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
File size: 684 Bytes
10ef792 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 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"]
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