refine LLM containers

docker/llm/finetune/lora/cpu/docker/README.md

@@ -3,7 +3,7 @@
 You can download directly from Dockerhub like:
 
 ```bash
-docker pull intelanalytics/bigdl-llm-finetune-cpu:2.4.0-SNAPSHOT
+docker pull intelanalytics/bigdl-llm-finetune-lora-cpu:2.4.0-SNAPSHOT
 ```
 
 Or build the image from source:
@@ -15,6 +15,6 @@ export HTTPS_PROXY=your_https_proxy
 docker build \
   --build-arg http_proxy=${HTTP_PROXY} \
   --build-arg https_proxy=${HTTPS_PROXY} \
-  -t intelanalytics/bigdl-llm-finetune-cpu:2.4.0-SNAPSHOT \
+  -t intelanalytics/bigdl-llm-finetune-lora-cpu:2.4.0-SNAPSHOT \
   -f ./Dockerfile .
 ```

docker/llm/finetune/lora/cpu/kubernetes/values.yaml

@@ -1,4 +1,4 @@
-imageName: intelanalytics/bigdl-llm-finetune-cpu:2.4.0-SNAPSHOT
+imageName: intelanalytics/bigdl-llm-finetune-lora-cpu:2.4.0-SNAPSHOT
 trainerNum: 8
 microBatchSize: 8
 nfsServerIp: your_nfs_server_ip

docker/llm/finetune/qlora/xpu/docker/README.md

@@ -28,14 +28,18 @@ docker build \
 Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [English Quotes](https://huggingface.co/datasets/Abirate/english_quotes) dataset, and please download them and start a docker container with files mounted like below:
 
 ```bash
-export BASE_MODE_PATH=<your_downloaded_base_model_path>
-export DATA_PATH=<your_downloaded_data_path>
+export BASE_MODE_PATH=your_downloaded_base_model_path
+export DATA_PATH=your_downloaded_data_path
+export HTTP_PROXY=your_http_proxy
+export HTTPS_PROXY=your_https_proxy
 
 docker run -itd \
    --net=host \
    --device=/dev/dri \
    --memory="32G" \
    --name=bigdl-llm-fintune-qlora-xpu \
+   -e http_proxy=${HTTP_PROXY} \
+   -e https_proxy=${HTTPS_PROXY} \
    -v $BASE_MODE_PATH:/model \
    -v $DATA_PATH:/data/english_quotes \
    --shm-size="16g" \
@@ -45,11 +49,16 @@ docker run -itd \
 The download and mount of base model and data to a docker container demonstrates a standard fine-tuning process. You can skip this step for a quick start, and in this way, the fine-tuning codes will automatically download the needed files:
 
 ```bash
+export HTTP_PROXY=your_http_proxy
+export HTTPS_PROXY=your_https_proxy
+
 docker run -itd \
    --net=host \
    --device=/dev/dri \
    --memory="32G" \
    --name=bigdl-llm-fintune-qlora-xpu \
+   -e http_proxy=${HTTP_PROXY} \
+   -e https_proxy=${HTTPS_PROXY} \
    --shm-size="16g" \
    intelanalytics/bigdl-llm-fintune-qlora-xpu:2.4.0-SNAPSHOT
 ```

docker/llm/inference/xpu/docker/Dockerfile

@@ -8,7 +8,9 @@ ENV TZ=Asia/Shanghai
 # Disable pip's cache behavior
 ARG PIP_NO_CACHE_DIR=false
 
-RUN apt-get update && \
+RUN curl -fsSL https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2023.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \
+    echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " > /etc/apt/sources.list.d/oneAPI.list && \
+    apt-get update && \
     apt-get install -y curl wget git gnupg gpg-agent && \
     wget -qO - https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg && \
     echo 'deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc' | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \