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Since llama.cpp is CPU only we should not need CUDA right now. I'm looking into running quantized models ion GPU though.
The Dockerfile does not copy and models and the code doesn't download any on initialization so yeah, currently dockerizing should not work.
Duplicates
Steps to reproduce 🕹
=> => exporting layers 8.5s
=> => exporting manifest sha256:81b29524e6ca86716c44c2fa16b8dc312af04dd88c3e3c03af98f087b650c8f4 0.0s
=> => exporting config sha256:00a84a43fa0487344f94620038375e6d5607fc5f482c144bf8e938ceb7c76803 0.0s
=> => naming to dangling@sha256:81b29524e6ca86716c44c2fa16b8dc312af04dd88c3e3c03af98f087b650c8f4 0.0s
=> => unpacking to dangling@sha256:81b29524e6ca86716c44c2fa16b8dc312af04dd88c3e3c03af98f087b650c8f4
but something like perks it right up, the source image can be swapped for rocm, intel, arm etc. i habve a cuda gpu so i played to my strong suite.
Use an official CUDA runtime as a parent image
FROM nvidia/cuda:11.5.0-runtime-ubuntu20.04
Install Python and any necessary dependencies
RUN apt-get update && apt-get install python3.11 python3-pip -y
Set the working directory to /app
WORKDIR /app
Copy the scripts and requirements.txt files into the container at /app
COPY scripts/ /app/scripts/
COPY requirements.txt /app/
Install any necessary Python packages
RUN ls //requirements.txt|xargs -n 1 -P 3 pip install -r
Set any necessary environment variables
ENV CUDA_VISIBLE_DEVICES=all
Set the command to run when the container starts
CMD ["python3.11", "/bin/bash"]
Current behavior 😯
failure to store and run image
Expected behavior 🤔
store and run image
Your prompt 📝
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