use official docker image (#10)
Signed-off-by: lvliang-intel <liang1.lv@intel.com>
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@@ -3,7 +3,16 @@ This ChatQnA use case performs RAG using LangChain, Redis vectordb and Text Gene
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# Environment Setup
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To use [🤗 text-generation-inference](https://github.com/huggingface/text-generation-inference) on Habana Gaudi/Gaudi2, please follow these steps:
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## Build TGI Gaudi Docker Image
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## Prepare Docker
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Getting started is straightforward with the official Docker container. Simply pull the image using:
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```bash
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docker pull ghcr.io/huggingface/tgi-gaudi:1.2.1
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```
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Alternatively, you can build the Docker image yourself with:
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```bash
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bash ./serving/tgi_gaudi/build_docker.sh
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```
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@@ -27,9 +27,9 @@ volume=$PWD/data
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# Build the Docker run command based on the number of cards
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if [ "$num_cards" -eq 1 ]; then
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docker_cmd="docker run -p $port_number:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy tgi_gaudi --model-id $model_name"
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docker_cmd="docker run -p $port_number:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy ghcr.io/huggingface/tgi-gaudi --model-id $model_name"
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else
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docker_cmd="docker run -p $port_number:80 -v $volume:/data --runtime=habana -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy tgi_gaudi --model-id $model_name --sharded true --num-shard $num_cards"
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docker_cmd="docker run -p $port_number:80 -v $volume:/data --runtime=habana -e PT_HPU_ENABLE_LAZY_COLLECTIVES=true -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --ipc=host -e HTTPS_PROXY=$https_proxy -e HTTP_PROXY=$https_proxy ghcr.io/huggingface/tgi-gaudi --model-id $model_name --sharded true --num-shard $num_cards"
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fi
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# Execute the Docker run command
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