2.2 KiB
Dockerfiles for building the llama-cpp-python server
Dockerfile.openblas_simple
- a simple Dockerfile for non-GPU OpenBLASDockerfile.cuda_simple
- a simple Dockerfile for CUDA accelerated CuBLAShug_model.py
- a Python utility for interactively choosing and downloading the latest5_1
quantized models from huggingface.co/TheBlokeDockerfile
- a single OpenBLAS and CuBLAS combined Dockerfile that automatically installs a previously downloaded modelmodel.bin
Get model from Hugging Face
python3 ./hug_model.py
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.
docker $ ls -lh *.bin
-rw-rw-r-- 1 user user 4.8G May 23 18:30 <downloaded-model-file>.q5_1.bin
lrwxrwxrwx 1 user user 24 May 23 18:30 model.bin -> <downloaded-model-file>.q5_1.bin
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 TWICE as much disk space as the size of the model:
Model | Quantized size |
---|---|
7B | 5 GB |
13B | 10 GB |
30B | 25 GB |
65B | 50 GB |
Note #2: If you want to pass or tune additional parameters, customise ./start_server.sh
before running docker build ...
Install Docker Server
Note #3: 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!
Use OpenBLAS
Use if you don't have a NVidia GPU. Defaults to python:3-slim-bullseye
Docker base image and OpenBLAS:
Build:
docker build --build-arg -t openblas .
Run:
docker run --cap-add SYS_RESOURCE -t openblas
Use CuBLAS
Requires a NVidia GPU with sufficient VRAM (approximately as much as the size above) and Docker NVidia support (see container-toolkit/install-guide)
Build:
docker build --build-arg IMAGE=nvidia/cuda:12.1.1-devel-ubuntu22.04 -t cublas .
Run:
docker run --cap-add SYS_RESOURCE -t cublas