Instructions to use Zipeng365/WISP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Zipeng365/WISP with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Zipeng365/WISP", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download environments/Dockerfile.cpu from Zipeng365/WISP: direct link, hf CLI and curl.
- Browser
- Download file 684 Bytes
-
https://huggingface.co/Zipeng365/WISP/resolve/main/environments/Dockerfile.cpu
- Command line
-
hf download hf://Zipeng365/WISP/environments/Dockerfile.cpu
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curl -L -o Dockerfile.cpu https://huggingface.co/Zipeng365/WISP/resolve/main/environments/Dockerfile.cpu
684 Bytes
| FROM python:3.12-slim | |
| # Optional convenience image. The base tag is not digest-pinned; the validated | |
| # release is the wheel and the version-pinned environment, not a tested image. | |
| ENV OMP_NUM_THREADS=1 OPENBLAS_NUM_THREADS=1 MKL_NUM_THREADS=1 | |
| WORKDIR /opt/wisp | |
| COPY environments/cpu.lock.txt /tmp/wisp-cpu.lock.txt | |
| RUN python -m pip install --no-cache-dir -r /tmp/wisp-cpu.lock.txt | |
| COPY pyproject.toml README.md LICENSE ./ | |
| COPY src ./src | |
| RUN python -m pip install --no-cache-dir --no-deps . | |
| # Mount downloaded checkpoints and inputs; do not bake 75 GB of models into an | |
| # environment image. No baseline, CUDA or dataset packages are installed here. | |
| ENTRYPOINT ["wisp"] | |
| CMD ["list"] | |