using the .generate API. I did torch.compile + bf16 already. Do we have an even faster multi-gpu inference framework? I have 8 GPUs so I was thinking about MUCH faster speed like ~10 or 20 instances per second (or is it possible at all? I am pretty new to this field).
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Looking at lefnire's suggestion of splitting huggingface batches per gradient_accumulation_steps
Looking through https://github.com/huggingface/accelerate/tree/main/src/accelerate/utils/ I think it might be feasible, but will require some modifications to:
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Have to abandon my (almost) finished LLaMA-API-Inference server. If anybody finds it useful and wants to continue, the repo is yours. :)
As /u/RabbitHole32 already mentioned, the speed increase stems from a patch which modifies, how a certain, large tensor is distributed between the GPU's. The patch was created by /u/emvw7yf. Here you can find the respective GitHub issue: https://github.com/huggingface/accelerate/issues/1394
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Help please! SD installation broken
::pip install git+https://github.com/huggingface/accelerate
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Batch Controlnet
pip install controlnet_aux pip install diffusers transformers git+https://github.com/huggingface/accelerate.git
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[D] Large Language Models feasible to run on 32GB RAM / 8 GB VRAM / 24GB VRAM
Try to use both GPUs with this one: https://github.com/huggingface/accelerate https://huggingface.co/docs/accelerate/usage_guides/big_modeling https://huggingface.co/blog/accelerate-large-models Maybe it will help (the last link is clearer IMHO).
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Fine Tuning Stable Diffusion with Dreambooth from Within My Python Code
I read through this page on accelerate, but it's not clear to me how the arguments such as instance_prompt gets passed in.
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What does ACCELERATE do in AUTOMATIC1111?
To activate it you have to uncomment webui-user.sh line 44 and adding set ACCELERATE="True" to webui-user.bat. It seems to use huggingface/accelerate (Microsoft DeepSpeed, ZeRO paper) ACCELERATE
stoke
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
bitsandbytes - Accessible large language models via k-bit quantization for PyTorch.
FlexGen - Running large language models like OPT-175B/GPT-3 on a single GPU. Focusing on high-throughput generation. [Moved to: https://github.com/FMInference/FlexGen]
ChatGLM-6B - ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型
unsloth - Finetune Llama 3, Mistral & Gemma LLMs 2-5x faster with 80% less memory
stable-diffusion-webui - Stable Diffusion web UI
llamazoo - Large Model Collider - The Platform for serving LLM models [Moved to: https://github.com/gotzmann/collider]
TALIS - Simple and fast server for GPTQ-quantized LLaMA inference
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
rwkvstic - Framework agnostic python runtime for RWKV models