963a5c8d79
* test * test * fix last kv cache * add xpu readme * remove numactl for xpu example * fix link error * update max_num_batched_tokens logic * add explaination * add xpu environement version requirement * refine gpu memory * fix * fix style
28 lines
1.7 KiB
Markdown
28 lines
1.7 KiB
Markdown
# BigDL-LLM Examples on Intel GPU
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This folder contains examples of running BigDL-LLM on Intel GPU:
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- [HF-Transformers-AutoModels](HF-Transformers-AutoModels): running any ***Hugging Face Transformers*** model on BigDL-LLM (using the standard AutoModel APIs)
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- [QLoRA-FineTuning](QLoRA-FineTuning): running ***QLoRA finetuning*** using BigDL-LLM on Intel GPUs
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- [vLLM-Serving](vLLM-Serving): running ***vLLM*** serving framework on intel GPUs (with BigDL-LLM low-bit optimized models)
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- [Deepspeed-AutoTP](Deepspeed-AutoTP): running distributed inference using ***DeepSpeed AutoTP*** (with BigDL-LLM low-bit optimized models) on Intel GPUs
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- [PyTorch-Models](PyTorch-Models): running any PyTorch model on BigDL-LLM (with "one-line code change")
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## System Support
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**Hardware**:
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- Intel Arc™ A-Series Graphics
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- Intel Data Center GPU Flex Series
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- Intel Data Center GPU Max Series
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**Operating System**:
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- Ubuntu 20.04 or later (Ubuntu 22.04 is preferred)
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## Requirements
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To apply Intel GPU acceleration, there’re several steps for tools installation and environment preparation.
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Step 1, please refer to our [driver installation](https://dgpu-docs.intel.com/driver/installation.html) for general purpose GPU capabilities.
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> **Note**: IPEX 2.0.110+xpu requires Intel GPU Driver version is [Stable 647.21](https://dgpu-docs.intel.com/releases/stable_647_21_20230714.html).
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Step 2, you also need to download and install [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html). OneMKL and DPC++ compiler are needed, others are optional.
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> **Note**: IPEX 2.0.110+xpu requires Intel® oneAPI Base Toolkit's version >= 2023.2.0.
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