# Getting started with BigDL-LLM in Docker

### Index
- [Docker installation guide for BigDL-LLM on CPU](#docker-installation-guide-for-bigdl-llm-on-cpu)
    - [BigDL-LLM on Windows](#bigdl-llm-on-windows)
    - [BigDL-LLM on Linux/MacOS](#bigdl-llm-on-linuxmacos)
- [Docker installation guide for BigDL LLM on XPU](#docker-installation-guide-for-bigdl-llm-on-xpu) 
- [Docker installation guide for BigDL LLM Serving on CPU](#docker-installation-guide-for-bigdl-llm-serving-on-cpu) 
- [Docker installation guide for BigDL LLM Serving on XPU](#docker-installation-guide-for-bigdl-llm-serving-on-xpu) 
- [Docker installation guide for BigDL LLM Fine Tuning on CPU](#docker-installation-guide-for-bigdl-llm-fine-tuning-on-cpu) 
- [Docker installation guide for BigDL LLM Fine Tuning on XPU](#docker-installation-guide-for-bigdl-llm-fine-tuning-on-xpu) 

## Docker installation guide for BigDL-LLM on CPU

### BigDL-LLM on Windows

#### Install docker

New users can quickly get started with Docker using this [official link](https://www.docker.com/get-started/).

For Windows users, make sure WSL2 or Hyper-V is enabled on your computer. 
The instructions for installing can be accessed from 
[here](https://docs.docker.com/desktop/install/windows-install/).


#### Pull bigdl-llm-cpu image

To pull image from hub, you can execute command on console:
```bash
docker pull intelanalytics/bigdl-llm-cpu:2.5.0-SNAPSHOT
```
to check if the image is successfully downloaded, you can use:
```powershell
docker images | sls intelanalytics/bigdl-llm-cpu:2.5.0-SNAPSHOT
```


#### Start bigdl-llm-cpu container

To run the image and do inference, you could create and run a bat script on Windows.

An example on Windows could be:
```bat
@echo off
set DOCKER_IMAGE=intelanalytics/bigdl-llm-cpu:2.5.0-SNAPSHOT
set CONTAINER_NAME=my_container
set MODEL_PATH=D:/llm/models[change to your model path]

:: Run the Docker container
docker run -itd ^
    -p 12345:12345 ^
    --cpuset-cpus="0-7" ^
    --cpuset-mems="0" ^
    --memory="8G" ^
    --name=%CONTAINER_NAME% ^
    -v %MODEL_PATH%:/llm/models ^
    %DOCKER_IMAGE%
```

After the container is booted, you could get into the container through `docker exec`.
```
docker exec -it my_container bash
```

To run inference using `BigDL-LLM` using cpu, you could refer to this [documentation](https://github.com/intel-analytics/BigDL/tree/main/python/llm#cpu-int4).


#### Getting started with chat

chat.py can be used to initiate a conversation with a specified model. The file is under directory '/llm'.

You can download models and bind the model directory from host machine to container when start a container.

After entering the container through `docker exec`, you can run chat.py by:
```bash
cd /llm/portable-zip
python chat.py --model-path YOUR_MODEL_PATH
```
If your model is chatglm-6b and mounted on /llm/models, you can excute:
```bash
python chat.py --model-path /llm/models/chatglm-6b
```
Here is a demostration:

<a align="left"  href="https://llm-assets.readthedocs.io/en/latest/_images/llm-inference-cpu-docker-chatpy-demo.gif">
            <img src="https://llm-assets.readthedocs.io/en/latest/_images/llm-inference-cpu-docker-chatpy-demo.gif" width='60%' /> 

</a>

#### Getting started with tutorials

You could start a jupyter-lab serving to explore bigdl-llm-tutorial which can help you build a more sophisticated Chatbo.

To start serving,  run the script under '/llm':
```bash
cd /llm
./start-notebook.sh [--port EXPECTED_PORT]
```
You could assign a port to serving, or the default port 12345 will be assigned.

If you use host network mode when booted the container, after successfully running service, you can access http://127.0.0.1:12345/lab to get into tutorial, or you should bind the correct ports between container and host. 

Here is a demostration of how to use tutorial in explorer:

<a align="left" href="https://llm-assets.readthedocs.io/en/latest/_images/llm-inference-cpu-docker-tutorial-demo.gif">
            <img src="https://llm-assets.readthedocs.io/en/latest/_images/llm-inference-cpu-docker-tutorial-demo.gif" width='60%' /> 

</a>

### BigDL-LLM on Linux/MacOS

To run container on Linux/MacOS:
```bash
#/bin/bash
export DOCKER_IMAGE=intelanalytics/bigdl-llm-cpu:2.5.0-SNAPSHOT
export CONTAINER_NAME=my_container
export MODEL_PATH=/llm/models[change to your model path]

docker run -itd \
    -p 12345:12345 \
    --cpuset-cpus="0-7" \
    --cpuset-mems="0" \
    --memory="8G" \
    --name=$CONTAINER_NAME \
    -v $MODEL_PATH:/llm/models \
    $DOCKER_IMAGE
```

Also, you could use chat.py and bigdl-llm-tutorial for development.

[Getting started with chat](#getting-started-with-chat)

[Getting started with tutorials](#getting-started-with-tutorials)

## Docker installation guide for BigDL LLM on XPU

First, pull docker image from docker hub:
```
docker pull intelanalytics/bigdl-llm-xpu:2.5.0-SNAPSHOT
```
To map the xpu into the container, you need to specify --device=/dev/dri when booting the container.
An example could be:
```bash
#/bin/bash
export DOCKER_IMAGE=intelanalytics/bigdl-llm-xpu:2.5.0-SNAPSHOT
export CONTAINER_NAME=my_container
export MODEL_PATH=/llm/models[change to your model path]

sudo docker run -itd \
        --net=host \
        --device=/dev/dri \
        --memory="32G" \
        --name=$CONTAINER_NAME \
        --shm-size="16g" \
        -v $MODEL_PATH:/llm/models \
        $DOCKER_IMAGE
```

After the container is booted, you could get into the container through `docker exec`.

To verify the device is successfully mapped into the container, run `sycl-ls` to check the result. In a machine with Arc A770, the sampled output is:

```bash
root@arda-arc12:/# sycl-ls
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device 1.2 [2023.16.7.0.21_160000]
[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i9-13900K 3.0 [2023.16.7.0.21_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics 3.0 [23.17.26241.33]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26241]
```

To run inference using `BigDL-LLM` using xpu, you could refer to this [documentation](https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU).

## Docker installation guide for BigDL LLM Serving on CPU

### Boot container

Pull image:
```
docker pull intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
```

You could use the following bash script to start the container. Please be noted that the CPU config is specified for Xeon CPUs, change it accordingly if you are not using a Xeon CPU.
```bash
export DOCKER_IMAGE=intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
export CONTAINER_NAME=my_container
export MODEL_PATH=/llm/models[change to your model path]

docker run -itd \
    --net=host \
    --cpuset-cpus="0-47" \
    --cpuset-mems="0" \
    --memory="32G" \
    --name=$CONTAINER_NAME \
    -v $MODEL_PATH:/llm/models \
    $DOCKER_IMAGE
```
After the container is booted, you could get into the container through `docker exec`.

### Models

Using BigDL-LLM in FastChat does not impose any new limitations on model usage. Therefore, all Hugging Face Transformer models can be utilized in FastChat.

FastChat determines the Model adapter to use through path matching. Therefore, in order to load models using BigDL-LLM, you need to make some modifications to the model's name.

For instance, assuming you have downloaded the `llama-7b-hf` from [HuggingFace](https://huggingface.co/decapoda-research/llama-7b-hf).  Then, to use the `BigDL-LLM` as backend, you need to change name from `llama-7b-hf` to `bigdl-7b`.
The key point here is that the model's path should include "bigdl" and should not include paths matched by other model adapters.

A special case is `ChatGLM` models. For these models, you do not need to do any changes after downloading the model and the `BigDL-LLM` backend will be used automatically.


### Start the service

#### Serving with Web UI

To serve using the Web UI, you need three main components: web servers that interface with users, model workers that host one or more models, and a controller to coordinate the web server and model workers.

##### Launch the Controller
```bash
python3 -m fastchat.serve.controller
```

This controller manages the distributed workers.

##### Launch the model worker(s)
```bash
python3 -m ipex_llm.serving.model_worker --model-path lmsys/vicuna-7b-v1.3 --device cpu
```
Wait until the process finishes loading the model and you see "Uvicorn running on ...". The model worker will register itself to the controller.

> To run model worker using Intel GPU, simply change the --device cpu option to --device xpu

##### Launch the Gradio web server

```bash
python3 -m fastchat.serve.gradio_web_server
```

This is the user interface that users will interact with.

By following these steps, you will be able to serve your models using the web UI with `BigDL-LLM` as the backend. You can open your browser and chat with a model now.

#### Serving with OpenAI-Compatible RESTful APIs

To start an OpenAI API server that provides compatible APIs using `BigDL-LLM` backend, you need three main components: an OpenAI API Server that serves the in-coming requests, model workers that host one or more models, and a controller to coordinate the web server and model workers.

First, launch the controller

```bash
python3 -m fastchat.serve.controller
```

Then, launch the model worker(s):

```bash
python3 -m ipex_llm.serving.model_worker --model-path lmsys/vicuna-7b-v1.3 --device cpu
```

Finally, launch the RESTful API server

```bash
python3 -m fastchat.serve.openai_api_server --host localhost --port 8000
```


## Docker installation guide for BigDL LLM Serving on XPU

### Boot container

Pull image:
```
docker pull intelanalytics/bigdl-llm-serving-xpu:2.5.0-SNAPSHOT
```

To map the `xpu` into the container, you need to specify `--device=/dev/dri` when booting the container.

An example could be:
```bash
#/bin/bash
export DOCKER_IMAGE=intelanalytics/bigdl-llm-serving-cpu:2.5.0-SNAPSHOT
export CONTAINER_NAME=my_container
export MODEL_PATH=/llm/models[change to your model path]
export SERVICE_MODEL_PATH=/llm/models/chatglm2-6b[a specified model path for running service]

docker run -itd \
    --net=host \
    --device=/dev/dri \
    --memory="32G" \
    --name=$CONTAINER_NAME \
    --shm-size="16g" \
    -v $MODEL_PATH:/llm/models \
    -e SERVICE_MODEL_PATH=$SERVICE_MODEL_PATH \
    $DOCKER_IMAGE --service-model-path $SERVICE_MODEL_PATH
```
You can assign specified model path to service-model-path to run the service while booting the container. Also you can manually run the service after entering container. Run `/opt/entrypoint.sh --help` in container to see more information. There are steps below describe how to run service in details as well.

To verify the device is successfully mapped into the container, run `sycl-ls` to check the result. In a machine with Arc A770, the sampled output is:

```bash
root@arda-arc12:/# sycl-ls
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device 1.2 [2023.16.7.0.21_160000]
[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i9-13900K 3.0 [2023.16.7.0.21_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics 3.0 [23.17.26241.33]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26241]
```
After the container is booted, you could get into the container through `docker exec`.

### Start the service

#### Serving with Web UI

To serve using the Web UI, you need three main components: web servers that interface with users, model workers that host one or more models, and a controller to coordinate the web server and model workers.

##### Launch the Controller
```bash
python3 -m fastchat.serve.controller
```

This controller manages the distributed workers.

##### Launch the model worker(s)
```bash
python3 -m ipex_llm.serving.model_worker --model-path lmsys/vicuna-7b-v1.3 --device xpu
```
Wait until the process finishes loading the model and you see "Uvicorn running on ...". The model worker will register itself to the controller.

##### Launch the Gradio web server

```bash
python3 -m fastchat.serve.gradio_web_server
```

This is the user interface that users will interact with.

By following these steps, you will be able to serve your models using the web UI with `BigDL-LLM` as the backend. You can open your browser and chat with a model now.

#### Serving with OpenAI-Compatible RESTful APIs

To start an OpenAI API server that provides compatible APIs using `BigDL-LLM` backend, you need three main components: an OpenAI API Server that serves the in-coming requests, model workers that host one or more models, and a controller to coordinate the web server and model workers.

First, launch the controller

```bash
python3 -m fastchat.serve.controller
```

Then, launch the model worker(s):

```bash
python3 -m ipex_llm.serving.model_worker --model-path lmsys/vicuna-7b-v1.3 --device xpu
```

Finally, launch the RESTful API server

```bash
python3 -m fastchat.serve.openai_api_server --host localhost --port 8000
```

## Docker installation guide for BigDL LLM Fine Tuning on CPU

### 1. Prepare Docker Image

You can download directly from Dockerhub like:

```bash
# For standalone
docker pull intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT

# For k8s
docker pull intelanalytics/bigdl-llm-finetune-qlora-cpu-k8s:2.5.0-SNAPSHOT
```

Or build the image from source:

```bash
# For standalone
export HTTP_PROXY=your_http_proxy
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-qlora-cpu-standalone:2.5.0-SNAPSHOT \
  -f ./Dockerfile .

# For k8s
export HTTP_PROXY=your_http_proxy
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-qlora-cpu-k8s:2.5.0-SNAPSHOT \
  -f ./Dockerfile.k8s .
```

### 2. Prepare Base Model, Data and Container

Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) 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 HTTP_PROXY=your_http_proxy
export HTTPS_PROXY=your_https_proxy

docker run -itd \
   --net=host \
   --name=bigdl-llm-fintune-qlora-cpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   -v $BASE_MODE_PATH:/bigdl/model \
   -v $DATA_PATH:/bigdl/data/alpaca-cleaned \
   intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
```

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 \
   --name=bigdl-llm-fintune-qlora-cpu \
   -e http_proxy=${HTTP_PROXY} \
   -e https_proxy=${HTTPS_PROXY} \
   intelanalytics/bigdl-llm-finetune-qlora-cpu-standalone:2.5.0-SNAPSHOT
```

However, we do recommend you to handle them manually, because the automatical download can be blocked by Internet access and Huggingface authentication etc. according to different environment, and the manual method allows you to fine-tune in a custom way (with different base model and dataset).

### 3. Start Fine-Tuning (Local Mode)

Enter the running container:

```bash
docker exec -it bigdl-llm-fintune-qlora-cpu bash
```

Then, start QLoRA fine-tuning:
If the machine memory is not enough, you can try to set `use_gradient_checkpointing=True`.

```bash
cd /bigdl
bash start-qlora-finetuning-on-cpu.sh
```

After minutes, it is expected to get results like:

```bash
{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00,  2.18s/it]
TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
```

### 4. Merge the adapter into the original model

Using the [export_merged_model.py](../../../../../../python/llm/example/GPU/LLM-Finetuning/QLoRA/export_merged_model.py) to merge.

```
python ./export_merged_model.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --adapter_path ./outputs/checkpoint-200 --output_path ./outputs/checkpoint-200-merged
```

Then you can use `./outputs/checkpoint-200-merged` as a normal huggingface transformer model to do inference.

## Docker installation guide for BigDL LLM Fine Tuning on XPU

The following shows how to fine-tune LLM with Quantization (QLoRA built on BigDL-LLM 4bit optimizations) in a docker environment, which is accelerated by Intel XPU.

### 1. Prepare Docker Image

You can download directly from Dockerhub like:

```bash
docker pull intelanalytics/bigdl-llm-finetune-qlora-xpu:2.5.0-SNAPSHOT
```

Or build the image from source:

```bash
export HTTP_PROXY=your_http_proxy
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-qlora-xpu:2.5.0-SNAPSHOT \
  -f ./Dockerfile .
```

### 2. Prepare Base Model, Data and Container

Here, we try to fine-tune a [Llama2-7b](https://huggingface.co/meta-llama/Llama-2-7b) with [yahma/alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) 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 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/alpaca-cleaned \
   --shm-size="16g" \
   intelanalytics/bigdl-llm-fintune-qlora-xpu:2.5.0-SNAPSHOT
```

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:2.5.0-SNAPSHOT
```

However, we do recommend you to handle them manually, because the automatical download can be blocked by Internet access and Huggingface authentication etc. according to different environment, and the manual method allows you to fine-tune in a custom way (with different base model and dataset).

### 3. Start Fine-Tuning

Enter the running container:

```bash
docker exec -it bigdl-llm-fintune-qlora-xpu bash
```

Then, start QLoRA fine-tuning:

```bash
bash start-qlora-finetuning-on-xpu.sh
```

After minutes, it is expected to get results like:

```bash
{'loss': 2.0251, 'learning_rate': 0.0002, 'epoch': 0.02}
{'loss': 1.2389, 'learning_rate': 0.00017777777777777779, 'epoch': 0.03}
{'loss': 1.032, 'learning_rate': 0.00015555555555555556, 'epoch': 0.05}
{'loss': 0.9141, 'learning_rate': 0.00013333333333333334, 'epoch': 0.06}
{'loss': 0.8505, 'learning_rate': 0.00011111111111111112, 'epoch': 0.08}
{'loss': 0.8713, 'learning_rate': 8.888888888888889e-05, 'epoch': 0.09}
{'loss': 0.8635, 'learning_rate': 6.666666666666667e-05, 'epoch': 0.11}
{'loss': 0.8853, 'learning_rate': 4.4444444444444447e-05, 'epoch': 0.12}
{'loss': 0.859, 'learning_rate': 2.2222222222222223e-05, 'epoch': 0.14}
{'loss': 0.8608, 'learning_rate': 0.0, 'epoch': 0.15}
{'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15}
100%|███████████████████████████████████████████████████████████████████████████████████| 200/200 [07:16<00:00,  2.18s/it]
TrainOutput(global_step=200, training_loss=1.0400420665740966, metrics={'train_runtime': xxxx, 'train_samples_per_second': xxxx, 'train_steps_per_second': xxxx, 'train_loss': 1.0400420665740966, 'epoch': 0.15})
```