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Adjust pins to fix learn tools tests errors (#1477)
Torch and Cuml have compatibility issues, installing them outside of requirements files removes some of our pins. Let's add them back in when installing to ensure that pins are in place.
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Dockerfile.tmpl

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@@ -45,9 +45,11 @@ RUN uv pip install --no-build-isolation --system "git+https://github.com/Kaggle/
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# b/408281617: Torch is adamant that it can not install cudnn 9.3.x, only 9.1.x, but Tensorflow can only support 9.3.x.
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# This conflict causes a number of package downgrades, which are handled in this command
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RUN uv pip install --system --force-reinstall --extra-index-url https://pypi.nvidia.com pynvjitlink-cu12 cuml-cu12==25.2.1 \
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nvidia-cudnn-cu12==9.3.0.75 scipy tsfresh
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RUN uv pip install --system --force-reinstall pynvjitlink-cu12==0.5.2
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# b/302136621: Fix eli5 import for learntools
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RUN uv pip install --system --force-reinstall --extra-index-url https://pypi.nvidia.com "cuml-cu12==25.2.1" \
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"nvidia-cudnn-cu12==9.3.0.75" scipy tsfresh scikit-learn==1.2.2 category-encoders eli5
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RUN uv pip install --system --force-reinstall "pynvjitlink-cu12==0.5.2"
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# b/385145217 Latest Colab lacks mkl numpy, install it.
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RUN uv pip install --system --force-reinstall -i https://pypi.anaconda.org/intel/simple numpy

kaggle_requirements.txt

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bayesian-optimization
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boto3
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catboost
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category-encoders
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cesium
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comm
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cytoolz
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dipy
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docker
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easyocr
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eli5
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emoji
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fastcore>=1.7.20
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fasttext
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ray
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rgf-python
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s3fs
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# b/302136621: Fix eli5 import for learntools
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scikit-learn==1.2.2
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# Scikit-learn accelerated library for x86
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scikit-learn-intelex>=2023.0.1
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scikit-multilearn

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