67 lines
2.9 KiB
Markdown
67 lines
2.9 KiB
Markdown
# Install Docker Server
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**Note #1:** This was tested with Docker running on Linux. If you can get it working on Windows or MacOS, please update this `README.md` with a PR!
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[Install Docker Engine](https://docs.docker.com/engine/install)
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**Note #2:** NVidia GPU CuBLAS support requires a NVidia GPU with sufficient VRAM (approximately as much as the size in the table below) and Docker NVidia support (see [container-toolkit/install-guide](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
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# Simple Dockerfiles for building the llama-cpp-python server with external model bin files
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## openblas_simple - a simple Dockerfile for non-GPU OpenBLAS, where the model is located outside the Docker image
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```
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cd ./openblas_simple
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docker build -t openblas_simple .
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docker run -e USE_MLOCK=0 -e MODEL=/var/model/<model-path> -v <model-root-path>:/var/model -t openblas_simple
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```
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where `<model-root-path>/<model-path>` is the full path to the model file on the Docker host system.
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## cuda_simple - a simple Dockerfile for CUDA accelerated CuBLAS, where the model is located outside the Docker image
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```
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cd ./cuda_simple
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docker build -t cuda_simple .
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docker run -e USE_MLOCK=0 -e MODEL=/var/model/<model-path> -v <model-root-path>:/var/model -t cuda_simple
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```
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where `<model-root-path>/<model-path>` is the full path to the model file on the Docker host system.
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# "Open-Llama-in-a-box"
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## Download an Apache V2.0 licensed 3B paramter Open Llama model and install into a Docker image that runs an OpenBLAS-enabled llama-cpp-python server
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```
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$ cd ./open_llama
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./build.sh
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./start.sh
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```
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# Manually choose your own Llama model from Hugging Face
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`python3 ./hug_model.py -a TheBloke -t llama`
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You should now have a model in the current directory and `model.bin` symlinked to it for the subsequent Docker build and copy step. e.g.
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```
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docker $ ls -lh *.bin
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-rw-rw-r-- 1 user user 4.8G May 23 18:30 <downloaded-model-file>q5_1.bin
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lrwxrwxrwx 1 user user 24 May 23 18:30 model.bin -> <downloaded-model-file>q5_1.bin
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```
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**Note #1:** Make sure you have enough disk space to download the model. As the model is then copied into the image you will need at least
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**TWICE** as much disk space as the size of the model:
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| Model | Quantized size |
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| 3B | 3 GB |
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| 7B | 5 GB |
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| 13B | 10 GB |
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| 33B | 25 GB |
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| 65B | 50 GB |
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**Note #2:** If you want to pass or tune additional parameters, customise `./start_server.sh` before running `docker build ...`
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## Use OpenBLAS
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Use if you don't have a NVidia GPU. Defaults to `python:3-slim-bullseye` Docker base image and OpenBLAS:
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### Build:
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`docker build -t openblas .`
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### Run:
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`docker run --cap-add SYS_RESOURCE -t openblas`
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## Use CuBLAS
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### Build:
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`docker build --build-arg IMAGE=nvidia/cuda:12.1.1-devel-ubuntu22.04 -t cublas .`
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### Run:
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`docker run --cap-add SYS_RESOURCE -t cublas`
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