| .. | ||
| assets | ||
| Dockerfile | ||
| README.md | ||
Unsloth in a docker container
❗ Very important ❗
The complete docker image is designed for CUDA 12.1! There must be a compatible version on the host such as CUDA 12.2 and docker has to be installed.
Run nvidia-smi on the Host to see if you are using the right Conda version.
Build the container with:
docker build -t cuda-unsloth-jupyter .
And mount your directory to the /app path when starting.
Setting default password to: 123
docker run --rm --gpus=all -v path_to_files:/app -e JUPYTER_TOKEN='123' -p 8088:8088 -d cuda-unsloth-jupyter
--rm: This flag automatically deletes the container after it exits, freeing up disk space and keeping the environment clean. Add the -it flag to run the container in interactive mode, giving you direct access to its shell. Use -d to detach the container and run it in the background. Combining -itd allows you to interact with the container initially, then detach, leaving it running in the background.
GPU Testing Information
Currently Tested Configuration
| GPU Model | Memory (GB) |
|---|---|
| RTX 3090 Ti | 24 |
System Details
- Operating System: Windows 10
- CUDA Version: 12.2
Please note:
If you are using a different graphics card or a different Pytorch version, then you must adapt the Dockerfile accordingly. See: docs.unsloth.ai