diff --git a/Dockerfile.tmpl b/Dockerfile.tmpl index 8f788d26..9d661201 100644 --- a/Dockerfile.tmpl +++ b/Dockerfile.tmpl @@ -1,170 +1,71 @@ -ARG BASE_IMAGE_REPO -ARG BASE_IMAGE_TAG -ARG CPU_BASE_IMAGE_NAME -ARG GPU_BASE_IMAGE_NAME -ARG LIGHTGBM_VERSION -ARG TORCH_VERSION -ARG TORCHAUDIO_VERSION -ARG TORCHTEXT_VERSION -ARG TORCHVISION_VERSION - {{ if eq .Accelerator "gpu" }} -FROM gcr.io/kaggle-images/python-lightgbm-whl:${GPU_BASE_IMAGE_NAME}-${BASE_IMAGE_TAG}-${LIGHTGBM_VERSION} AS lightgbm_whl -FROM gcr.io/kaggle-images/python-torch-whl:${GPU_BASE_IMAGE_NAME}-${BASE_IMAGE_TAG}-${TORCH_VERSION} AS torch_whl -FROM ${BASE_IMAGE_REPO}/${GPU_BASE_IMAGE_NAME}:${BASE_IMAGE_TAG} -ENV CUDA_MAJOR_VERSION=11 -ENV CUDA_MINOR_VERSION=0 -# NVIDIA binaries from the host are mounted to /opt/bin. -ENV PATH=/opt/bin:${PATH} -# Add CUDA stubs to LD_LIBRARY_PATH to support building the GPU image on a CPU machine. -ENV LD_LIBRARY_PATH_NO_STUBS="$LD_LIBRARY_PATH" -ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64/stubs" -RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 +FROM us-docker.pkg.dev/colab-images/public/runtime:release-colab-external_20260126-060048_RC00 {{ else }} -FROM ${BASE_IMAGE_REPO}/${CPU_BASE_IMAGE_NAME}:${BASE_IMAGE_TAG} -{{ end }} -# Keep these variables in sync if base image is updated. -ENV TENSORFLOW_VERSION=2.6.0 +FROM us-docker.pkg.dev/colab-images/public/cpu-runtime:release-colab-external_20260123-060023_RC00 +{{ end}} + +ADD kaggle_requirements.txt /kaggle_requirements.txt + +# Freeze existing requirements from base image for critical packages: +RUN pip freeze | grep -E 'tensorflow|keras|torch|jax' > /colab_requirements.txt -# We need to redefine the ARG here to get the ARG value defined above the FROM instruction. -# See: https://docs.docker.com/engine/reference/builder/#understand-how-arg-and-from-interact -ARG LIGHTGBM_VERSION -ARG TORCH_VERSION -ARG TORCHAUDIO_VERSION -ARG TORCHTEXT_VERSION -ARG TORCHVISION_VERSION +# Merge requirements files: +RUN cat /colab_requirements.txt >> /requirements.txt +RUN cat /kaggle_requirements.txt >> /requirements.txt -# Disable pesky logs like: KMP_AFFINITY: pid 6121 tid 6121 thread 0 bound to OS proc set 0 -# See: https://stackoverflow.com/questions/57385766/disable-tensorflow-log-information -ENV KMP_WARNINGS=0 +# Install Kaggle packages +RUN uv pip install --system --no-cache -r /requirements.txt +# Install manual packages: +# b/183041606#comment5: the Kaggle data proxy doesn't support these APIs. If the library is missing, it falls back to using a regular BigQuery query to fetch data. +RUN uv pip uninstall --system --no-cache google-cloud-bigquery-storage + +# uv cannot install this in requirements.txt without --no-build-isolation +# to avoid affecting the larger build, we'll post-install it. +RUN uv pip install --no-build-isolation --no-cache --system "git+https://github.com/Kaggle/learntools" + +# b/404590350: Ray and torchtune have conflicting cli named `tune`. `ray` is not part of Colab's base image. Re-install `tune` to ensure the torchtune CLI is available by default. +# b/468367647: Unpin protobuf, version greater than v5.29.5 causes issues with numerous packages +RUN uv pip install --system --force-reinstall --no-cache --no-deps torchtune +RUN uv pip install --system --force-reinstall --no-cache "protobuf==5.29.5" + +# Adding non-package dependencies: ADD clean-layer.sh /tmp/clean-layer.sh ADD patches/nbconvert-extensions.tpl /opt/kaggle/nbconvert-extensions.tpl ADD patches/template_conf.json /opt/kaggle/conf.json +ARG PACKAGE_PATH=/usr/local/lib/python3.12/dist-packages + +# Install GPU-specific non-pip packages. {{ if eq .Accelerator "gpu" }} -# b/200968891 Keeps horovod once torch is upgraded. -RUN pip uninstall -y horovod && \ - /tmp/clean-layer.sh +RUN uv pip install --system --no-cache "pycuda" {{ end }} + # Use a fixed apt-get repo to stop intermittent failures due to flaky httpredir connections, # as described by Lionel Chan at http://stackoverflow.com/a/37426929/5881346 RUN sed -i "s/httpredir.debian.org/debian.uchicago.edu/" /etc/apt/sources.list && \ - apt-get update && \ + apt-get update --allow-releaseinfo-change && \ # Needed by lightGBM (GPU build) # https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html#build-lightgbm - apt-get install -y build-essential unzip cmake libboost-dev libboost-system-dev libboost-filesystem-dev && \ + apt-get install -y build-essential unzip cmake libboost-dev libboost-system-dev libboost-filesystem-dev p7zip-full && \ # b/182601974: ssh client was removed from the base image but is required for packages such as stable-baselines. apt-get install -y openssh-client && \ + apt-get install -y graphviz && pip install graphviz && \ /tmp/clean-layer.sh -# b/128333086: Set PROJ_LIB to points to the proj4 cartographic library. -ENV PROJ_LIB=/opt/conda/share/proj - -# Install conda packages not available on pip. -# When using pip in a conda environment, conda commands should be ran first and then -# the remaining pip commands: https://www.anaconda.com/using-pip-in-a-conda-environment/ -RUN conda config --add channels nvidia && \ - conda config --add channels rapidsai && \ - # Base image channel order: conda-forge (highest priority), defaults. - # End state: rapidsai (highest priority), nvidia, conda-forge, defaults. - conda install mkl cartopy=0.19 imagemagick=7.1 pyproj==3.1.0 && \ - /tmp/clean-layer.sh - -{{ if eq .Accelerator "gpu" }} -RUN conda install cudf=21.10 cuml=21.10 cudatoolkit=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION && \ - /tmp/clean-layer.sh -{{ end }} - -# Install PyTorch -{{ if eq .Accelerator "gpu" }} -COPY --from=torch_whl /tmp/whl/*.whl /tmp/torch/ -RUN pip install /tmp/torch/*.whl && \ - rm -rf /tmp/torch && \ - /tmp/clean-layer.sh -{{ else }} -RUN pip install torch==$TORCH_VERSION+cpu torchvision==$TORCHVISION_VERSION+cpu torchaudio==$TORCHAUDIO_VERSION torchtext==$TORCHTEXT_VERSION -f https://download.pytorch.org/whl/torch_stable.html && \ - /tmp/clean-layer.sh -{{ end }} - -# Install LightGBM -{{ if eq .Accelerator "gpu" }} -COPY --from=lightgbm_whl /tmp/whl/*.whl /tmp/lightgbm/ -# Install OpenCL (required by LightGBM GPU version) -RUN apt-get install -y ocl-icd-libopencl1 clinfo && \ - mkdir -p /etc/OpenCL/vendors && \ - echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd && \ - pip install /tmp/lightgbm/*.whl && \ - rm -rf /tmp/lightgbm && \ - /tmp/clean-layer.sh -{{ else }} -RUN pip install lightgbm==$LIGHTGBM_VERSION && \ - /tmp/clean-layer.sh -{{ end }} - -# Install JAX -ENV JAX_VERSION=0.2.19 -{{ if eq .Accelerator "gpu" }} -RUN pip install jax[cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION]==$JAX_VERSION -f https://storage.googleapis.com/jax-releases/jax_releases.html && \ - /tmp/clean-layer.sh -{{ else }} -RUN pip install jax[cpu]==$JAX_VERSION && \ - /tmp/clean-layer.sh -{{ end }} - -# Install mxnet -{{ if eq .Accelerator "gpu" }} -RUN pip install mxnet-cu$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION && \ - /tmp/clean-layer.sh -{{ else }} -RUN pip install mxnet && \ - /tmp/clean-layer.sh -{{ end}} - -# Install GPU specific packages -{{ if eq .Accelerator "gpu" }} -# Install GPU-only packages -RUN pip install pycuda && \ - pip install pynvrtc && \ - # b/190622765 latest version is causing issue. nnabla fixed it in https://github.com/sony/nnabla/issues/892, waiting for new release before we can remove this pin. - pip install pynvml==8.0.4 && \ - pip install nnabla-ext-cuda$CUDA_MAJOR_VERSION$CUDA_MINOR_VERSION && \ - /tmp/clean-layer.sh -{{ end }} - -RUN pip install pysal && \ - pip install seaborn python-dateutil dask python-igraph && \ - pip install pyyaml joblib husl geopy ml_metrics mne pyshp && \ - pip install pandas && \ - pip install flax && \ - # Install h2o from source. - # Use `conda install -c h2oai h2o` once Python 3.7 version is released to conda. - apt-get install -y default-jre-headless && \ - pip install -f https://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o && \ - pip install tensorflow-gcs-config==2.6.0 && \ - pip install tensorflow-addons==0.14.0 && \ - /tmp/clean-layer.sh +ADD patches/keras_internal.py \ + patches/keras_internal_test.py \ + $PACKAGE_PATH/tensorflow_decision_forests/keras/ RUN apt-get install -y libfreetype6-dev && \ apt-get install -y libglib2.0-0 libxext6 libsm6 libxrender1 libfontconfig1 --fix-missing && \ - # b/198300835 kornia 4.1.0 is not compatible with our version of numpy. - pip install gensim==4.0.1 && \ - pip install textblob && \ - pip install wordcloud && \ - pip install xgboost && \ - pip install pydot && \ - pip install flake8 && \ - # Pinned because it breaks theano test with the latest version (b/178107003). - pip install theano-pymc==1.0.11 && \ - pip install python-Levenshtein && \ - pip install hep_ml && \ - # NLTK Project datasets - mkdir -p /usr/share/nltk_data && \ + /tmp/clean-layer.sh + +RUN mkdir -p /usr/share/nltk_data && \ # NLTK Downloader no longer continues smoothly after an error, so we explicitly list # the corpuses that work - # "yes | ..." answers yes to the retry prompt in case of an error. See b/133762095. - yes | python -m nltk.downloader -d /usr/share/nltk_data abc alpino averaged_perceptron_tagger \ + python -m nltk.downloader -d /usr/share/nltk_data abc alpino averaged_perceptron_tagger \ basque_grammars biocreative_ppi bllip_wsj_no_aux \ book_grammars brown brown_tei cess_cat cess_esp chat80 city_database cmudict \ comtrans conll2000 conll2002 conll2007 crubadan dependency_treebank \ @@ -173,322 +74,17 @@ RUN apt-get install -y libfreetype6-dev && \ masc_tagged maxent_ne_chunker maxent_treebank_pos_tagger moses_sample movie_reviews \ mte_teip5 names nps_chat omw opinion_lexicon paradigms \ pil pl196x porter_test ppattach problem_reports product_reviews_1 product_reviews_2 propbank \ - pros_cons ptb punkt qc reuters rslp rte sample_grammars semcor senseval sentence_polarity \ + pros_cons ptb punkt punkt_tab qc reuters rslp rte sample_grammars semcor senseval sentence_polarity \ sentiwordnet shakespeare sinica_treebank smultron snowball_data spanish_grammars \ state_union stopwords subjectivity swadesh switchboard tagsets timit toolbox treebank \ twitter_samples udhr2 udhr unicode_samples universal_tagset universal_treebanks_v20 \ - vader_lexicon verbnet webtext word2vec_sample wordnet wordnet_ic words ycoe && \ - # Stop-words - pip install stop-words && \ - pip install scikit-image && \ - /tmp/clean-layer.sh - -RUN pip install ibis-framework && \ - pip install gluonnlp && \ - pip install gluoncv && \ - /tmp/clean-layer.sh - -RUN pip install scipy && \ - # b/176817038 avoid upgrade to 0.24 which is causing issues with hep-ml package. - pip install scikit-learn==0.23.2 && \ - # HDF5 support - pip install h5py && \ - pip install biopython && \ - # PUDB, for local debugging convenience - pip install pudb && \ - pip install imbalanced-learn && \ - # Profiling and other utilities - pip install line_profiler && \ - pip install orderedmultidict && \ - pip install smhasher && \ - pip install bokeh && \ - pip install numba && \ - pip install datashader && \ - # Boruta (python implementation) - pip install Boruta && \ - apt-get install -y graphviz && pip install graphviz && \ - # Pandoc is a dependency of deap - apt-get install -y pandoc && \ - pip install git+git://github.com/scikit-learn-contrib/py-earth.git@issue191 && \ - pip install essentia && \ - /tmp/clean-layer.sh + vader_lexicon verbnet webtext word2vec_sample wordnet wordnet_ic words ycoe -# vtk with dependencies -RUN apt-get install -y libgl1-mesa-glx && \ - pip install vtk && \ - # xvfbwrapper with dependencies +RUN apt-get install -y git-lfs && \ + # vtk dependencies + apt-get install -y libgl1-mesa-glx && \ + # xvfbwrapper dependencies apt-get install -y xvfb && \ - pip install xvfbwrapper && \ - /tmp/clean-layer.sh - -RUN pip install mpld3 && \ - pip install gpxpy && \ - pip install arrow && \ - pip install nilearn && \ - pip install nibabel && \ - pip install pronouncing && \ - pip install markovify && \ - pip install imgaug && \ - pip install preprocessing && \ - pip install path.py && \ - pip install Geohash && \ - # https://github.com/vinsci/geohash/issues/4 - sed -i -- 's/geohash/.geohash/g' /opt/conda/lib/python3.7/site-packages/Geohash/__init__.py && \ - pip install deap && \ - pip install tpot && \ - pip install scikit-optimize && \ - pip install haversine && \ - pip install toolz cytoolz && \ - pip install plotly && \ - # b/206631257: hyperopt requires py4j >= 0.2.6 requires py4j but doesn't list it as a dependency. Remove once hyperopt properly list it. - pip install py4j && \ - pip install hyperopt && \ - pip install fitter && \ - pip install langid && \ - # Delorean. Useful for dealing with datetime - pip install delorean && \ - pip install trueskill && \ - # Useful data exploration libraries (for missing data and generating reports) - pip install missingno && \ - pip install pandas-profiling && \ - pip install s2sphere && \ - pip install bayesian-optimization && \ - pip install matplotlib-venn && \ - # b/184083722 pyldavis >= 3.3 requires numpy >= 1.20.0 but TensorFlow 2.4.1 / 2.5.0 requires 1.19.2 - pip install pyldavis==3.2.2 && \ - pip install mlxtend && \ - pip install altair && \ - # b/183944405 pystan 3.x is not compatible with fbprophet. - pip install pystan==2.19.1.1 && \ - pip install ImageHash && \ - pip install ecos && \ - pip install CVXcanon && \ - pip install pymc3 && \ - pip install imagecodecs && \ - pip install tifffile && \ - pip install spectral && \ - pip install descartes && \ - pip install geojson && \ - pip install pydicom && \ - pip install wavio && \ - pip install SimpleITK && \ - pip install hmmlearn && \ - pip install bayespy && \ - pip install gplearn && \ - pip install PyAstronomy && \ - pip install squarify && \ - pip install fuzzywuzzy && \ - pip install python-louvain && \ - pip install pyexcel-ods && \ - pip install sklearn-pandas && \ - pip install stemming && \ - pip install fbprophet && \ - pip install holoviews && \ - pip install geoviews && \ - pip install hypertools && \ - pip install py_stringsimjoin && \ - pip install mlens && \ - pip install scikit-multilearn && \ - pip install cleverhans && \ - pip install leven && \ - pip install catboost && \ - pip install lightfm && \ - pip install folium && \ - pip install scikit-plot && \ - # dipy requires the optional fury dependency for visualizations. - pip install fury dipy && \ - pip install plotnine && \ - pip install scikit-surprise && \ - pip install pymongo && \ - pip install geoplot && \ - pip install eli5 && \ - pip install implicit && \ - pip install kaggle && \ - /tmp/clean-layer.sh - -RUN pip install tensorpack && \ - # Add google PAIR-code Facets - cd /opt/ && git clone https://github.com/PAIR-code/facets && cd facets/ && jupyter nbextension install facets-dist/ --user && \ - export PYTHONPATH=$PYTHONPATH:/opt/facets/facets_overview/python/ && \ - pip install pycountry && \ - pip install iso3166 && \ - pip install pydash && \ - pip install kmodes --no-dependencies && \ - pip install librosa && \ - pip install polyglot && \ - pip install mmh3 && \ - pip install fbpca && \ - pip install sentencepiece && \ - pip install cufflinks && \ - pip install lime && \ - pip install memory_profiler && \ - /tmp/clean-layer.sh - -# install cython & cysignals before pyfasttext -RUN pip install --upgrade cython && \ - pip install --upgrade cysignals && \ - pip install pyfasttext && \ - pip install fasttext && \ - apt-get install -y libhunspell-dev && pip install hunspell && \ - pip install annoy && \ - pip install category_encoders && \ - # google-cloud-automl 2.0.0 introduced incompatible API changes, need to pin to 1.0.1 - pip install google-cloud-automl==1.0.1 && \ - pip install google-cloud-bigquery==2.2.0 && \ - pip install google-cloud-storage && \ - pip install google-cloud-translate==3.* && \ - pip install google-cloud-language==2.* && \ - pip install google-cloud-videointelligence==2.* && \ - pip install google-cloud-vision==2.* && \ - # b/183041606#comment5: the Kaggle data proxy doesn't support these APIs. If the library is missing, it falls back to using a regular BigQuery query to fetch data. - pip uninstall -y google-cloud-bigquery-storage && \ - # After launch this should be installed from pip - pip install git+https://github.com/googleapis/python-aiplatform.git@mb-release && \ - pip install ortools && \ - pip install scattertext && \ - # Pandas data reader - pip install pandas-datareader && \ - pip install wordsegment && \ - pip install wordbatch && \ - pip install emoji && \ - # Add Japanese morphological analysis engine - pip install janome && \ - pip install wfdb && \ - pip install vecstack && \ - # yellowbrick machine learning visualization library - pip install yellowbrick && \ - pip install mlcrate && \ - /tmp/clean-layer.sh - -RUN pip install bleach && \ - pip install certifi && \ - pip install cycler && \ - pip install decorator && \ - pip install entrypoints && \ - pip install html5lib && \ - pip install ipykernel && \ - pip install ipython && \ - pip install ipython-genutils && \ - pip install ipywidgets && \ - pip install isoweek && \ - pip install jedi && \ - pip install Jinja2 && \ - pip install jsonschema && \ - pip install jupyter-client && \ - pip install jupyter-console && \ - pip install jupyter-core && \ - pip install MarkupSafe && \ - pip install mistune && \ - pip install nbconvert && \ - pip install nbformat && \ - pip install notebook && \ - pip install papermill && \ - pip install olefile && \ - # b/198300835 kornia 0.5.10 is not compatible with our version of numpy. - pip install kornia==0.5.8 && \ - pip install pandas_summary && \ - pip install pandocfilters && \ - pip install pexpect && \ - pip install pickleshare && \ - pip install Pillow && \ - # Install openslide and its python binding - apt-get install -y openslide-tools && \ - pip install openslide-python && \ - pip install ptyprocess && \ - pip install Pygments && \ - pip install pyparsing && \ - pip install pytz && \ - pip install PyYAML && \ - pip install pyzmq && \ - pip install qtconsole && \ - pip install six && \ - pip install terminado && \ - pip install tornado && \ - pip install tqdm && \ - pip install traitlets && \ - pip install wcwidth && \ - pip install webencodings && \ - pip install widgetsnbextension && \ - pip install pyarrow && \ - pip install feather-format && \ - pip install fastai && \ - pip install allennlp && \ - pip install importlib-metadata && \ - python -m spacy download en_core_web_sm && python -m spacy download en_core_web_lg && \ - apt-get install -y ffmpeg && \ - /tmp/clean-layer.sh - - ########### - # - # NEW CONTRIBUTORS: - # Please add new pip/apt installs in this block. Don't forget a "&& \" at the end - # of all non-final lines. Thanks! - # - ########### - -RUN pip install flashtext && \ - pip install wandb && \ - pip install vaex && \ - pip install marisa-trie && \ - pip install pyemd && \ - pip install pyupset && \ - pip install pympler && \ - pip install s3fs && \ - pip install featuretools && \ - pip install -e git+https://github.com/SohierDane/BigQuery_Helper#egg=bq_helper && \ - pip install hpsklearn && \ - pip install git+https://github.com/Kaggle/learntools && \ - pip install kmapper && \ - pip install shap && \ - pip install ray && \ - pip install gym && \ - pip install pyarabic && \ - pip install pandasql && \ - pip install tensorflow_hub && \ - pip install jieba && \ - pip install git+https://github.com/SauceCat/PDPbox && \ - # ggplot is broken and main repo does not merge and release https://github.com/yhat/ggpy/pull/668 - pip install https://github.com/hbasria/ggpy/archive/0.11.5.zip && \ - pip install cesium && \ - pip install rgf_python && \ - # b/205704651 remove install cmd for matrixprofile after version > 1.1.10 is released. - pip install git+git://github.com/matrix-profile-foundation/matrixprofile.git@6bea7d4445284dbd9700a097974ef6d4613fbca7 && \ - pip install tsfresh && \ - # b/204442455 unpin once tsfresh is compatible with latest version of statsmodels. - pip install statsmodels==0.12.2 && \ - pip install pykalman && \ - pip install optuna && \ - pip install plotly_express && \ - pip install albumentations && \ - pip install catalyst && \ - # b/206990323 osmx 1.1.2 requires numpy >= 1.21 which we don't want. - pip install osmnx==1.1.1 && \ - apt-get -y install libspatialindex-dev && \ - pip install pytorch-ignite && \ - pip install qgrid && \ - pip install bqplot && \ - pip install earthengine-api && \ - pip install transformers && \ - pip install dlib && \ - pip install kaggle-environments && \ - pip install geopandas && \ - pip install nnabla && \ - pip install vowpalwabbit && \ - pip install pydub && \ - pip install pydegensac && \ - # b/198635596 latest versions of torchmetrics & pytorch-lightning are failing at runtime. - pip install torchmetrics==0.5.0 && \ - pip install pytorch-lightning==1.4.4 && \ - pip install datatable && \ - pip install sympy && \ - # flask is used by agents in the simulation competitions. - pip install flask && \ - # pycrypto is used by competitions team. - pip install pycrypto && \ - pip install easyocr && \ - # ipympl adds interactive widget support for matplotlib - pip install ipympl==0.7.0 && \ - pip install pandarallel && \ /tmp/clean-layer.sh # Download base easyocr models. @@ -507,75 +103,62 @@ RUN mkdir -p /root/.EasyOCR/model && \ # Tesseract and some associated utility packages RUN apt-get install tesseract-ocr -y && \ - pip install pytesseract && \ - pip install wand && \ - pip install pdf2image && \ - pip install PyPDF && \ - pip install pyocr && \ /tmp/clean-layer.sh -ENV TESSERACT_PATH=/usr/bin/tesseract -# For Facets -ENV PYTHONPATH=$PYTHONPATH:/opt/facets/facets_overview/python/ -# For Theano with MKL -ENV MKL_THREADING_LAYER=GNU +ENV TESSERACT_PATH=/usr/bin/tesseract \ + # For Facets, we also include an empty path to include $PWD. + PYTHONPATH=:$PYTHONPATH:/opt/facets/facets_overview/python/ \ + # For Theano with MKL + MKL_THREADING_LAYER=GNU # Temporary fixes and patches -# Temporary patch for Dask getting downgraded, which breaks Keras -RUN pip install --upgrade dask && \ - # Stop jupyter nbconvert trying to rewrite its folder hierarchy - mkdir -p /root/.jupyter && touch /root/.jupyter/jupyter_nbconvert_config.py && touch /root/.jupyter/migrated && \ +# Stop jupyter nbconvert trying to rewrite its folder hierarchy +RUN mkdir -p /root/.jupyter && touch /root/.jupyter/jupyter_nbconvert_config.py && touch /root/.jupyter/migrated && \ mkdir -p /.jupyter && touch /.jupyter/jupyter_nbconvert_config.py && touch /.jupyter/migrated && \ - # Stop Matplotlib printing junk to the console on first load - sed -i "s/^.*Matplotlib is building the font cache using fc-list.*$/# Warning removed by Kaggle/g" /opt/conda/lib/python3.7/site-packages/matplotlib/font_manager.py && \ # Make matplotlib output in Jupyter notebooks display correctly mkdir -p /etc/ipython/ && echo "c = get_config(); c.IPKernelApp.matplotlib = 'inline'" > /etc/ipython/ipython_config.py && \ - # Temporary patch for broken libpixman 0.38 in conda-forge, symlink to system libpixman 0.34 untile conda package gets updated to 0.38.5 or higher. - ln -sf /usr/lib/x86_64-linux-gnu/libpixman-1.so.0.34.0 /opt/conda/lib/libpixman-1.so.0.38.0 && \ /tmp/clean-layer.sh -# Add BigQuery client proxy settings -ENV PYTHONUSERBASE "/root/.local" -ADD patches/kaggle_gcp.py /root/.local/lib/python3.7/site-packages/kaggle_gcp.py -ADD patches/kaggle_secrets.py /root/.local/lib/python3.7/site-packages/kaggle_secrets.py -ADD patches/kaggle_session.py /root/.local/lib/python3.7/site-packages/kaggle_session.py -ADD patches/kaggle_web_client.py /root/.local/lib/python3.7/site-packages/kaggle_web_client.py -ADD patches/kaggle_datasets.py /root/.local/lib/python3.7/site-packages/kaggle_datasets.py -ADD patches/log.py /root/.local/lib/python3.7/site-packages/log.py -ADD patches/sitecustomize.py /root/.local/lib/python3.7/site-packages/sitecustomize.py +# install imagemagick for wand +# https://docs.wand-py.org/en/latest/guide/install.html#install-imagemagick-on-debian-ubuntu +RUN apt-get install libmagickwand-dev && \ + /tmp/clean-layer.sh + # Override default imagemagick policies ADD patches/imagemagick-policy.xml /etc/ImageMagick-6/policy.xml -# TensorBoard Jupyter extension. Should be replaced with TensorBoard's provided magic once we have -# worker tunneling support in place. -# b/139212522 re-enable TensorBoard once solution for slowdown is implemented. -# ENV JUPYTER_CONFIG_DIR "/root/.jupyter/" -# RUN pip install jupyter_tensorboard && \ -# jupyter serverextension enable jupyter_tensorboard && \ -# jupyter tensorboard enable -# ADD patches/tensorboard/notebook.py /opt/conda/lib/python3.7/site-packages/tensorboard/notebook.py +# Add Kaggle module resolver +ADD patches/kaggle_module_resolver.py $PACKAGE_PATH/tensorflow_hub/kaggle_module_resolver.py +RUN sed -i '/from tensorflow_hub import uncompressed_module_resolver/a from tensorflow_hub import kaggle_module_resolver' $PACKAGE_PATH/tensorflow_hub/config.py && \ + sed -i '/_install_default_resolvers()/a \ \ registry.resolver.add_implementation(kaggle_module_resolver.KaggleFileResolver())' $PACKAGE_PATH/tensorflow_hub/config.py + +# Add BigQuery client proxy settings +ENV PYTHONUSERBASE="/root/.local" +ADD patches/kaggle_gcp.py \ + patches/kaggle_secrets.py \ + patches/kaggle_session.py \ + patches/kaggle_web_client.py \ + patches/kaggle_datasets.py \ + $PACKAGE_PATH/ -# Disable unnecessary jupyter extensions -RUN jupyter-nbextension disable nb_conda --py --sys-prefix && \ - jupyter-serverextension disable nb_conda --py --sys-prefix && \ - python -m nb_conda_kernels.install --disable +# Figure out why this is in a different place? +# Found by doing a export PYTHONVERBOSE=1 and then running python and checking for where it looked for it. +ADD patches/sitecustomize.py /usr/lib/python3.12/sitecustomize.py -# Set backend for matplotlib -ENV MPLBACKEND "agg" +ARG GIT_COMMIT=unknown \ + BUILD_DATE=unknown -ARG GIT_COMMIT=unknown -ARG BUILD_DATE=unknown +LABEL git-commit=$GIT_COMMIT \ + build-date=$BUILD_DATE -LABEL git-commit=$GIT_COMMIT -LABEL build-date=$BUILD_DATE -LABEL tensorflow-version=$TENSORFLOW_VERSION -# Used in the Jenkins `Docker GPU Build` step to restrict the images being pruned. -LABEL kaggle-lang=python +ENV GIT_COMMIT=${GIT_COMMIT} \ + BUILD_DATE=${BUILD_DATE} # Correlate current release with the git hash inside the kernel editor by running `!cat /etc/git_commit`. RUN echo "$GIT_COMMIT" > /etc/git_commit && echo "$BUILD_DATE" > /etc/build_date {{ if eq .Accelerator "gpu" }} -# Remove the CUDA stubs. -ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH_NO_STUBS" +# Add the CUDA home. +ENV CUDA_HOME=/usr/local/cuda {{ end }} +ENTRYPOINT ["/usr/bin/env"] diff --git a/Jenkinsfile b/Jenkinsfile index 157fd79b..c4af03e6 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -14,48 +14,13 @@ pipeline { GIT_COMMIT_SUBJECT = sh(returnStdout: true, script:"git log --format=%s -n 1 HEAD").trim() GIT_COMMIT_AUTHOR = sh(returnStdout: true, script:"git log --format='%an' -n 1 HEAD").trim() GIT_COMMIT_SUMMARY = "`` ${GIT_COMMIT_SUBJECT} - ${GIT_COMMIT_AUTHOR}" - SLACK_CHANNEL = sh(returnStdout: true, script: "if [[ \"${GIT_BRANCH}\" == \"main\" ]]; then echo \"#kernelops\"; else echo \"#builds\"; fi").trim() - PRETEST_TAG = sh(returnStdout: true, script: "if [[ \"${GIT_BRANCH}\" == \"main\" ]]; then echo \"ci-pretest\"; else echo \"${GIT_BRANCH}-pretest\"; fi").trim() - STAGING_TAG = sh(returnStdout: true, script: "if [[ \"${GIT_BRANCH}\" == \"main\" ]]; then echo \"staging\"; else echo \"${GIT_BRANCH}-staging\"; fi").trim() + MATTERMOST_CHANNEL = sh(returnStdout: true, script: "if [[ \"${GIT_BRANCH}\" == \"main\" ]]; then echo \"#kernelops\"; else echo \"#builds\"; fi").trim() + // Use dev branch names as tags, but replace '/' with '-' using sed since docker images don't support forward slash + PRETEST_TAG = sh(returnStdout: true, script: "if [[ \"${GIT_BRANCH}\" == \"main\" ]]; then echo \"ci-pretest\"; else echo \"${GIT_BRANCH}-pretest\" | sed 's/\\//-/g'; fi").trim() + STAGING_TAG = sh(returnStdout: true, script: "if [[ \"${GIT_BRANCH}\" == \"main\" ]]; then echo \"staging\"; else echo \"${GIT_BRANCH}-staging\" | sed 's/\\//-/g'; fi").trim() } stages { - stage('Pre-build Packages from Source') { - parallel { - stage('torch') { - options { - timeout(time: 180, unit: 'MINUTES') - } - steps { - sh '''#!/bin/bash - set -exo pipefail - source config.txt - cd packages/ - ./build_package --base-image $BASE_IMAGE_REPO/$GPU_BASE_IMAGE_NAME:$BASE_IMAGE_TAG \ - --package torch \ - --version $TORCH_VERSION \ - --build-arg TORCHAUDIO_VERSION=$TORCHAUDIO_VERSION \ - --build-arg TORCHTEXT_VERSION=$TORCHTEXT_VERSION \ - --build-arg TORCHVISION_VERSION=$TORCHVISION_VERSION \ - --push - ''' - } - } - stage('lightgbm') { - options { - timeout(time: 10, unit: 'MINUTES') - } - steps { - sh '''#!/bin/bash - set -exo pipefail - source config.txt - cd packages/ - ./build_package --base-image $BASE_IMAGE_REPO/$GPU_BASE_IMAGE_NAME:$BASE_IMAGE_TAG --package lightgbm --version $LIGHTGBM_VERSION --push - ''' - } - } - } - } stage('Build/Test/Diff') { parallel { stage('CPU') { @@ -73,20 +38,6 @@ pipeline { ''' } } - stage('Test CPU Image') { - options { - timeout(time: 5, unit: 'MINUTES') - } - steps { - sh '''#!/bin/bash - set -exo pipefail - - date - docker pull gcr.io/kaggle-images/python:${PRETEST_TAG} - ./test --image gcr.io/kaggle-images/python:${PRETEST_TAG} - ''' - } - } stage('Diff CPU image') { steps { sh '''#!/bin/bash @@ -104,7 +55,7 @@ pipeline { stages { stage('Build GPU Image') { options { - timeout(time: 120, unit: 'MINUTES') + timeout(time: 4324, unit: 'MINUTES') } steps { sh '''#!/bin/bash @@ -123,52 +74,112 @@ pipeline { ''' } } - stage('Test GPU Image') { + stage('Diff GPU Image') { + steps { + sh '''#!/bin/bash + set -exo pipefail + + docker pull gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ./diff --gpu --target gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ''' + } + } + } + } + stage('TPU VM') { + agent { label 'ephemeral-linux' } + stages { + stage('Build TPU VM Image') { options { - timeout(time: 20, unit: 'MINUTES') + timeout(time: 60, unit: 'MINUTES') } steps { sh '''#!/bin/bash set -exo pipefail - date - docker pull gcr.io/kaggle-private-byod/python:${PRETEST_TAG} - ./test --gpu --image gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ./tpu/build | ts + ./push --tpu ${PRETEST_TAG} ''' } } - stage('Diff GPU Image') { + stage('Diff TPU VM Image') { steps { sh '''#!/bin/bash set -exo pipefail - docker pull gcr.io/kaggle-private-byod/python:${PRETEST_TAG} - ./diff --gpu --target gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + docker pull gcr.io/kaggle-private-byod/python-tpuvm:${PRETEST_TAG} + ./diff --tpu --target gcr.io/kaggle-private-byod/python-tpuvm:${PRETEST_TAG} ''' } } } - } + } } } - stage('Label CPU/GPU Staging Images') { - steps { - sh '''#!/bin/bash - set -exo pipefail + stage('Test') { + parallel { + stage('Test CPU Image') { + options { + timeout(time: 15, unit: 'MINUTES') + } + steps { + retry(2) { + sh '''#!/bin/bash + set -exo pipefail - gcloud container images add-tag gcr.io/kaggle-images/python:${PRETEST_TAG} gcr.io/kaggle-images/python:${STAGING_TAG} - gcloud container images add-tag gcr.io/kaggle-private-byod/python:${PRETEST_TAG} gcr.io/kaggle-private-byod/python:${STAGING_TAG} - ''' + date + docker pull gcr.io/kaggle-images/python:${PRETEST_TAG} + ./test --image gcr.io/kaggle-images/python:${PRETEST_TAG} + ''' + } + } + } + stage('Test on P100') { + agent { label 'ephemeral-linux-gpu' } + options { + timeout(time: 40, unit: 'MINUTES') + } + steps { + retry(2) { + sh '''#!/bin/bash + set -exo pipefail + + date + docker pull gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ./test --gpu --image gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ''' + } + } + } + stage('Test on T4x2') { + agent { label 'ephemeral-linux-gpu-t4x2' } + options { + timeout(time: 60, unit: 'MINUTES') + } + steps { + retry(2) { + sh '''#!/bin/bash + set -exo pipefail + + date + docker pull gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ./test --gpu --image gcr.io/kaggle-private-byod/python:${PRETEST_TAG} + ''' + } + } + } } } - stage('Delete Old Unversioned Images') { + stage('Label CPU/GPU Staging Images') { steps { sh '''#!/bin/bash set -exo pipefail - gcloud container images list-tags gcr.io/kaggle-images/python --filter="NOT tags:v* AND timestamp.datetime < -P6M" --format='get(digest)' --limit 100 | xargs -I {} gcloud container images delete gcr.io/kaggle-images/python@{} --quiet --force-delete-tags - gcloud container images list-tags gcr.io/kaggle-private-byod/python --filter="NOT tags:v* AND timestamp.datetime < -P6M" --format='get(digest)' --limit 100 | xargs -I {} gcloud container images delete gcr.io/kaggle-private-byod/python@{} --quiet --force-delete-tags + + gcloud container images add-tag gcr.io/kaggle-images/python:${PRETEST_TAG} gcr.io/kaggle-images/python:${STAGING_TAG} + gcloud container images add-tag gcr.io/kaggle-private-byod/python:${PRETEST_TAG} gcr.io/kaggle-private-byod/python:${STAGING_TAG} + # NOTE(b/336842777): TPUVM images are tested on an actual TPU VM outside this pipeline, so they are not auto-promoted to :staging tag. ''' } } @@ -176,16 +187,13 @@ pipeline { post { failure { - slackSend color: 'danger', message: "*<${env.BUILD_URL}console|${JOB_NAME} failed>* ${GIT_COMMIT_SUMMARY} @kernels-backend-ops", channel: env.SLACK_CHANNEL - mattermostSend color: 'danger', message: "*<${env.BUILD_URL}console|${JOB_NAME} failed>* ${GIT_COMMIT_SUMMARY} @kernels-backend-ops", channel: env.SLACK_CHANNEL + mattermostSend color: 'danger', message: "*<${env.BUILD_URL}console|${JOB_NAME} failed>* ${GIT_COMMIT_SUMMARY} @dockerops", channel: env.MATTERMOST_CHANNEL } success { - slackSend color: 'good', message: "*<${env.BUILD_URL}console|${JOB_NAME} passed>* ${GIT_COMMIT_SUMMARY}", channel: env.SLACK_CHANNEL - mattermostSend color: 'good', message: "*<${env.BUILD_URL}console|${JOB_NAME} passed>* ${GIT_COMMIT_SUMMARY} @kernels-backend-ops", channel: env.SLACK_CHANNEL + mattermostSend color: 'good', message: "*<${env.BUILD_URL}console|${JOB_NAME} passed>* ${GIT_COMMIT_SUMMARY} @dockerops", channel: env.MATTERMOST_CHANNEL } aborted { - slackSend color: 'warning', message: "*<${env.BUILD_URL}console|${JOB_NAME} aborted>* ${GIT_COMMIT_SUMMARY}", channel: env.SLACK_CHANNEL - mattermostSend color: 'warning', message: "*<${env.BUILD_URL}console|${JOB_NAME} aborted>* ${GIT_COMMIT_SUMMARY} @kernels-backend-ops", channel: env.SLACK_CHANNEL + mattermostSend color: 'warning', message: "*<${env.BUILD_URL}console|${JOB_NAME} aborted>* ${GIT_COMMIT_SUMMARY} @dockerops", channel: env.MATTERMOST_CHANNEL } } } diff --git a/README.md b/README.md index 614ba517..315e7db2 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@ If you the first step above doesn't work for your use case, [open an issue](http ## Opening a pull request -1. Edit the [Dockerfile](Dockerfile.tmpl). +1. Edit [kaggle_requirements.txt](kaggle_requirements.txt). 1. Follow the instructions below to build a new image. 1. Add tests for your new package. See this [example](https://github.com/Kaggle/docker-python/blob/main/tests/test_fastai.py). 1. Follow the instructions below to test the new image. @@ -45,6 +45,8 @@ A suite of tests can be found under the `/tests` folder. You can run the test us Flags: * `--gpu` to test the GPU image. +* `--pattern test_keras.py` or `-p test_keras.py` to run a single test +* `--image gcr.io/kaggle-images/python:ci-pretest` or `-i gcr.io/kaggle-images/python:ci-pretest` to test against a specific image ## Running the image diff --git a/build b/build index 9b20f2dc..83bbe577 100755 --- a/build +++ b/build @@ -47,18 +47,13 @@ done BUILD_ARGS+=" --build-arg GIT_COMMIT=$(git rev-parse HEAD)" BUILD_ARGS+=" --build-arg BUILD_DATE=$(date '+%Y%m%d-%H%M%S')" -# Read build args from config.txt file. -SRCDIR=$(dirname "${BASH_SOURCE[0]}") -for l in `cat ${SRCDIR}/config.txt`; do - BUILD_ARGS+=" --build-arg $l" -done - readonly CACHE_FLAG readonly DOCKERFILE readonly ACCELERATOR readonly IMAGE_TAG readonly BUILD_ARGS +SRCDIR=$(dirname "${BASH_SOURCE[0]}") DOCKERFILE_OUTDIR="${SRCDIR}/.generated" mkdir -p $DOCKERFILE_OUTDIR DOCKERFILE_PATH="$DOCKERFILE_OUTDIR/$DOCKERFILE" diff --git a/clean-layer.sh b/clean-layer.sh index d1a048fc..9a50e7bf 100755 --- a/clean-layer.sh +++ b/clean-layer.sh @@ -10,8 +10,6 @@ set -e set -x -# Delete files that pip caches when installing a package. -rm -rf /root/.cache/pip/* # Delete old downloaded archive files apt-get autoremove -y # Delete downloaded archive files @@ -19,6 +17,4 @@ apt-get clean # Ensures the current working directory won't be deleted cd /usr/local/src/ # Delete source files used for building binaries -rm -rf /usr/local/src/* -# Delete conda downloaded tarballs -conda clean -y --tarballs +rm -rf /usr/local/src/* \ No newline at end of file diff --git a/config.txt b/config.txt deleted file mode 100644 index b6b788f2..00000000 --- a/config.txt +++ /dev/null @@ -1,9 +0,0 @@ -BASE_IMAGE_REPO=gcr.io/deeplearning-platform-release -BASE_IMAGE_TAG=m86 -CPU_BASE_IMAGE_NAME=tf2-cpu.2-6 -GPU_BASE_IMAGE_NAME=tf2-gpu.2-6 -LIGHTGBM_VERSION=3.3.1 -TORCH_VERSION=1.9.1 -TORCHAUDIO_VERSION=0.9.1 -TORCHTEXT_VERSION=0.10.1 -TORCHVISION_VERSION=0.10.1 diff --git a/diff b/diff index 79b9b756..c8251703 100755 --- a/diff +++ b/diff @@ -32,6 +32,10 @@ while :; do BASE_IMAGE_TAG='gcr.io/kaggle-private-byod/python:latest' TARGET_IMAGE_TAG='kaggle/python-gpu-build' ;; + -x|--tpu) + BASE_IMAGE_TAG='gcr.io/kaggle-private-byod/python-tpuvm:latest' + TARGET_IMAGE_TAG='kaggle/python-tpuvm-build' + ;; -b|--base) if [[ -z "$2" ]]; then usage @@ -94,13 +98,13 @@ if [[ -n "$PACKAGE_NAME" ]]; then echo "Package: $PACKAGE_NAME" CMDS=("python /tools/pip_list_versions.py $PACKAGE_NAME | sort") else - CMDS=("pip list --format=freeze" 'cat /etc/os-release | grep -oP "PRETTY_NAME=\"\K([^\"]*)"' "uname -r" "dpkg --list | awk '{print \$2\"==\"\$3}'") + CMDS=("pip list --format=freeze" 'cat /etc/os-release | grep -oP "PRETTY_NAME=\"\K([^\"]*)"' "uname -r" "dpkg --list | awk '{print \$2\"==\"\$3}'" "printenv | sort") fi for cmd in "${CMDS[@]}"; do echo "== Comparing $cmd ==" diff --suppress-common-lines --side-by-side \ - <(docker run -v $PWD/tools:/tools --rm "$BASE_IMAGE_TAG" /bin/bash -c "$cmd") \ - <(docker run -v $PWD/tools:/tools --rm "$TARGET_IMAGE_TAG" /bin/bash -c "$cmd") \ + <(docker run -v $PWD/tools:/tools --entrypoint bash --rm "$BASE_IMAGE_TAG" -c "$cmd") \ + <(docker run -v $PWD/tools:/tools --entrypoint bash --rm "$TARGET_IMAGE_TAG" -c "$cmd") \ && echo 'No diff' || true done diff --git a/kaggle_requirements.txt b/kaggle_requirements.txt new file mode 100644 index 00000000..30e0683f --- /dev/null +++ b/kaggle_requirements.txt @@ -0,0 +1,128 @@ +# Please keep this in alphabetical order +Boruta +Cartopy +ImageHash +Janome +PyArabic +PyUpSet +Pympler +Rtree +shapely +SimpleITK +TPOT +Wand +bayesian-optimization +boto3 +catboost +category-encoders +cesium +comm +cytoolz +# Older versions of datasets fail with "Loading a dataset cached in a LocalFileSystem is not supported" +# https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor +datasets>=2.14.6 +deap +dipy +docker +easyocr +emoji +fasttext +featuretools +fiona +fury +fuzzywuzzy +geojson +gensim +# b/443054743,b/455550872 +google-adk[a2a,eval]>=1.21.0 +google-cloud-aiplatform +google-cloud-videointelligence +google-cloud-vision +google-genai +gpxpy +h2o +haversine +hep-ml +igraph +ipympl +ipywidgets==8.1.5 +isoweek +jedi +# jitler 0.11.1 breaks simulation image +jiter==0.10.0 +# b/276358430: fix Jupyter lsp freezing up the jupyter server +jupyter-lsp==1.5.1 +# b/333854354: pin jupyter-server to version 2.12.5; later versions break LSP (b/333854354) +jupyter_server==2.12.5 +jupyter_server_proxy +jupyterlab +jupyterlab-lsp +kaggle>=1.8.3 +kaggle-environments +kagglehub[pandas-datasets,hf-datasets,signing]>=0.4.2 +keras-cv +keras-nlp +keras-tuner +kornia +langid +libpysal +lime +line_profiler +mamba +matplotlib +mlcrate +mne +mpld3 +# b/274619697: learntools requires a specific nbconvert right now +nbconvert==6.4.5 +nbdev +nilearn +olefile +onnx +openslide-bin +openslide-python +optuna +pandas-profiling +pandasql +papermill +path +path.py +pdf2image +plotly-express +pudb +pyLDAvis +pycryptodome +pydicom +pyemd +pyexcel-ods +pymongo +pypdf +pytesseract +python-lsp-server +pytorch-ignite +pytorch-lightning +qgrid +qtconsole +ray +rgf-python +s3fs +scikit-learn +# Scikit-learn accelerated library for x86 +scikit-learn-intelex>=2023.0.1 +scikit-multilearn +scikit-optimize +scikit-plot +scikit-surprise +git+https://github.com/facebookresearch/segment-anything.git +squarify +tensorflow-io +# Must be compatible with torch version: https://github.com/meta-pytorch/torchcodec?tab=readme-ov-file#installing-torchcodec +torchcodec==0.9 +torchinfo +torchmetrics +torchtune +transformers>=5.0.0 +vtk +wavio +xvfbwrapper +ydata-profiling diff --git a/packages/build_package b/packages/build_package deleted file mode 100755 index 6a6e7e5c..00000000 --- a/packages/build_package +++ /dev/null @@ -1,150 +0,0 @@ -#!/bin/bash -set -e - -usage() { -cat << EOF -Usage: $0 [OPTIONS] -Build a new package ".whl". - -Options: - -p, --package PACKAGE Package to build (e.g. lightgbm). - -v, --version VERSION Package version to build. - -b, --base-image IMAGE Base image tag (e.g. m80). - -c, --use-cache Use layer cache when building a new image. - -f, --force-rebuild Rebuild the image regardless of whether it already exist on GCR. - -u, --push Push image to GCR. - --build-arg ARG=VALUE Build arguments to pass to the docker build command. -EOF -} - -PACKAGE='' -PACKAGE_VERSION='' -BASE_IMAGE='' -DOCKERFILE='' -CACHE_FLAG='--no-cache' -FORCE_REBUILD=false -PUSH_TO_GCR=false -BUILD_ARGS='' - -while :; do - case "$1" in - -h|--help) - usage - exit - ;; - -p|--package) - if [[ -z $2 ]]; then - usage - printf 'ERROR: No IMAGE specified after the %s flag.\n' "$1" >&2 - exit 1 - fi - PACKAGE=$2 - DOCKERFILE="${PACKAGE}.Dockerfile" - shift # skip the flag value - ;; - -v|--version) - if [[ -z $2 ]]; then - usage - printf 'ERROR: No VERSION specified after the %s flag.\n' "$1" >&2 - exit 1 - fi - PACKAGE_VERSION=$2 - shift # skip the flag value - ;; - -t|--base-image) - if [[ -z $2 ]]; then - usage - printf 'ERROR: No TAG specified after the %s flag.\n' "$1" >&2 - exit 1 - fi - BASE_IMAGE=$2 - shift # skip the flag value - ;; - -c|--use-cache) - CACHE_FLAG='' - ;; - -f|--force-rebuild) - FORCE_REBUILD=true - ;; - -u|--push) - PUSH_TO_GCR=true - ;; - --build-arg) - if [[ -z $2 ]]; then - usage - printf 'ERROR: No ARG=VALUE specified after the %s flag.\n' "$1" >&2 - exit 1 - fi - BUILD_ARGS+=" $1 $2" - shift # skip the flag value - ;; - -?*) - usage - printf 'ERROR: Unknown option: %s\n' "$1" >&2 - exit 1 - ;; - *) - break - esac - - shift -done - -readonly PACKAGE -readonly PACKAGE_VERSION -readonly BASE_IMAGE -readonly DOCKERFILE -readonly CACHE_FLAG -readonly FORCE_REBUILD - -SRCDIR=$(dirname "${BASH_SOURCE[0]}") -DOCKERFILE_PATH="$SRCDIR/$DOCKERFILE" - -if [[ -z "$PACKAGE_VERSION" ]]; then - printf 'ERROR: missing --version flag.\n' - exit 1 -fi - -if [[ -z "$BASE_IMAGE" ]]; then - printf 'ERROR: missing --base-image flag.\n' - exit 1 -fi - -if [[ -z "$DOCKERFILE" ]]; then - printf 'ERROR: missing --package flag.\n' - exit 1 -fi - -# Keep only `tf2-gpu.2-6:m80` in `gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m80` -TAG=${BASE_IMAGE/gcr.io\/deeplearning-platform-release\//} -# Replace the `:` in `tf2-gpu.2-6:m80` by `-` -TAG=${TAG/:/-} -# Append the package version -TAG=$TAG-$PACKAGE_VERSION -# Add the gcr repo. -TAG=gcr.io/kaggle-images/python-$PACKAGE-whl:$TAG - -SHOULD_BUILD=true -if ! $FORCE_REBUILD; then - echo "Checking if $TAG exists..." - docker pull $TAG && SHOULD_BUILD=false -fi - -if $SHOULD_BUILD; then - echo "Building $TAG..." - docker build --rm --pull $BUILD_ARGS \ - $CACHE_FLAG \ - -t $TAG \ - -f "$DOCKERFILE_PATH" \ - --build-arg BASE_IMAGE=$BASE_IMAGE \ - --build-arg PACKAGE_VERSION=$PACKAGE_VERSION \ - $SRCDIR - - if $PUSH_TO_GCR; then - echo "Pushing $TAG to GCR..." - docker push $TAG - fi -else - echo "Skipping build. $TAG already exists." - echo "Use --force-rebuild if you want to build a new version anyway." -fi \ No newline at end of file diff --git a/packages/lightgbm.Dockerfile b/packages/lightgbm.Dockerfile deleted file mode 100644 index 408c6acc..00000000 --- a/packages/lightgbm.Dockerfile +++ /dev/null @@ -1,29 +0,0 @@ -ARG BASE_IMAGE - -FROM ${BASE_IMAGE} AS builder - -ARG PACKAGE_VERSION - -# Build instructions: https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html#build-lightgbm -RUN apt-get update && \ - apt-get install -y build-essential cmake libboost-dev libboost-system-dev libboost-filesystem-dev ocl-icd-libopencl1 clinfo - -RUN cd /usr/local/src && \ - git clone --recursive https://github.com/microsoft/LightGBM && \ - cd LightGBM && \ - git checkout tags/v$PACKAGE_VERSION && \ - mkdir build && cd build && \ - cmake -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ .. && \ - make -j$(nproc) && \ - cd /usr/local/src/LightGBM/python-package && \ - python setup.py bdist_wheel - -# Using multi-stage builds to ensure the output image is very small -# See: https://docs.docker.com/develop/develop-images/multistage-build/ -FROM alpine:latest - -RUN mkdir -p /tmp/whl/ -COPY --from=builder /usr/local/src/LightGBM/python-package/dist/*.whl /tmp/whl - -# Print out the built .whl file. -RUN ls -lh /tmp/whl/ \ No newline at end of file diff --git a/packages/torch.Dockerfile b/packages/torch.Dockerfile deleted file mode 100644 index de451730..00000000 --- a/packages/torch.Dockerfile +++ /dev/null @@ -1,79 +0,0 @@ -ARG BASE_IMAGE - -FROM ${BASE_IMAGE} AS builder - -ARG PACKAGE_VERSION -ARG TORCHAUDIO_VERSION -ARG TORCHTEXT_VERSION -ARG TORCHVISION_VERSION - -# TORCHVISION_VERSION is mandatory -RUN test -n "$TORCHVISION_VERSION" - -# Build instructions: https://github.com/pytorch/pytorch#from-source -RUN conda install astunparse numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses - -# By default, it uses the version from version.txt which includes the `a0` (alpha zero) suffix and part of the git hash. -# This causes dependency conflicts like these: https://paste.googleplex.com/4786486378496000 -ENV PYTORCH_BUILD_VERSION=$PACKAGE_VERSION -ENV PYTORCH_BUILD_NUMBER=1 - -ENV TORCH_CUDA_ARCH_LIST="3.7;6.0;7.0+PTX;7.5+PTX" -ENV FORCE_CUDA=1 -RUN cd /usr/local/src && \ - git clone --recursive https://github.com/pytorch/pytorch && \ - cd pytorch && \ - git checkout tags/v$PACKAGE_VERSION && \ - git submodule sync && \ - git submodule update --init --recursive --jobs 0 && \ - python setup.py bdist_wheel - -# Install torch which is required before we can build other torch* packages. -RUN pip install /usr/local/src/pytorch/dist/*.whl - -# Build torchaudio -# Instructions: https://github.com/pytorch/audio#from-source -# See comment above for PYTORCH_BUILD_VERSION. -ENV BUILD_VERSION=$TORCHAUDIO_VERSION -RUN cd /usr/local/src && \ - git clone https://github.com/pytorch/audio && \ - cd audio && \ - git checkout tags/v$TORCHAUDIO_VERSION && \ - git submodule sync && \ - git submodule update --init --recursive --jobs 0 && \ - python setup.py bdist_wheel - -# Build torchtext -# Instructions: https://github.com/pytorch/text#building-from-source -# See comment above for PYTORCH_BUILD_VERSION. -ENV BUILD_VERSION=$TORCHTEXT_VERSION -RUN cd /usr/local/src && \ - git clone https://github.com/pytorch/text && \ - cd text && \ - git checkout tags/v$TORCHTEXT_VERSION && \ - git submodule sync && \ - git submodule update --init --recursive --jobs 0 && \ - python setup.py bdist_wheel - -# Build torchvision. -# Instructions: https://github.com/pytorch/vision/tree/main#installation -# See comment above for PYTORCH_BUILD_VERSION. -ENV BUILD_VERSION=$TORCHVISION_VERSION -RUN cd /usr/local/src && \ - git clone --recursive https://github.com/pytorch/vision && \ - cd vision && \ - git checkout tags/v$TORCHVISION_VERSION && \ - python setup.py bdist_wheel - -# Using multi-stage builds to ensure the output image is very small -# See: https://docs.docker.com/develop/develop-images/multistage-build/ -FROM alpine:latest - -RUN mkdir -p /tmp/whl/ -COPY --from=builder /usr/local/src/pytorch/dist/*.whl /tmp/whl -COPY --from=builder /usr/local/src/audio/dist/*.whl /tmp/whl -COPY --from=builder /usr/local/src/text/dist/*.whl /tmp/whl -COPY --from=builder /usr/local/src/vision/dist/*.whl /tmp/whl - -# Print out the built .whl file. -RUN ls -lh /tmp/whl/ \ No newline at end of file diff --git a/patches/kaggle_datasets.py b/patches/kaggle_datasets.py index f5134673..e60db2ef 100644 --- a/patches/kaggle_datasets.py +++ b/patches/kaggle_datasets.py @@ -1,7 +1,12 @@ import os +import sys +from os import listdir +from os.path import isdir, join from kaggle_web_client import KaggleWebClient _KAGGLE_TPU_NAME_ENV_VAR_NAME = 'TPU_NAME' +_KAGGLE_TPUVM_NAME_ENV_VAR_NAME = 'ISTPUVM' +_KAGGLE_INPUT_DIR = '/kaggle/input' class KaggleDatasets: GET_GCS_PATH_ENDPOINT = '/requests/CopyDatasetVersionToKnownGcsBucketRequest' @@ -14,8 +19,20 @@ class KaggleDatasets: def __init__(self): self.web_client = KaggleWebClient() self.has_tpu = os.getenv(_KAGGLE_TPU_NAME_ENV_VAR_NAME) is not None + self.has_tpuvm = os.getenv(_KAGGLE_TPUVM_NAME_ENV_VAR_NAME) is not None def get_gcs_path(self, dataset_dir: str = None) -> str: + if self.has_tpuvm: + if dataset_dir is None: + onlydirs = [f for f in listdir(_KAGGLE_INPUT_DIR) if isdir(join(_KAGGLE_INPUT_DIR, f))] + if len(onlydirs) == 1: + dataset_dir = onlydirs[0] + else: + raise Exception("Could not infer dataset_dir. dataset_dir can only be inferred if there is exactly 1 Kaggle dataset attached.") + dataset = join(_KAGGLE_INPUT_DIR, dataset_dir) + print("get_gcs_path is not required on TPU VMs which can directly use Kaggle datasets, using path: " + dataset, file=sys.stderr) + return dataset + integration_type = self.TPU if self.has_tpu else self.AUTO_ML data = { 'MountSlug': dataset_dir, diff --git a/patches/kaggle_gcp.py b/patches/kaggle_gcp.py index 2c8b64cc..4cb98858 100644 --- a/patches/kaggle_gcp.py +++ b/patches/kaggle_gcp.py @@ -1,5 +1,6 @@ import os import inspect +import logging from google.auth import credentials, environment_vars from google.auth.exceptions import RefreshError from google.api_core.gapic_v1.client_info import ClientInfo @@ -8,8 +9,6 @@ from google.cloud.bigquery._http import Connection from kaggle_secrets import GcpTarget, UserSecretsClient -from log import Log - KAGGLE_GCP_CLIENT_USER_AGENT="kaggle-gcp-client/1.0" def get_integrations(): @@ -22,7 +21,7 @@ def get_integrations(): target = GcpTarget[integration.upper()] kernel_integrations.add_integration(target) except KeyError as e: - Log.error(f"Unknown integration target: {integration.upper()}") + logging.debug(f"Unknown integration target: {integration.upper()}") return kernel_integrations @@ -66,14 +65,14 @@ def refresh(self, request): elif self.target == GcpTarget.CLOUDAI: self.token, self.expiry = client._get_cloudai_access_token() except ConnectionError as e: - Log.error(f"Connection error trying to refresh access token: {e}") + logging.error(f"Connection error trying to refresh access token: {e}") print("There was a connection error trying to fetch the access token. " f"Please ensure internet is on in order to use the {self.target.service} Integration.") raise RefreshError('Unable to refresh access token due to connection error.') from e except Exception as e: - Log.error(f"Error trying to refresh access token: {e}") + logging.error(f"Error trying to refresh access token: {e}") if (not get_integrations().has_integration(self.target)): - Log.error(f"No {self.target.service} integration found.") + logging.error(f"No {self.target.service} integration found.") print( f"Please ensure you have selected a {self.target.service} account in the Notebook Add-ons menu.") raise RefreshError('Unable to refresh access token.') from e @@ -102,7 +101,7 @@ def api_request(self, *args, **kwargs): msg = ("Permission denied using Kaggle's public BigQuery integration. " "Did you mean to select a BigQuery account in the Notebook Add-ons menu?") print(msg) - Log.info(msg) + logging.info(msg) raise e @@ -156,23 +155,23 @@ def monkeypatch_bq(bq_client, *args, **kwargs): # Remove these two lines once this is resolved: # https://github.com/googleapis/google-cloud-python/issues/8108 if explicit_project_id: - Log.info(f"Explicit project set to {explicit_project_id}") + logging.info(f"Explicit project set to {explicit_project_id}") kwargs['project'] = explicit_project_id if explicit_project_id is None and specified_credentials is None and not has_bigquery: msg = "Using Kaggle's public dataset BigQuery integration." - Log.info(msg) + logging.info(msg) print(msg) return PublicBigqueryClient(*args, **kwargs) else: if specified_credentials is None: - Log.info("No credentials specified, using KaggleKernelCredentials.") + logging.info("No credentials specified, using KaggleKernelCredentials.") kwargs['credentials'] = KaggleKernelCredentials() if (not has_bigquery): - Log.info("No bigquery integration found, creating client anyways.") + logging.info("No bigquery integration found, creating client anyways.") print('Please ensure you have selected a BigQuery ' 'account in the Notebook Add-ons menu.') if explicit_project_id is None: - Log.info("No project specified while using the unmodified client.") + logging.info("No project specified while using the unmodified client.") print('Please ensure you specify a project id when creating the client' ' in order to use your BigQuery account.') kwargs['client_info'] = set_kaggle_user_agent(kwargs.get('client_info')) @@ -196,20 +195,20 @@ def monkeypatch_aiplatform_init(aiplatform_klass, kaggle_kernel_credentials): def patched_init(*args, **kwargs): specified_credentials = kwargs.get('credentials') if specified_credentials is None: - Log.info("No credentials specified, using KaggleKernelCredentials.") + logging.info("No credentials specified, using KaggleKernelCredentials.") kwargs['credentials'] = kaggle_kernel_credentials return aiplatform_init(*args, **kwargs) if (not has_been_monkeypatched(aiplatform_klass.init)): aiplatform_klass.init = patched_init - Log.info("aiplatform.init patched") + logging.info("aiplatform.init patched") def monkeypatch_client(client_klass, kaggle_kernel_credentials): client_init = client_klass.__init__ def patched_init(self, *args, **kwargs): specified_credentials = kwargs.get('credentials') if specified_credentials is None: - Log.info("No credentials specified, using KaggleKernelCredentials.") + logging.info("No credentials specified, using KaggleKernelCredentials.") # Some GCP services demand the billing and target project must be the same. # To avoid using default service account based credential as caller credential # user need to provide ClientOptions with quota_project_id: @@ -227,7 +226,7 @@ def patched_init(self, *args, **kwargs): if (not has_been_monkeypatched(client_klass.__init__)): client_klass.__init__ = patched_init - Log.info(f"Client patched: {client_klass}") + logging.info(f"Client patched: {client_klass}") def set_kaggle_user_agent(client_info: ClientInfo): # Add kaggle client user agent in order to attribute usage. @@ -253,37 +252,6 @@ def init_gcs(): KaggleKernelCredentials(target=GcpTarget.GCS)) return storage -def init_automl(): - from google.cloud import automl, automl_v1beta1 - if not is_user_secrets_token_set(): - return - - from kaggle_gcp import get_integrations - if not get_integrations().has_cloudai(): - return - - from kaggle_secrets import GcpTarget - from kaggle_gcp import KaggleKernelCredentials - kaggle_kernel_credentials = KaggleKernelCredentials(target=GcpTarget.CLOUDAI) - - # Patch the 2 GA clients: AutoMlClient and PreditionServiceClient - monkeypatch_client(automl.AutoMlClient, kaggle_kernel_credentials) - monkeypatch_client(automl.PredictionServiceClient, kaggle_kernel_credentials) - - # The AutoML client library exposes 3 different client classes (AutoMlClient, - # TablesClient, PredictionServiceClient), so patch each of them. - # The same KaggleKernelCredentials are passed to all of them. - # The GcsClient class is only used internally by TablesClient. - - # The beta version of the clients that are now GA are included here for now. - # They are deprecated and will be removed by 1 May 2020. - monkeypatch_client(automl_v1beta1.AutoMlClient, kaggle_kernel_credentials) - monkeypatch_client(automl_v1beta1.PredictionServiceClient, kaggle_kernel_credentials) - - # The TablesClient is still in beta, so this will not be deprecated until - # the TablesClient is GA. - monkeypatch_client(automl_v1beta1.TablesClient, kaggle_kernel_credentials) - def init_translation_v2(): from google.cloud import translate_v2 if not is_user_secrets_token_set(): @@ -379,7 +347,6 @@ def init_vision(): def init(): init_bigquery() init_gcs() - init_automl() init_translation_v2() init_translation_v3() init_natural_language() @@ -392,4 +359,4 @@ def init(): # google.cloud.* and kaggle_gcp. By calling init here, we guarantee # that regardless of the original import that caused google.cloud.* to be # loaded, the monkeypatching will be done. -init() +init() \ No newline at end of file diff --git a/patches/kaggle_module_resolver.py b/patches/kaggle_module_resolver.py new file mode 100644 index 00000000..430cb980 --- /dev/null +++ b/patches/kaggle_module_resolver.py @@ -0,0 +1,22 @@ +import os +import re +import kagglehub + +from tensorflow_hub import resolver + +short_url_pattern = re.compile(r"https?://([a-z]+\.)?kaggle.com/models/(?P[^\\/]+)/(?P[^\\/]+)/(?P[^\\/]+)/(?P[^\\/]+)/(?P[0-9]+)$") +long_url_pattern = re.compile(r"https?://([a-z]+\.)?kaggle.com/models/(?P[^\\/]+)/(?P[^\\/]+)/frameworks/(?P[^\\/]+)/variations/(?P[^\\/]+)/versions/(?P[0-9]+)$") + +def _is_on_kaggle_notebook(): + return os.getenv("KAGGLE_KERNEL_RUN_TYPE") != None and os.getenv("KAGGLE_USER_SECRETS_TOKEN") != None + +def _is_kaggle_handle(handle): + return long_url_pattern.match(handle) != None or short_url_pattern.match(handle) != None + +class KaggleFileResolver(resolver.HttpResolverBase): + def is_supported(self, handle): + return _is_on_kaggle_notebook() and _is_kaggle_handle(handle) + + def __call__(self, handle): + m = long_url_pattern.match(handle) or short_url_pattern.match(handle) + return kagglehub.model_download(f"{m.group('owner')}/{m.group('model')}/{m.group('framework').lower()}/{m.group('variation')}/{m.group('version')}") diff --git a/patches/kaggle_secrets.py b/patches/kaggle_secrets.py index c1c5dc99..a177c171 100644 --- a/patches/kaggle_secrets.py +++ b/patches/kaggle_secrets.py @@ -94,7 +94,6 @@ def set_gcloud_credentials(self, project=None, account=None): """ creds = self.get_gcloud_credential() creds_path = self._write_credentials_file(creds) - self._write_gsutil_credentials_file(creds) subprocess.run(['gcloud', 'config', 'set', 'auth/credential_file_override', creds_path]) @@ -107,19 +106,11 @@ def set_gcloud_credentials(self, project=None, account=None): subprocess.run(['gcloud', 'config', 'set', 'account', account]) def set_tensorflow_credential(self, credential): - """Sets the credential for use by Tensorflow both in the local notebook - and to pass to the TPU. - """ - # b/159906185: Import tensorflow_gcs_config only when this method is called to prevent preloading TensorFlow. - import tensorflow_gcs_config + """Sets the credential for use by Tensorflow""" - # Write to a local JSON credentials file and set - # GOOGLE_APPLICATION_CREDENTIALS for tensorflow running in the notebook. + # Write to a local JSON credentials file self._write_credentials_file(credential) - # set the credential for the TPU - tensorflow_gcs_config.configure_gcs(credentials=credential) - def get_bigquery_access_token(self) -> Tuple[str, Optional[datetime]]: """Retrieves BigQuery access token information from the UserSecrets service. @@ -139,17 +130,6 @@ def _write_credentials_file(self, credentials) -> str: return adc_path - def _write_gsutil_credentials_file(self, credentials) -> str: - import json - creds_dict = json.loads(credentials) - boto_path = os.path.join(os.environ.get('HOME', '/'), '.boto') - with open(boto_path, 'w') as f: - f.write('[Credentials]\n') - f.write(' gs_oauth2_refresh_token = ') - f.write(creds_dict['refresh_token']) - - return boto_path - def _get_gcs_access_token(self) -> Tuple[str, Optional[datetime]]: return self._get_access_token(GcpTarget.GCS) diff --git a/patches/keras_internal.py b/patches/keras_internal.py new file mode 100644 index 00000000..e28127f9 --- /dev/null +++ b/patches/keras_internal.py @@ -0,0 +1,24 @@ +# Copyright 2021 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Access to Keras function with a different internal and external path.""" + +from tf_keras.src.engine import data_adapter as _data_adapter +from tf_keras.src.models import Functional +from tf_keras.layers import DenseFeatures +from tf_keras.src.utils.dataset_creator import DatasetCreator + + +unpack_x_y_sample_weight = _data_adapter.unpack_x_y_sample_weight +get_data_handler = _data_adapter.get_data_handler diff --git a/patches/keras_internal_test.py b/patches/keras_internal_test.py new file mode 100644 index 00000000..edc33ec2 --- /dev/null +++ b/patches/keras_internal_test.py @@ -0,0 +1,23 @@ +# Copyright 2021 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import tensorflow as tf +from tensorflow_decision_forests.keras import keras_internal + + +# Does nothing. Ensures keras_internal can be loaded. + +if __name__ == "__main__": + tf.test.main() + diff --git a/patches/log.py b/patches/log.py deleted file mode 100644 index 88040ba3..00000000 --- a/patches/log.py +++ /dev/null @@ -1,132 +0,0 @@ -import logging -import os - -import google.auth - - -_LOG_TO_FILE_ENV = os.getenv("KAGGLE_LOG_TO_FILE") - - -class _LogFormatter(logging.Formatter): - """A logging formatter which truncates long messages.""" - - _MAX_LOG_LENGTH = 10000 # Be generous, not to truncate long backtraces. - - def format(self, record): - msg = super(_LogFormatter, self).format(record) - return msg[:_LogFormatter._MAX_LOG_LENGTH] if msg else msg - -# TODO(vimota): Clean this up once we're using python 3.8 and can use -# (https://github.com/python/cpython/commit/dde9fdbe453925279ac3d2a6a72102f6f9ef247c) -# Right now, making the logging module display the intended frame's information -# when the logging calls (info, warn, ...) are wrapped (as is the case in our -# Log class) involves fragile logic. -class _Logger(logging.Logger): - - # This is a copy of logging.Logger.findCaller with the filename ignore - # set expanded to include the current filename (".../log.py"). - # Copyright 2001-2015 by Vinay Sajip. All Rights Reserved. - # License: https://github.com/python/cpython/blob/ce9e62544571e7ade7186697d5dd065fb4c5243f/LICENSE - def findCaller(self, stack_info=False): - f = logging.currentframe() - f = f.f_back - rv = "(unknown file)", 0, "(unknown function)", None - while hasattr(f, "f_code"): - co = f.f_code - filename = os.path.normcase(co.co_filename) - if filename in _ignore_srcfiles: - f = f.f_back - continue - sinfo = None - if stack_info: - sio = io.StringIO() - sio.write('Stack (most recent call last):\n') - traceback.print_stack(f, file=sio) - sinfo = sio.getvalue() - if sinfo[-1] == '\n': - sinfo = sinfo[:-1] - sio.close() - rv = (co.co_filename, f.f_lineno, co.co_name, sinfo) - break - return rv - - -_srcfile = os.path.normcase(_Logger.findCaller.__code__.co_filename) -_ignore_srcfiles = (_srcfile, logging._srcfile) - -class Log: - """ Helper aggregate for all things related to logging activity. """ - - _GLOBAL_LOG = logging.getLogger("") - _initialized = False - - # These are convenience helpers. For performance, consider saving Log.get_logger() and using that - @staticmethod - def critical(msg, *args, **kwargs): - Log._GLOBAL_LOG.critical(msg, *args, **kwargs) - - @staticmethod - def fatal(msg, *args, **kwargs): - Log._GLOBAL_LOG.fatal(msg, *args, **kwargs) - - @staticmethod - def exception(msg, *args, **kwargs): - Log._GLOBAL_LOG.exception(msg, *args, **kwargs) - - @staticmethod - def error(msg, *args, **kwargs): - Log._GLOBAL_LOG.error(msg, *args, **kwargs) - - @staticmethod - def warn(msg, *args, **kwargs): - Log._GLOBAL_LOG.warn(msg, *args, **kwargs) - - @staticmethod - def warning(msg, *args, **kwargs): - Log._GLOBAL_LOG.warning(msg, *args, **kwargs) - - @staticmethod - def debug(msg, *args, **kwargs): - Log._GLOBAL_LOG.debug(msg, *args, **kwargs) - - @staticmethod - def info(msg, *args, **kwargs): - Log._GLOBAL_LOG.info(msg, *args, **kwargs) - - @staticmethod - def set_level(loglevel): - if isinstance(loglevel, int): - Log._GLOBAL_LOG.setLevel(loglevel) - return - elif isinstance(loglevel, str): - # idea from https://docs.python.org/3.5/howto/logging.html#logging-to-a-file - numeric_level = getattr(logging, loglevel.upper(), None) - if isinstance(numeric_level, int): - Log._GLOBAL_LOG.setLevel(numeric_level) - return - - raise ValueError('Invalid log level: %s' % loglevel) - - @staticmethod - def _static_init(): - if Log._initialized: - return - - logging.setLoggerClass(_Logger) - # The root logger's type is unfortunately (and surprisingly) not affected by - # `setLoggerClass`. Monkey patch it instead. TODO(vimota): Remove this, see the TODO - # associated with _Logger. - logging.RootLogger.findCaller = _Logger.findCaller - log_to_file = _LOG_TO_FILE_ENV.lower() in ("yes", "true", "t", "1") if _LOG_TO_FILE_ENV is not None else True - if log_to_file: - handler = logging.FileHandler(filename='/tmp/kaggle.log', mode='w') - else: - handler = logging.StreamHandler() - - # ".1s" is for the first letter: http://stackoverflow.com/a/27453084/1869. - format_string = "%(asctime)s %(levelname).1s %(process)d %(filename)s:%(lineno)d] %(message)s" - handler.setFormatter(_LogFormatter(format_string)) - logging.basicConfig(level=logging.INFO, handlers=[handler]) - Log._initialized = True - -Log._static_init() \ No newline at end of file diff --git a/patches/sitecustomize.py b/patches/sitecustomize.py index d01f3845..1bb8a1b6 100644 --- a/patches/sitecustomize.py +++ b/patches/sitecustomize.py @@ -1,16 +1,17 @@ +import logging import os -from log import Log - import sys +import importlib.abc import importlib import importlib.machinery +import wrapt + class GcpModuleFinder(importlib.abc.MetaPathFinder): _MODULES = [ 'google.cloud.bigquery', 'google.cloud.storage', - 'google.cloud.automl_v1beta1', 'google.cloud.translate', 'google.cloud.translate_v2', 'google.cloud.translate_v3', @@ -53,7 +54,6 @@ def create_module(self, spec): _LOADERS = { 'google.cloud.bigquery': kaggle_gcp.init_bigquery, 'google.cloud.storage': kaggle_gcp.init_gcs, - 'google.cloud.automl_v1beta1': kaggle_gcp.init_automl, 'google.cloud.translate': kaggle_gcp.init_translation_v3, 'google.cloud.translate_v2': kaggle_gcp.init_translation_v2, 'google.cloud.translate_v3': kaggle_gcp.init_translation_v3, @@ -72,3 +72,72 @@ def exec_module(self, module): if not hasattr(sys, 'frozen'): sys.meta_path.insert(0, GcpModuleFinder()) + +@wrapt.when_imported('google.generativeai') +def post_import_logic(module): + if os.getenv('KAGGLE_DISABLE_GOOGLE_GENERATIVE_AI_INTEGRATION') != None: + return + if (os.getenv('KAGGLE_DATA_PROXY_TOKEN') == None or + os.getenv('KAGGLE_USER_SECRETS_TOKEN') == None or + (os.getenv('KAGGLE_DATA_PROXY_URL') == None and + os.getenv('KAGGLE_GRPC_DATA_PROXY_URL') == None)): + return + + old_configure = module.configure + + def new_configure(*args, **kwargs): + if ('default_metadata' in kwargs): + default_metadata = kwargs['default_metadata'] + else: + default_metadata = [] + default_metadata.append(("x-kaggle-proxy-data", os.environ['KAGGLE_DATA_PROXY_TOKEN'])) + user_secrets_token = os.environ['KAGGLE_USER_SECRETS_TOKEN'] + default_metadata.append(('x-kaggle-authorization', f'Bearer {user_secrets_token}')) + kwargs['default_metadata'] = default_metadata + + if ('client_options' in kwargs): + client_options = kwargs['client_options'] + else: + client_options = {} + + if os.getenv('KAGGLE_GOOGLE_GENERATIVE_AI_USE_REST_ONLY') != None: + kwargs['transport'] = 'rest' + + if 'transport' in kwargs and kwargs['transport'] == 'rest': + client_options['api_endpoint'] = os.environ['KAGGLE_DATA_PROXY_URL'] + client_options['api_endpoint'] += '/palmapi' + else: + client_options['api_endpoint'] = os.environ['KAGGLE_GRPC_DATA_PROXY_URL'] + kwargs['client_options'] = client_options + + old_configure(*args, **kwargs) + + module.configure = new_configure + module.configure() # generativeai can use GOOGLE_API_KEY env variable, so make sure we have the other configs set + +@wrapt.when_imported('google.genai') +def post_genai_import_logic(module): + if os.getenv('KAGGLE_DISABLE_GOOGLE_GENERATIVE_AI_INTEGRATION'): + return + + if not (os.getenv('KAGGLE_DATA_PROXY_TOKEN') and + os.getenv('KAGGLE_USER_SECRETS_TOKEN') and + os.getenv('KAGGLE_DATA_PROXY_URL')): + return + @wrapt.patch_function_wrapper(module, 'Client.__init__') + def init_wrapper(wrapped, instance, args, kwargs): + # Don't want to forward requests that are to Vertex AI, debug mode, or have their own http_options specified + # Thus, if the client constructor contains any params other than api_key, we don't set up forwarding + if any(value is not None for key, value in kwargs.items() if key != 'api_key'): + return wrapped(*args, **kwargs) + + default_metadata = { + "x-kaggle-proxy-data": os.environ['KAGGLE_DATA_PROXY_TOKEN'], + 'x-kaggle-authorization': f"Bearer {os.environ['KAGGLE_USER_SECRETS_TOKEN']}" + } + http_options = { + 'base_url': os.getenv('KAGGLE_DATA_PROXY_URL') + '/palmapi/', + 'headers': default_metadata + } + kwargs['http_options'] = http_options + return wrapped(*args, **kwargs) diff --git a/push b/push index cdd53389..124a3469 100755 --- a/push +++ b/push @@ -8,6 +8,7 @@ Push a newly-built image with the given LABEL to gcr.io and DockerHub. Options: -g, --gpu Push the image with GPU support. + -t, --tpu Push the image with GPU support. -s, --source-image IMAGE Tag for the source image. EOF } @@ -26,6 +27,10 @@ while :; do SOURCE_IMAGE_TAG='kaggle/python-gpu-build:latest' TARGET_IMAGE='gcr.io/kaggle-private-byod/python' ;; + -t|--tpu) + SOURCE_IMAGE_TAG='kaggle/python-tpuvm-build:latest' + TARGET_IMAGE='gcr.io/kaggle-private-byod/python-tpuvm' + ;; -s|--source-image) if [[ -z $2 ]]; then usage diff --git a/test b/test index e43387b9..574b49e3 100755 --- a/test +++ b/test @@ -3,7 +3,7 @@ set -e IMAGE_TAG='kaggle/python-build' IMAGE_TAG_OVERRIDE='' -ADDITONAL_OPTS='' +ADDITONAL_OPTS='--runtime runc ' # Use the CPU runtime by default PATTERN='test*.py' usage() { @@ -28,7 +28,7 @@ while :; do ;; -g|--gpu) IMAGE_TAG='kaggle/python-gpu-build' - ADDITONAL_OPTS='-v /tmp/empty_dir:/usr/local/cuda/lib64/stubs:ro' + ADDITONAL_OPTS='--runtime nvidia -v /tmp/empty_dir:/usr/local/cuda/lib64/stubs:ro' ;; -i|--image) if [[ -z $2 ]]; then @@ -69,11 +69,10 @@ readonly ADDITONAL_OPTS readonly PATTERN set -x -docker run --rm --net=none -v /tmp/python-build:/tmp/python-build "$IMAGE_TAG" rm -rf /tmp/python-build/* -docker rm jupyter_test || true mkdir -p /tmp/python-build/tmp mkdir -p /tmp/python-build/devshm mkdir -p /tmp/python-build/working +mkdir -p /tmp/python-build/kaggle # Only run Jupyter server test if no specific test pattern is specified. if [ $PATTERN == 'test*.py' ]; then @@ -96,18 +95,23 @@ fi # Note about `--hostname localhost` (b/158137436) # hostname defaults to the container name which fails DNS name # resolution with --net=none (required to keep tests hermetic). See details in bug. +# +# Note about CLOUDSDK_CONFIG=/tmp/.config/gcloud +# We use the /tmp dir since the filesystem is --read-only and we need writable space for gcloud configs. docker run --rm -t --read-only --net=none \ -e HOME=/tmp -e KAGGLE_DATA_PROXY_TOKEN=test-key \ -e KAGGLE_USER_SECRETS_TOKEN_KEY=test-secrets-key \ - -e KAGGLE_URL_BASE=http://127.0.0.1:8001 \ + -e KAGGLE_URL_BASE=http://127.0.0.1:0 \ -e KAGGLE_DATA_PROXY_URL=http://127.0.0.1:8000 \ -e KAGGLE_DATA_PROXY_PROJECT=test \ -e TF_FORCE_GPU_ALLOW_GROWTH=true \ -e XLA_PYTHON_CLIENT_PREALLOCATE=false \ + -e CLOUDSDK_CONFIG=/tmp/.config/gcloud \ --hostname localhost \ --shm-size=2g \ -v $PWD:/input:ro -v /tmp/python-build/working:/working \ -v /tmp/python-build/tmp:/tmp -v /tmp/python-build/devshm:/dev/shm \ + -v /tmp/python-build/kaggle:/kaggle \ -w=/working \ $ADDITONAL_OPTS \ "$IMAGE_TAG" \ diff --git a/test_pytables.py b/test_pytables.py new file mode 100644 index 00000000..20ec0b7b --- /dev/null +++ b/test_pytables.py @@ -0,0 +1,13 @@ +import unittest + +import pandas as pd +from pandas.testing import assert_frame_equal + + +class TestPandasPytables(unittest.TestCase): + + def test_rw_hd5(self): + want = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z']) + want.to_hdf('./want.h5', 'data') + got = pd.read_hdf('./want.h5') + assert_frame_equal(want, got) diff --git a/tests/common.py b/tests/common.py index 823212a2..469033dd 100644 --- a/tests/common.py +++ b/tests/common.py @@ -2,5 +2,19 @@ import os import unittest +import subprocess -gpu_test = unittest.skipIf(len(os.environ.get('CUDA_VERSION', '')) == 0, 'Not running GPU tests') +def getAcceleratorName(): + try: + deviceName = subprocess.check_output(['nvidia-smi', '--query-gpu=name', '--format=csv,noheader']) + return deviceName.decode('utf-8').strip() + except FileNotFoundError: + return("nvidia-smi not found.") + +def isGPU(): + return os.path.isfile('/proc/driver/nvidia/version') + +gpu_test = unittest.skipIf(not isGPU(), 'Not running GPU tests') +# b/342143152 P100s are slowly being unsupported in new release of popular ml tools such as RAPIDS. +p100_exempt = unittest.skipIf(getAcceleratorName() == "Tesla P100-PCIE-16GB", 'Not running p100 exempt tests') +tpu_test = unittest.skipIf(len(os.environ.get('ISTPUVM', '')) == 0, 'Not running TPU tests') diff --git a/tests/data/haarcascade_frontalface_alt.xml b/tests/data/haarcascade_frontalface_alt.xml new file mode 100644 index 00000000..ade4b212 --- /dev/null +++ b/tests/data/haarcascade_frontalface_alt.xml @@ -0,0 +1,24350 @@ + + + +BOOST + HAAR + 20 + 20 + + 213 + + 0 + 22 + + <_> + 3 + 8.2268941402435303e-01 + + <_> + + 0 -1 0 4.0141958743333817e-03 + + 3.3794190734624863e-02 8.3781069517135620e-01 + <_> + + 0 -1 1 1.5151339583098888e-02 + + 1.5141320228576660e-01 7.4888122081756592e-01 + <_> + + 0 -1 2 4.2109931819140911e-03 + + 9.0049281716346741e-02 6.3748198747634888e-01 + <_> + 16 + 6.9566087722778320e+00 + + <_> + + 0 -1 3 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"sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 4]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 4]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}2 +ëroot.layer_with_weights-0"_tf_keras_layer*´{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "linear", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 4}}, "shared_object_id": 6}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 4]}}2 +¸root.keras_api.metrics.0"_tf_keras_metric*{"class_name": "Mean", "name": "loss", "dtype": "float32", "config": {"name": "loss", "dtype": "float32"}, "shared_object_id": 7}2 \ No newline at end of file diff --git a/tests/data/saved_model/saved_model.pb b/tests/data/saved_model/saved_model.pb new file mode 100644 index 00000000..70ec686e Binary files /dev/null and b/tests/data/saved_model/saved_model.pb differ diff --git a/tests/data/saved_model/variables/variables.data-00000-of-00001 b/tests/data/saved_model/variables/variables.data-00000-of-00001 new file mode 100644 index 00000000..ba62ce20 Binary files /dev/null and b/tests/data/saved_model/variables/variables.data-00000-of-00001 differ diff --git a/tests/data/saved_model/variables/variables.index b/tests/data/saved_model/variables/variables.index new file mode 100644 index 00000000..efb1a69f Binary files /dev/null and b/tests/data/saved_model/variables/variables.index differ diff --git a/tests/test_7z.py b/tests/test_7z.py new file mode 100644 index 00000000..aa1e5059 --- /dev/null +++ b/tests/test_7z.py @@ -0,0 +1,8 @@ +import subprocess +import unittest + + +class Test7z(unittest.TestCase): + def test_basic(self): + result = subprocess.run('which 7z', shell=True, capture_output=True) + assert len(result.stdout) > 0 diff --git a/tests/test_allennlp.py b/tests/test_allennlp.py deleted file mode 100644 index 1bc80f2d..00000000 --- a/tests/test_allennlp.py +++ /dev/null @@ -1,15 +0,0 @@ -import unittest - -from allennlp.data.tokenizers import SpacyTokenizer - - -class TestAllenNlp(unittest.TestCase): - # reference - # https://github.com/allenai/allennlp/blob/master/allennlp/tests/data/tokenizers/word_tokenizer_test.py - def test_passes_through_correctly(self): - tokenizer = SpacyTokenizer() - sentence = "this (sentence) has 'crazy' \"punctuation\"." - tokens = [t.text for t in tokenizer.tokenize(sentence)] - expected_tokens = ["this", "(", "sentence", ")", "has", "'", "crazy", "'", "\"", - "punctuation", "\"", "."] - self.assertSequenceEqual(tokens, expected_tokens) diff --git a/tests/test_annoy.py b/tests/test_annoy.py deleted file mode 100644 index 93b7d0c2..00000000 --- a/tests/test_annoy.py +++ /dev/null @@ -1,11 +0,0 @@ -import unittest - -from annoy import AnnoyIndex - - -class TestAnnoy(unittest.TestCase): - def test_tree(self): - t = AnnoyIndex(5, 'angular') - t.add_item(1, [1,2,3,4,5]) - - self.assertTrue(t.build(1)) diff --git a/tests/test_automl.py b/tests/test_automl.py deleted file mode 100644 index ba906a5f..00000000 --- a/tests/test_automl.py +++ /dev/null @@ -1,137 +0,0 @@ -import unittest - -from unittest.mock import Mock, patch - -from kaggle_gcp import KaggleKernelCredentials, init_automl -from test.support import EnvironmentVarGuard -from google.cloud import storage, automl_v1beta1, automl - -def _make_credentials(): - import google.auth.credentials - return Mock(spec=google.auth.credentials.Credentials) - -class TestAutoMl(unittest.TestCase): - - class FakeClient: - def __init__(self, credentials=None, client_info=None, **kwargs): - self.credentials = credentials - - class FakeConnection(): - def __init__(self, user_agent): - self.user_agent = user_agent - if (client_info is not None): - self._connection = FakeConnection(client_info.user_agent) - - @patch("google.cloud.automl.AutoMlClient", new=FakeClient) - def test_user_provided_credentials(self): - credentials = _make_credentials() - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - init_automl() - client = automl.AutoMlClient(credentials=credentials) - self.assertNotIsInstance(client.credentials, KaggleKernelCredentials) - self.assertIsNotNone(client.credentials) - - def test_tables_gcs_client(self): - # The GcsClient can't currently be monkeypatched for default - # credentials because it requires a project which can't be set. - # Verify that creating an automl_v1beta1.GcsClient given an actual - # storage.Client sets the client properly. - gcs_client = storage.Client(project="xyz", credentials=_make_credentials()) - tables_gcs_client = automl_v1beta1.GcsClient(client=gcs_client) - self.assertIs(tables_gcs_client.client, gcs_client) - - @patch("google.cloud.automl_v1beta1.gapic.auto_ml_client.AutoMlClient", new=FakeClient) - def test_tables_client_credentials(self): - credentials = _make_credentials() - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - init_automl() - tables_client = automl_v1beta1.TablesClient(credentials=credentials) - self.assertEqual(tables_client.auto_ml_client.credentials, credentials) - - @patch("google.cloud.automl.AutoMlClient", new=FakeClient) - def test_default_credentials_automl_client(self): - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - init_automl() - automl_client = automl.AutoMlClient() - self.assertIsNotNone(automl_client.credentials) - self.assertIsInstance(automl_client.credentials, KaggleKernelCredentials) - self.assertTrue(automl_client._connection.user_agent.startswith("kaggle-gcp-client/1.0")) - - @patch("google.cloud.automl_v1beta1.AutoMlClient", new=FakeClient) - def test_default_credentials_automl_v1beta1_client(self): - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - init_automl() - automl_client = automl_v1beta1.AutoMlClient() - self.assertIsNotNone(automl_client.credentials) - self.assertIsInstance(automl_client.credentials, KaggleKernelCredentials) - self.assertTrue(automl_client._connection.user_agent.startswith("kaggle-gcp-client/1.0")) - - @patch("google.cloud.automl_v1beta1.TablesClient", new=FakeClient) - def test_default_credentials_tables_client(self): - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - init_automl() - tables_client = automl_v1beta1.TablesClient() - self.assertIsNotNone(tables_client.credentials) - self.assertIsInstance(tables_client.credentials, KaggleKernelCredentials) - self.assertTrue(tables_client._connection.user_agent.startswith("kaggle-gcp-client/1.0")) - - @patch("google.cloud.automl.PredictionServiceClient", new=FakeClient) - def test_default_credentials_prediction_client(self): - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - prediction_client = automl.PredictionServiceClient() - self.assertIsNotNone(prediction_client.credentials) - self.assertIsInstance(prediction_client.credentials, KaggleKernelCredentials) - self.assertTrue(prediction_client._connection.user_agent.startswith("kaggle-gcp-client/1.0")) - - @patch("google.cloud.automl_v1beta1.PredictionServiceClient", new=FakeClient) - def test_default_credentials_prediction_v1beta1_client(self): - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - prediction_client = automl_v1beta1.PredictionServiceClient() - self.assertIsNotNone(prediction_client.credentials) - self.assertIsInstance(prediction_client.credentials, KaggleKernelCredentials) - self.assertTrue(prediction_client._connection.user_agent.startswith("kaggle-gcp-client/1.0")) - - def test_monkeypatching_idempotent(self): - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - with env: - client1 = automl.AutoMlClient.__init__ - init_automl() - client2 = automl.AutoMlClient.__init__ - self.assertEqual(client1, client2) - - @patch("google.cloud.automl_v1beta1.PredictionServiceClient", new=FakeClient) - def test_legacy_AUTOML_variable_v1beta1_client(self): - """ - Tests previous KAGGLE_KERNEL_INTEGRATIONS="AUTOML" environment setting - """ - env = EnvironmentVarGuard() - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'AUTOML') - with env: - prediction_client = automl_v1beta1.PredictionServiceClient() - self.assertIsNotNone(prediction_client.credentials) - self.assertIsInstance(prediction_client.credentials, KaggleKernelCredentials) - self.assertTrue(prediction_client._connection.user_agent.startswith("kaggle-gcp-client/1.0")) \ No newline at end of file diff --git a/tests/test_bigquery.py b/tests/test_bigquery.py index cf50da25..400ac32e 100644 --- a/tests/test_bigquery.py +++ b/tests/test_bigquery.py @@ -3,7 +3,7 @@ import json from unittest.mock import patch import threading -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from urllib.parse import urlparse from http.server import BaseHTTPRequestHandler, HTTPServer @@ -126,7 +126,7 @@ def test_no_project_with_connected_account(self): env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') env.set('KAGGLE_KERNEL_INTEGRATIONS', 'BIGQUERY') with env: - with self.assertRaises(DefaultCredentialsError): + with self.assertRaises(OSError): # TODO(vimota): Handle this case, either default to Kaggle Proxy or use some default project # by the user or throw a custom exception. client = bigquery.Client(client_options={"api_endpoint": TestBigQuery.API_BASE_URL}) diff --git a/tests/test_bigquery_proxy.py b/tests/test_bigquery_proxy.py index 0036c912..d9912f2c 100644 --- a/tests/test_bigquery_proxy.py +++ b/tests/test_bigquery_proxy.py @@ -3,7 +3,7 @@ import json from unittest.mock import patch import threading -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from urllib.parse import urlparse from http.server import BaseHTTPRequestHandler, HTTPServer diff --git a/tests/test_catalyst.py b/tests/test_catalyst.py deleted file mode 100644 index 48b2934b..00000000 --- a/tests/test_catalyst.py +++ /dev/null @@ -1,150 +0,0 @@ -import unittest -import collections -import json -import numpy as np - -import torch -import torch.nn as nn -import torch.nn.functional as F -import torchvision -import torchvision.transforms as transforms - -import catalyst -from catalyst.dl import SupervisedRunner, CheckpointCallback -from catalyst import utils - - -def _to_categorical(y, num_classes=None, dtype='float32'): - """ - Taken from - github.com/keras-team/keras/blob/master/keras/utils/np_utils.py - Converts a class vector (integers) to binary class matrix. - E.g. for use with categorical_crossentropy. - # Arguments - y: class vector to be converted into a matrix - (integers from 0 to num_classes). - num_classes: total number of classes. - dtype: The data type expected by the input, as a string - (`float32`, `float64`, `int32`...) - # Returns - A binary matrix representation of the input. The classes axis - is placed last. - # Example - ```python - # Consider an array of 5 labels out of a set of 3 classes {0, 1, 2}: - > labels - array([0, 2, 1, 2, 0]) - # `to_categorical` converts this into a matrix with as many - # columns as there are classes. The number of rows - # stays the same. - > to_categorical(labels) - array([[ 1., 0., 0.], - [ 0., 0., 1.], - [ 0., 1., 0.], - [ 0., 0., 1.], - [ 1., 0., 0.]], dtype=float32) - ``` - """ - - y = np.array(y, dtype='int') - input_shape = y.shape - if input_shape and input_shape[-1] == 1 and len(input_shape) > 1: - input_shape = tuple(input_shape[:-1]) - y = y.ravel() - if not num_classes: - num_classes = np.max(y) + 1 - n = y.shape[0] - categorical = np.zeros((n, num_classes), dtype=dtype) - categorical[np.arange(n), y] = 1 - output_shape = input_shape + (num_classes,) - categorical = np.reshape(categorical, output_shape) - return categorical - - -class Net(nn.Module): - def __init__(self): - super().__init__() - self.conv1 = nn.Conv2d(1, 20, 5, 1) - self.conv2 = nn.Conv2d(20, 50, 5, 1) - self.fc1 = nn.Linear(4 * 4 * 50, 500) - self.fc2 = nn.Linear(500, 10) - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = F.max_pool2d(x, 2, 2) - x = F.relu(self.conv2(x)) - x = F.max_pool2d(x, 2, 2) - x = x.view(-1, 4 * 4 * 50) - x = F.relu(self.fc1(x)) - x = self.fc2(x) - return x - - -class TestCatalyst(unittest.TestCase): - - def test_version(self): - self.assertIsNotNone(catalyst.__version__) - - def test_mnist(self): - utils.set_global_seed(42) - x_train = np.random.random((100, 1, 28, 28)).astype(np.float32) - y_train = _to_categorical( - np.random.randint(10, size=(100, 1)), - num_classes=10 - ).astype(np.float32) - x_valid = np.random.random((20, 1, 28, 28)).astype(np.float32) - y_valid = _to_categorical( - np.random.randint(10, size=(20, 1)), - num_classes=10 - ).astype(np.float32) - - x_train, y_train, x_valid, y_valid = \ - list(map(torch.tensor, [x_train, y_train, x_valid, y_valid])) - - bs = 32 - num_workers = 4 - data_transform = transforms.ToTensor() - - loaders = collections.OrderedDict() - - trainset = torch.utils.data.TensorDataset(x_train, y_train) - trainloader = torch.utils.data.DataLoader( - trainset, batch_size=bs, - shuffle=True, num_workers=num_workers) - - validset = torch.utils.data.TensorDataset(x_valid, y_valid) - validloader = torch.utils.data.DataLoader( - validset, batch_size=bs, - shuffle=False, num_workers=num_workers) - - loaders["train"] = trainloader - loaders["valid"] = validloader - - # experiment setup - num_epochs = 3 - logdir = "./logs" - - # model, criterion, optimizer - model = Net() - criterion = nn.BCEWithLogitsLoss() - optimizer = torch.optim.Adam(model.parameters()) - - # model runner - runner = SupervisedRunner() - - # model training - runner.train( - model=model, - criterion=criterion, - optimizer=optimizer, - loaders=loaders, - logdir=logdir, - num_epochs=num_epochs, - verbose=False, - callbacks=[CheckpointCallback(save_n_best=3, use_runner_logdir=True)] - ) - - with open('./logs/_metrics.json') as f: - metrics = json.load(f) - self.assertTrue(metrics['train.3']['valid']['loss'] < metrics['train.1']['valid']['loss']) - self.assertTrue(metrics['best']['valid']['loss'] < 0.35) diff --git a/tests/test_cudf.py b/tests/test_cudf.py new file mode 100644 index 00000000..cae69a6c --- /dev/null +++ b/tests/test_cudf.py @@ -0,0 +1,20 @@ +import unittest + +from common import gpu_test, p100_exempt + + +class TestCudf(unittest.TestCase): + @gpu_test + @p100_exempt # b/342143152: cuDL(>=24.4v) is inompatible with p100 GPUs. + def test_cudf_dataframe_operations(self): + import cudf + + data = {'col1': [1, 2, 3], 'col2': [4, 5, 6]} + gdf = cudf.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]}) + + gdf['col3'] = gdf['col1'] + gdf['col2'] + + expected_col3 = cudf.Series([5, 7, 9]) + self.assertEqual(gdf.shape, (3, 3)) + self.assertEqual(list(gdf.columns), ['col1', 'col2', 'col3']) + self.assertTrue(gdf['col3'].equals(expected_col3)) diff --git a/tests/test_cuml.py b/tests/test_cuml.py new file mode 100644 index 00000000..bbb7f0c6 --- /dev/null +++ b/tests/test_cuml.py @@ -0,0 +1,19 @@ +import unittest + +from common import gpu_test, p100_exempt + + +class TestCuml(unittest.TestCase): + @gpu_test + @p100_exempt # b/342143152: cuML(>=24.4v) is inompatible with p100 GPUs. + def test_pca_fit_transform(self): + import unittest + import numpy as np + from cuml.decomposition import PCA + + x = np.array([[1.0, 2.0], [2.0, 4.0], [3.0, 6.0], [-1.0, -2.0], [-2.0, -4.0]]) + pca = PCA(n_components=1) + + x_transformed = pca.fit_transform(x) + + self.assertEqual(x_transformed.shape, (5, 1)) diff --git a/tests/test_datasets.py b/tests/test_datasets.py index 029a4570..109db0b7 100644 --- a/tests/test_datasets.py +++ b/tests/test_datasets.py @@ -3,7 +3,7 @@ import threading import unittest from http.server import BaseHTTPRequestHandler, HTTPServer -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from urllib.parse import urlparse from kaggle_web_client import (KaggleWebClient, @@ -39,7 +39,7 @@ def do_POST(s): class TestDatasets(unittest.TestCase): - SERVER_ADDRESS = urlparse(os.getenv(_KAGGLE_URL_BASE_ENV_VAR_NAME, default="http://127.0.0.1:8001")) + SERVER_ADDRESS = urlparse(os.getenv(_KAGGLE_URL_BASE_ENV_VAR_NAME, default="http://127.0.0.1:0")) def _test_client(self, client_func, expected_path, expected_body, is_tpu=True, success=True, iap_token=False): _request = {} @@ -72,6 +72,8 @@ def get_response(self): threading.Thread(target=httpd.serve_forever).start() try: + # Make sure this server is pointed to by clients (the port may have been dynamically chosen): + env.set(_KAGGLE_URL_BASE_ENV_VAR_NAME, "http://"+httpd.server_address[0]+":"+str(httpd.server_address[1])) client_func() finally: httpd.shutdown() diff --git a/tests/test_datashader.py b/tests/test_datashader.py deleted file mode 100644 index c8ff9295..00000000 --- a/tests/test_datashader.py +++ /dev/null @@ -1,38 +0,0 @@ -import unittest - -import numpy as np -import pandas as pd -import datashader as ds -import datashader.transfer_functions as tf - -class TestDatashader(unittest.TestCase): - # based on https://github.com/pyviz/datashader/blob/master/datashader/tests/test_pipeline.py - def test_pipeline(self): - df = pd.DataFrame({ - 'x': np.array(([0.] * 10 + [1] * 10)), - 'y': np.array(([0.] * 5 + [1] * 5 + [0] * 5 + [1] * 5)), - 'f64': np.arange(20, dtype='f8') - }) - df.f64.iloc[2] = np.nan - - cvs = ds.Canvas(plot_width=2, plot_height=2, x_range=(0, 1), y_range=(0, 1)) - - pipeline = ds.Pipeline(df, ds.Point('x', 'y')) - img = pipeline((0, 1), (0, 1), 2, 2) - agg = cvs.points(df, 'x', 'y', ds.count()) - self.assertTrue(img.equals(tf.shade(agg))) - - color_fn = lambda agg: tf.shade(agg, 'pink', 'red') - pipeline.color_fn = color_fn - img = pipeline((0, 1), (0, 1), 2, 2) - self.assertTrue(img.equals(color_fn(agg))) - - transform_fn = lambda agg: agg + 1 - pipeline.transform_fn = transform_fn - img = pipeline((0, 1), (0, 1), 2, 2) - self.assertTrue(img.equals(color_fn(transform_fn(agg)))) - - pipeline = ds.Pipeline(df, ds.Point('x', 'y'), ds.sum('f64')) - img = pipeline((0, 1), (0, 1), 2, 2) - agg = cvs.points(df, 'x', 'y', ds.sum('f64')) - self.assertTrue(img.equals(tf.shade(agg))) diff --git a/tests/test_datatable.py b/tests/test_datatable.py deleted file mode 100644 index ec68ce12..00000000 --- a/tests/test_datatable.py +++ /dev/null @@ -1,17 +0,0 @@ -import unittest -import datatable as dt -from datatable.internal import frame_integrity_check -from datatable import ltype - -class TestDatatable(unittest.TestCase): - def test_fread(self): - d0 = dt.fread( - "L,T,U,D\n" - "true,True,TRUE,1\n" - "false,False,FALSE,0\n" - ",,,\n" - ) - frame_integrity_check(d0) - assert d0.shape == (3, 4) - assert d0.ltypes == (ltype.bool,) * 4 - assert d0.to_list() == [[True, False, None]] * 4 diff --git a/tests/test_dipy.py b/tests/test_dipy.py index c1827120..6e5f8b33 100644 --- a/tests/test_dipy.py +++ b/tests/test_dipy.py @@ -64,6 +64,6 @@ def test_fbc(self): fbc.get_points_rfbc_thresholded(0, emphasis=0.01) # check mean RFBC against tested value - npt.assert_almost_equal(np.mean(rfbc_orig), 1.0500466494329224) + npt.assert_almost_equal(np.mean(rfbc_orig), 1.0500466494329224, decimal=4) diff --git a/tests/test_dlib.py b/tests/test_dlib.py deleted file mode 100644 index 9e8ff28d..00000000 --- a/tests/test_dlib.py +++ /dev/null @@ -1,14 +0,0 @@ -import unittest - -import cv2 -import dlib - - -class TestDLib(unittest.TestCase): - def test_dlib_face_detector(self): - detector = dlib.get_frontal_face_detector() - image = cv2.imread('/input/tests/data/face.jpg') - image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) - faces = detector(image_gray, 1) - - self.assertEqual(len(faces), 1) diff --git a/tests/test_essentia.py b/tests/test_essentia.py deleted file mode 100644 index 749b9466..00000000 --- a/tests/test_essentia.py +++ /dev/null @@ -1,7 +0,0 @@ -import unittest - -from essentia.standard import Windowing - -class TestEssentia(unittest.TestCase): - def test_windowing(self): - Windowing(type = 'hann') diff --git a/tests/test_fastai.py b/tests/test_fastai.py index edfd402e..33a436a5 100644 --- a/tests/test_fastai.py +++ b/tests/test_fastai.py @@ -1,27 +1,36 @@ import unittest import fastai - from fastai.tabular.all import * + class TestFastAI(unittest.TestCase): - def test_has_version(self): - self.assertGreater(len(fastai.__version__), 2) - - # based on https://github.com/fastai/fastai/blob/master/tests/test_torch_core.py#L17 - def test_torch_tensor(self): - a = tensor([1, 2, 3]) - b = torch.tensor([1, 2, 3]) - - self.assertTrue(torch.all(a == b)) - - def test_tabular(self): - dls = TabularDataLoaders.from_csv( - "/input/tests/data/train.csv", - cont_names=["pixel"+str(i) for i in range(784)], - y_names='label', - procs=[FillMissing, Categorify, Normalize]) - learn = tabular_learner(dls, layers=[200, 100]) - learn.fit_one_cycle(n_epoch=1) - - self.assertGreater(learn.smooth_loss, 0) + # Basic import + def test_basic(self): + import fastai + import fastcore + import fastprogress + import fastdownload + + def test_has_version(self): + self.assertGreater(len(fastai.__version__), 2) + + # based on https://github.com/fastai/fastai/blob/master/tests/test_torch_core.py#L17 + def test_torch_tensor(self): + a = tensor([1, 2, 3]) + b = torch.tensor([1, 2, 3]) + + self.assertTrue(torch.all(a == b)) + + def test_tabular(self): + dls = TabularDataLoaders.from_csv( + "/input/tests/data/train.csv", + cont_names=["pixel" + str(i) for i in range(784)], + y_names="label", + procs=[FillMissing, Categorify, Normalize], + ) + learn = tabular_learner(dls, layers=[200, 100]) + with learn.no_bar(): + learn.fit_one_cycle(n_epoch=1) + + self.assertGreater(learn.smooth_loss, 0) diff --git a/tests/test_fbprophet.py b/tests/test_fbprophet.py deleted file mode 100644 index ecb69697..00000000 --- a/tests/test_fbprophet.py +++ /dev/null @@ -1,16 +0,0 @@ -import unittest - -import numpy as np -import pandas as pd - -from fbprophet import Prophet - -class TestFbProphet(unittest.TestCase): - def test_fit(self): - train = pd.DataFrame({ - 'ds': np.array(['2012-05-18', '2012-05-20']), - 'y': np.array([38.23, 21.25]) - }) - - forecaster = Prophet(mcmc_samples=1) - forecaster.fit(train, control={'adapt_engaged': False}) diff --git a/tests/test_flax.py b/tests/test_flax.py index d1d1b63e..a420ab54 100644 --- a/tests/test_flax.py +++ b/tests/test_flax.py @@ -1,15 +1,52 @@ import unittest +import jax import jax.numpy as jnp import numpy as np +import optax from flax import linen as nn +from flax.training import train_state class TestFlax(unittest.TestCase): - def test_bla(self): + def test_pooling(self): x = jnp.full((1, 3, 3, 1), 2.) mul_reduce = lambda x, y: x * y y = nn.pooling.pool(x, 1., mul_reduce, (2, 2), (1, 1), 'VALID') np.testing.assert_allclose(y, np.full((1, 2, 2, 1), 2. ** 4)) + + def test_cnn(self): + class CNN(nn.Module): + @nn.compact + def __call__(self, x): + x = nn.Conv(features=32, kernel_size=(3, 3))(x) + x = nn.relu(x) + x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2)) + x = nn.Conv(features=64, kernel_size=(3, 3))(x) + x = nn.relu(x) + x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2)) + x = x.reshape((x.shape[0], -1)) + x = nn.Dense(features=256)(x) + x = nn.relu(x) + x = nn.Dense(features=120)(x) + x = nn.log_softmax(x) + return x + + def create_train_state(rng, learning_rate, momentum): + cnn = CNN() + params = cnn.init(rng, jnp.ones([1, 224, 224, 3]))['params'] + tx = optax.sgd(learning_rate, momentum) + return train_state.TrainState.create( + apply_fn=cnn.apply, params=params, tx=tx) + + rng = jax.random.PRNGKey(0) + rng, init_rng = jax.random.split(rng) + + learning_rate = 2e-5 + momentum = 0.9 + state = create_train_state(init_rng, learning_rate, momentum) + self.assertEqual(0, state.step) + + diff --git a/tests/test_gcs.py b/tests/test_gcs.py index 9902a877..94da58c9 100644 --- a/tests/test_gcs.py +++ b/tests/test_gcs.py @@ -3,12 +3,14 @@ from unittest.mock import Mock from kaggle_gcp import KaggleKernelCredentials, init_gcs -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from google.cloud import storage def _make_credentials(): import google.auth.credentials - return Mock(spec=google.auth.credentials.Credentials) + credentials = Mock(spec=google.auth.credentials.Credentials) + credentials.universe_domain = 'googleapis.com' + return credentials class TestStorage(unittest.TestCase): diff --git a/tests/test_geopandas.py b/tests/test_geopandas.py deleted file mode 100644 index e2bb4583..00000000 --- a/tests/test_geopandas.py +++ /dev/null @@ -1,16 +0,0 @@ -import unittest - -import geopandas - -class TestGeopandas(unittest.TestCase): - def test_read(self): - df = geopandas.read_file(geopandas.datasets.get_path('nybb')) - self.assertTrue(df.size > 1) - - def test_spatial_join(self): - cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities')) - world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres')) - countries = world[['geometry', 'name']] - countries = countries.rename(columns={'name':'country'}) - cities_with_country = geopandas.sjoin(cities, countries, how="inner", op='intersects') - self.assertTrue(cities_with_country.size > 1) \ No newline at end of file diff --git a/tests/test_geoviews.py b/tests/test_geoviews.py deleted file mode 100644 index 7b270a76..00000000 --- a/tests/test_geoviews.py +++ /dev/null @@ -1,11 +0,0 @@ -import unittest - -import geoviews.feature as gf - -from cartopy import crs - -class TestGeoviews(unittest.TestCase): - def test_viz(self): - (gf.ocean + gf.land + gf.ocean * gf.land * gf.coastline * gf.borders).options( - 'Feature', projection=crs.Geostationary(), global_extent=True - ).cols(3) diff --git a/tests/test_ggplot.py b/tests/test_ggplot.py deleted file mode 100644 index 30aec29f..00000000 --- a/tests/test_ggplot.py +++ /dev/null @@ -1,12 +0,0 @@ -import unittest -import os.path - -from ggplot import * - -class TestGgplot(unittest.TestCase): - - def test_plot(self): - p = ggplot(aes(x='mpg'), data=mtcars) + geom_histogram() - p.save("myplot.png") - - self.assertTrue(os.path.isfile("myplot.png")) diff --git a/tests/test_google_genai_patch.py b/tests/test_google_genai_patch.py new file mode 100644 index 00000000..9d225763 --- /dev/null +++ b/tests/test_google_genai_patch.py @@ -0,0 +1,55 @@ +import json +import unittest +import threading + +from test.support.os_helper import EnvironmentVarGuard +from urllib.parse import urlparse + +from http.server import BaseHTTPRequestHandler, HTTPServer + +class HTTPHandler(BaseHTTPRequestHandler): + called = False + path = None + headers = {} + + def do_HEAD(self): + self.send_response(200) + + def do_POST(self): + HTTPHandler.path = self.path + HTTPHandler.headers = self.headers + HTTPHandler.called = True + self.send_response(200) + self.send_header("Content-type", "application/json") + self.end_headers() + +class TestGoogleGenAiPatch(unittest.TestCase): + endpoint = "http://127.0.0.1:80" + + def test_proxy_enabled(self): + env = EnvironmentVarGuard() + secrets_token = "secrets_token" + proxy_token = "proxy_token" + env.set("KAGGLE_USER_SECRETS_TOKEN", secrets_token) + env.set("KAGGLE_DATA_PROXY_TOKEN", proxy_token) + env.set("KAGGLE_DATA_PROXY_URL", self.endpoint) + server_address = urlparse(self.endpoint) + with env: + with HTTPServer((server_address.hostname, server_address.port), HTTPHandler) as httpd: + threading.Thread(target=httpd.serve_forever).start() + from google import genai + api_key = "NotARealAPIKey" + client = genai.Client(api_key = api_key) + try: + client.models.generate_content( + model="gemini-2.0-flash-exp", + contents="What's the largest planet in our solar system?" + ) + except: + pass + httpd.shutdown() + self.assertTrue(HTTPHandler.called) + self.assertIn("/palmapi", HTTPHandler.path) + self.assertEqual(proxy_token, HTTPHandler.headers["x-kaggle-proxy-data"]) + self.assertEqual("Bearer {}".format(secrets_token), HTTPHandler.headers["x-kaggle-authorization"]) + self.assertEqual(api_key, HTTPHandler.headers["x-goog-api-key"]) diff --git a/tests/test_google_generativeai_patch.py b/tests/test_google_generativeai_patch.py new file mode 100644 index 00000000..87ac4ecd --- /dev/null +++ b/tests/test_google_generativeai_patch.py @@ -0,0 +1,55 @@ +import json +import unittest +import threading + +from test.support.os_helper import EnvironmentVarGuard +from urllib.parse import urlparse + +from http.server import BaseHTTPRequestHandler, HTTPServer + +class HTTPHandler(BaseHTTPRequestHandler): + called = False + path = None + headers = {} + + def do_HEAD(self): + self.send_response(200) + + def do_GET(self): + HTTPHandler.path = self.path + HTTPHandler.headers = self.headers + HTTPHandler.called = True + self.send_response(200) + self.send_header("Content-type", "application/json") + self.end_headers() + +class TestGoogleGenerativeAiPatch(unittest.TestCase): + endpoint = "http://127.0.0.1:80" + + def test_proxy_enabled(self): + env = EnvironmentVarGuard() + secrets_token = "secrets_token" + proxy_token = "proxy_token" + env.set("KAGGLE_USER_SECRETS_TOKEN", secrets_token) + env.set("KAGGLE_DATA_PROXY_TOKEN", proxy_token) + env.set("KAGGLE_DATA_PROXY_URL", self.endpoint) + env.set("KAGGLE_GRPC_DATA_PROXY_URL", "http://127.0.0.1:50001") + env.set("KAGGLE_GOOGLE_GENERATIVE_AI_USE_REST_ONLY", "True") + server_address = urlparse(self.endpoint) + with env: + with HTTPServer((server_address.hostname, server_address.port), HTTPHandler) as httpd: + threading.Thread(target=httpd.serve_forever).start() + import google.generativeai as palm + api_key = "NotARealAPIKey" + palm.configure(api_key = api_key) + try: + for _ in palm.list_models(): + pass + except: + pass + httpd.shutdown() + self.assertTrue(HTTPHandler.called) + self.assertIn("/palmapi", HTTPHandler.path) + self.assertEqual(proxy_token, HTTPHandler.headers["x-kaggle-proxy-data"]) + self.assertEqual("Bearer {}".format(secrets_token), HTTPHandler.headers["x-kaggle-authorization"]) + self.assertEqual(api_key, HTTPHandler.headers["x-goog-api-key"]) diff --git a/tests/test_google_generativeai_patch_disabled.py b/tests/test_google_generativeai_patch_disabled.py new file mode 100644 index 00000000..2f34af21 --- /dev/null +++ b/tests/test_google_generativeai_patch_disabled.py @@ -0,0 +1,46 @@ +import json +import unittest +import threading + +from test.support.os_helper import EnvironmentVarGuard +from urllib.parse import urlparse + +from http.server import BaseHTTPRequestHandler, HTTPServer + +class HTTPHandler(BaseHTTPRequestHandler): + called = False + + def do_HEAD(self): + self.send_response(200) + + def do_GET(self): + print('YO MOD', self.path) + HTTPHandler.called = True + self.send_response(200) + self.send_header("Content-type", "application/json") + self.end_headers() + +class TestGoogleGenerativeAiPatchDisabled(unittest.TestCase): + http_endpoint = "http://127.0.0.1:80" + grpc_endpoint = "http://127.0.0.1:50001" + + def test_disabled(self): + env = EnvironmentVarGuard() + env.set("KAGGLE_USER_SECRETS_TOKEN", "foobar") + env.set("KAGGLE_DATA_PROXY_TOKEN", "foobar") + env.set("KAGGLE_DATA_PROXY_URL", self.http_endpoint) + env.set("KAGGLE_GRPC_DATA_PROXY_URL", self.grpc_endpoint) + env.set("KAGGLE_DISABLE_GOOGLE_GENERATIVE_AI_INTEGRATION", "True") + server_address = urlparse(self.http_endpoint) + with env: + with HTTPServer((server_address.hostname, server_address.port), HTTPHandler) as httpd: + threading.Thread(target=httpd.serve_forever).start() + import google.generativeai as palm + palm.configure(api_key = "NotARealAPIKey") + try: + for _ in palm.list_models(): + pass + except: + pass + httpd.shutdown() + self.assertFalse(HTTPHandler.called) diff --git a/tests/test_google_import_adk.py b/tests/test_google_import_adk.py new file mode 100644 index 00000000..9ae11314 --- /dev/null +++ b/tests/test_google_import_adk.py @@ -0,0 +1,31 @@ +import json +import unittest +import threading +from urllib.parse import urlparse + +class TestGoogleADK(unittest.TestCase): + + def define_agent(self): + from google.adk.agents import Agent + from google.adk.models.google_llm import Gemini + from google.adk.runners import InMemoryRunner + from google.adk.tools import google_search + from google.genai import types + + retry_config = types.HttpRetryOptions( + attempts=5, # Maximum retry attempts + exp_base=7, # Delay multiplier + initial_delay=1, # Initial delay before first retry (in seconds) + http_status_codes=[429, 500, 503, 504] # Retry on these HTTP errors + ) + + root_agent = Agent( + name="helpful_assistant", + model=Gemini( + model="gemini-2.0-flash-lite", + retry_options=retry_config + ), + description="A simple agent that can answer general questions.", + instruction="You are a helpful assistant. Use Google Search for current info or if unsure.", + tools=[google_search], + ) diff --git a/tests/test_hf_datasets.py b/tests/test_hf_datasets.py new file mode 100644 index 00000000..048462a6 --- /dev/null +++ b/tests/test_hf_datasets.py @@ -0,0 +1,21 @@ +import unittest + +import datasets +import pandas as pd + +class TestHuggingFaceDatasets(unittest.TestCase): + + def test_map(self): + def some_func(batch): + batch['label'] = 'foo' + return batch + + df = datasets.Dataset.from_dict({'text': ['Kaggle rocks!']}) + mapped_df = df.map(some_func) + + self.assertEqual('foo', mapped_df[0]['label']) + + def test_load_dataset(self): + dataset = datasets.load_dataset("csv", data_files="/input/tests/data/train.csv") + full_data = pd.DataFrame(dataset['train']) + self.assertFalse(full_data.empty) \ No newline at end of file diff --git a/tests/test_imports.py b/tests/test_imports.py index 39595773..6c429516 100644 --- a/tests/test_imports.py +++ b/tests/test_imports.py @@ -3,7 +3,5 @@ class TestImport(unittest.TestCase): # Basic import tests for packages without any. def test_basic(self): - import bq_helper - import cleverhans - import tensorflow_gcs_config import tensorflow_datasets + import segment_anything diff --git a/tests/test_jax.py b/tests/test_jax.py index 1847ceec..f8eca3bb 100644 --- a/tests/test_jax.py +++ b/tests/test_jax.py @@ -1,9 +1,12 @@ import unittest + +import os import time +import jax import jax.numpy as np -from common import gpu_test +from common import gpu_test, isGPU from jax import grad, jit @@ -16,3 +19,7 @@ def test_grad(self): grad_tanh = grad(self.tanh) ag = grad_tanh(1.0) self.assertEqual(0.4199743, ag) + + def test_backend(self): + expected_backend = 'cpu' if not isGPU() else 'gpu' + self.assertEqual(expected_backend, jax.default_backend()) diff --git a/tests/test_jiter.py b/tests/test_jiter.py new file mode 100644 index 00000000..6b31925e --- /dev/null +++ b/tests/test_jiter.py @@ -0,0 +1,9 @@ +import unittest + +from distutils.version import StrictVersion + +import jiter + +class TestJiter(unittest.TestCase): + def test_version(self): + self.assertEqual(StrictVersion(jiter.__version__), StrictVersion("0.10.0")) diff --git a/tests/test_jupyter_nbconvert.py b/tests/test_jupyter_nbconvert.py index b3925993..f2549a50 100644 --- a/tests/test_jupyter_nbconvert.py +++ b/tests/test_jupyter_nbconvert.py @@ -3,7 +3,7 @@ import subprocess class TestJupyterNbconvert(unittest.TestCase): - def test_nbconvert(self): + def test_nbconvert_to_notebook(self): result = subprocess.run([ 'jupyter', 'nbconvert', @@ -17,4 +17,20 @@ def test_nbconvert(self): ], stdout=subprocess.PIPE) self.assertEqual(0, result.returncode) - self.assertTrue(b'999' in result.stdout) \ No newline at end of file + self.assertTrue(b'999' in result.stdout) + + def test_nbconvert_to_html(self): + result = subprocess.run([ + 'jupyter', + 'nbconvert', + '--to', + 'html', + '--template', + '/opt/kaggle/nbconvert-extensions.tpl', + '--execute', + '--stdout', + '/input/tests/data/notebook.ipynb', + ], stdout=subprocess.PIPE) + + self.assertEqual(0, result.returncode) + self.assertTrue(b'999' in result.stdout) diff --git a/tests/test_jupyter_server.py b/tests/test_jupyter_server.py new file mode 100644 index 00000000..7aa91175 --- /dev/null +++ b/tests/test_jupyter_server.py @@ -0,0 +1,15 @@ +import os +import unittest + + +class TestJupyterServer(unittest.TestCase): + def test_version(self): + from packaging.version import parse as parse_version + from importlib.metadata import version as pckg_version + + self.assertTrue(parse_version(pckg_version("jupyter_server")) >= parse_version("2.0")) + + def test_terminals(self): + import jupyter_server_terminals + + self.assertTrue(hasattr(jupyter_server_terminals, "version_info")) diff --git a/tests/test_jupyterlab_lsp.py b/tests/test_jupyterlab_lsp.py new file mode 100644 index 00000000..1a23494d --- /dev/null +++ b/tests/test_jupyterlab_lsp.py @@ -0,0 +1,31 @@ +import os +import unittest + +from jupyter_server.serverapp import ServerApp + +# Adapted from: +# https://github.com/jupyter-lsp/jupyterlab-lsp/blob/ce76fab170feea506faf9ef47e4bd6a468c24313/python_packages/jupyter_lsp/jupyter_lsp/tests/test_extension.py +class TestJupyterLabLsp(unittest.TestCase): + def test_serverextension_path(self): + import jupyter_lsp + paths = jupyter_lsp._jupyter_server_extension_paths() + for path in paths: + self.assertTrue(__import__(path["module"])) + + + def test_serverextension(self): + app = ServerApp() + app.initialize( + ["--ServerApp.jpserver_extensions={'jupyter_lsp.serverextension': True}"], + new_httpserver=False, + ) + self.assertTrue(app.language_server_manager) + + found_lsp = False + for r in app.web_app.default_router.rules: + for rr in r.target.rules: + if "/lsp/" in str(rr.matcher.regex): + found_lsp = True + + self.assertTrue(found_lsp, "didn't install the /lsp/ route") + app.stop() diff --git a/tests/test_kaggle_module_resolver.py b/tests/test_kaggle_module_resolver.py new file mode 100644 index 00000000..bb1f243e --- /dev/null +++ b/tests/test_kaggle_module_resolver.py @@ -0,0 +1,117 @@ +import unittest + +import os +import threading +import json + +import tensorflow as tf +import tensorflow_hub as hub + +from http.server import BaseHTTPRequestHandler, HTTPServer +from test.support.os_helper import EnvironmentVarGuard +from contextlib import contextmanager +from kagglehub.exceptions import BackendError + +MOUNT_PATH = "/kaggle/input" + +@contextmanager +def create_test_server(handler_class): + hostname = 'localhost' + port = 8080 + addr = f"http://{hostname}:{port}" + + # Simulates we are inside a Kaggle environment. + env = EnvironmentVarGuard() + env.set('KAGGLE_KERNEL_RUN_TYPE', 'Interactive') + env.set('KAGGLE_USER_SECRETS_TOKEN', 'foo jwt token') + env.set('KAGGLE_DATA_PROXY_TOKEN', 'foo proxy token') + env.set('KAGGLE_DATA_PROXY_URL', addr) + + with env: + with HTTPServer((hostname, port), handler_class) as test_server: + threading.Thread(target=test_server.serve_forever).start() + + try: + yield addr + finally: + test_server.shutdown() + +class HubHTTPHandler(BaseHTTPRequestHandler): + def do_GET(self): + self.send_response(200) + self.send_header('Content-Type', 'application/gzip') + self.end_headers() + + with open('/input/tests/data/model.tar.gz', 'rb') as model_archive: + self.wfile.write(model_archive.read()) + +class KaggleJwtHandler(BaseHTTPRequestHandler): + def do_POST(self): + if self.path.endswith("AttachDatasourceUsingJwtRequest"): + content_length = int(self.headers["Content-Length"]) + request = json.loads(self.rfile.read(content_length)) + model_ref = request["modelRef"] + + self.send_response(200) + self.send_header("Content-type", "application/json") + self.end_headers() + + if model_ref['ModelSlug'] == 'unknown': + self.wfile.write(bytes(json.dumps({ + "wasSuccessful": False, + }), "utf-8")) + return + + # Load the files + mount_slug = f"{model_ref['ModelSlug']}/{model_ref['Framework']}/{model_ref['InstanceSlug']}/{model_ref['VersionNumber']}" + os.makedirs(os.path.dirname(os.path.join(MOUNT_PATH, mount_slug))) + os.symlink('/input/tests/data/saved_model/', os.path.join(MOUNT_PATH, mount_slug), target_is_directory=True) + + # Return the response + self.wfile.write(bytes(json.dumps({ + "wasSuccessful": True, + "result": { + "mountSlug": mount_slug, + }, + }), "utf-8")) + else: + self.send_response(404) + self.wfile.write(bytes(f"Unhandled path: {self.path}", "utf-8")) + +class TestKaggleModuleResolver(unittest.TestCase): + def test_kaggle_resolver_long_url_succeeds(self): + model_url = "https://kaggle.com/models/foo/foomodule/frameworks/TensorFlow2/variations/barvar/versions/2" + with create_test_server(KaggleJwtHandler) as addr: + test_inputs = tf.ones([1,4]) + layer = hub.KerasLayer(model_url) + self.assertEqual([1, 1], layer(test_inputs).shape) + # Delete the files that were created in KaggleJwtHandler's do_POST method + os.unlink(os.path.join(MOUNT_PATH, "foomodule/tensorflow2/barvar/2")) + os.rmdir(os.path.dirname(os.path.join(MOUNT_PATH, "foomodule/tensorflow2/barvar/2"))) + + def test_kaggle_resolver_short_url_succeeds(self): + model_url = "https://kaggle.com/models/foo/foomodule/TensorFlow2/barvar/2" + with create_test_server(KaggleJwtHandler) as addr: + test_inputs = tf.ones([1,4]) + layer = hub.KerasLayer(model_url) + self.assertEqual([1, 1], layer(test_inputs).shape) + # Delete the files that were created in KaggleJwtHandler's do_POST method + os.unlink(os.path.join(MOUNT_PATH, "foomodule/tensorflow2/barvar/2")) + os.rmdir(os.path.dirname(os.path.join(MOUNT_PATH, "foomodule/tensorflow2/barvar/2"))) + + def test_kaggle_resolver_not_attached_throws(self): + with create_test_server(KaggleJwtHandler) as addr: + with self.assertRaises(BackendError): + hub.KerasLayer("https://kaggle.com/models/foo/unknown/frameworks/TensorFlow2/variations/barvar/versions/2") + + def test_http_resolver_succeeds(self): + with create_test_server(HubHTTPHandler) as addr: + test_inputs = tf.ones([1,4]) + layer = hub.KerasLayer(f'{addr}/model.tar.gz') + self.assertEqual([1, 1], layer(test_inputs).shape) + + def test_local_path_resolver_succeeds(self): + test_inputs = tf.ones([1,4]) + layer = hub.KerasLayer('/input/tests/data/saved_model') + + self.assertEqual([1, 1], layer(test_inputs).shape) diff --git a/tests/test_kagglehub.py b/tests/test_kagglehub.py new file mode 100644 index 00000000..f2c3e2a6 --- /dev/null +++ b/tests/test_kagglehub.py @@ -0,0 +1,17 @@ +import unittest +from unittest import mock + +import kagglehub + + +class TestKagglehub(unittest.TestCase): + def test_login(self): + with self.assertLogs('kagglehub', level='INFO') as l: + with mock.patch("builtins.input") as mock_input: + with mock.patch("getpass.getpass") as mock_getpass: + mock_input.side_effect = ["lastplacelarry"] + mock_getpass.return_value = "some-key" + + kagglehub.login(validate_credentials=False) + + self.assertIn("credentials set", l.output[0]) diff --git a/tests/test_keras.py b/tests/test_keras.py new file mode 100644 index 00000000..5dc4610d --- /dev/null +++ b/tests/test_keras.py @@ -0,0 +1,74 @@ +import unittest + +import numpy as np +import os + +os.environ["KERAS_BACKEND"] = "tensorflow" + +import keras + +class TestKeras(unittest.TestCase): + def test_train(self): + path = '/input/tests/data/mnist.npz' + with np.load(path) as f: + x_train, y_train = f['x_train'], f['y_train'] + x_test, y_test = f['x_test'], f['y_test'] + + + # Scale images to the [0, 1] range + x_train = x_train.astype("float32") / 255 + x_train = x_train[:100, :] + y_train = y_train[:100] + x_test = x_test.astype("float32") / 255 + x_test = x_test[:100, :] + y_test = y_test[:100] + # Make sure images have shape (28, 28, 1) + x_train = np.expand_dims(x_train, -1) + x_test = np.expand_dims(x_test, -1) + + # Model parameters + num_classes = 10 + input_shape = (28, 28, 1) + + model = keras.Sequential( + [ + keras.layers.Input(shape=input_shape), + keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), + keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), + keras.layers.MaxPooling2D(pool_size=(2, 2)), + keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"), + keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"), + keras.layers.GlobalAveragePooling2D(), + keras.layers.Dropout(0.5), + keras.layers.Dense(num_classes, activation="softmax"), + ] + ) + + model.compile( + loss=keras.losses.SparseCategoricalCrossentropy(), + optimizer=keras.optimizers.Adam(learning_rate=1e-3), + metrics=[ + keras.metrics.SparseCategoricalAccuracy(name="acc"), + ], + ) + + batch_size = 128 + epochs = 1 + + callbacks = [ + keras.callbacks.ModelCheckpoint(filepath="model_at_epoch_{epoch}.keras"), # Saves model checkpoint while training + keras.callbacks.EarlyStopping(monitor="val_loss", patience=2), + ] + + model.fit( + x_train, + y_train, + batch_size=batch_size, + epochs=epochs, + validation_split=0.15, + callbacks=callbacks, + ) + score = model.evaluate(x_test, y_test, verbose=0) + print(score) + self.assertNotEqual(0, score) + diff --git a/tests/test_keras_cv.py b/tests/test_keras_cv.py new file mode 100644 index 00000000..fb5d912c --- /dev/null +++ b/tests/test_keras_cv.py @@ -0,0 +1,20 @@ +import unittest + +import keras_cv +import keras +import numpy as np + +from utils.kagglehub import create_test_kagglehub_server + +class TestKerasCV(unittest.TestCase): + def test_inference(self): + with create_test_kagglehub_server(): + classifier = keras_cv.models.ImageClassifier.from_preset( + 'efficientnetv2_b0_imagenet_classifier', + load_weights=False, # load randomly initialized model from preset architecture with weights + ) + image = keras.utils.load_img('/input/tests/data/face.jpg') + image = np.array(image) + predictions = classifier.predict(np.expand_dims(image, axis=0)) + top_classes = predictions[0].argsort(axis=-1) + self.assertEqual(1000, len(top_classes)) \ No newline at end of file diff --git a/tests/test_keras_nlp.py b/tests/test_keras_nlp.py new file mode 100644 index 00000000..06defce0 --- /dev/null +++ b/tests/test_keras_nlp.py @@ -0,0 +1,19 @@ +import unittest + +import keras_nlp +import keras +import numpy as np + +from utils.kagglehub import create_test_kagglehub_server + +class TestKerasNLP(unittest.TestCase): + def test_fit(self): + with create_test_kagglehub_server(): + classifier = keras_nlp.models.BertClassifier.from_preset( + 'bert_tiny_en_uncased', + load_weights=False, # load randomly initialized model from preset architecture with weights + num_classes=2, + activation="softmax", + ) + result = classifier.predict(['What an amazing movie!']) + self.assertEqual((1, 2), result.shape) \ No newline at end of file diff --git a/tests/test_kmapper.py b/tests/test_kmapper.py deleted file mode 100644 index c75deea3..00000000 --- a/tests/test_kmapper.py +++ /dev/null @@ -1,7 +0,0 @@ -import unittest - -import kmapper as km - -class TestKMapper(unittest.TestCase): - def test_init(self): - km.KeplerMapper() diff --git a/tests/test_kornia.py b/tests/test_kornia.py index dcbbf3cc..dd41d16d 100644 --- a/tests/test_kornia.py +++ b/tests/test_kornia.py @@ -7,13 +7,13 @@ class TestKornia(unittest.TestCase): def test_imread_opencv(self): img = cv2.imread('/input/tests/data/dot.png') - img_t = kornia.image_to_tensor(img) + img_t = kornia.utils.image_to_tensor(img) self.assertEqual(img.shape, (1, 1, 3)) self.assertEqual(img_t.shape, (3, 1, 1)) def test_grayscale_torch(self): img_rgb = torch.rand(2, 3, 4, 5) - img_gray = kornia.rgb_to_grayscale(img_rgb) + img_gray = kornia.color.rgb_to_grayscale(img_rgb) self.assertEqual(img_gray.shape, (2, 1, 4, 5)) diff --git a/tests/test_levenshtein.py b/tests/test_levenshtein.py deleted file mode 100644 index 2ea45c98..00000000 --- a/tests/test_levenshtein.py +++ /dev/null @@ -1,9 +0,0 @@ -import unittest - -import Levenshtein - -class TestLevenshtein(unittest.TestCase): - def test_distance(self): - distance = Levenshtein.distance('Levenshtein', 'Lenvinsten') - - self.assertEqual(4, distance) \ No newline at end of file diff --git a/tests/test_lightgbm.py b/tests/test_lightgbm.py index f7c5ada4..bcdbb1a6 100644 --- a/tests/test_lightgbm.py +++ b/tests/test_lightgbm.py @@ -30,7 +30,7 @@ def test_cpu(self): lgb_train, num_boost_round=1, valid_sets=lgb_eval, - early_stopping_rounds=1) + callbacks=[lgb.early_stopping(stopping_rounds=1)]) self.assertEqual(1, gbm.best_iteration) @@ -57,7 +57,7 @@ def test_gpu(self): lgb_train, num_boost_round=1, valid_sets=lgb_eval, - early_stopping_rounds=1) + callbacks=[lgb.early_stopping(stopping_rounds=1)]) self.assertEqual(1, gbm.best_iteration) diff --git a/tests/test_matplotlib.py b/tests/test_matplotlib.py index 1cbc939a..125ccda4 100644 --- a/tests/test_matplotlib.py +++ b/tests/test_matplotlib.py @@ -1,6 +1,9 @@ import unittest import os.path +from distutils.version import StrictVersion + +import matplotlib import matplotlib.pyplot as plt import numpy as np diff --git a/tests/test_mxnet.py b/tests/test_mxnet.py deleted file mode 100644 index 59a3914e..00000000 --- a/tests/test_mxnet.py +++ /dev/null @@ -1,37 +0,0 @@ -import unittest -from common import gpu_test - -import mxnet as mx -from gluonnlp import Vocab -from gluonnlp.data import count_tokens - -from gluoncv.data.transforms.image import imresize - - -class TestMxNet(unittest.TestCase): - def test_array(self): - x = mx.nd.array([[1, 2, 3], [4, 5, 6]]) - - self.assertEqual((2, 3), x.shape) - - @gpu_test - def test_array_gpu(self): - x = mx.nd.array([2, 2, 2], ctx=mx.gpu(0)) - y = mx.nd.array([1, 1, 1], ctx=mx.gpu(0)) - self.assertEqual(3, ((x - y).sum().asscalar())) - - def test_gluon_nlp(self): - # get corpus statistics - counter = count_tokens(['alpha', 'beta', 'gamma', 'beta']) - # create Vocab - vocab = Vocab(counter) - # find index based on token - self.assertEqual(4, vocab['beta']) - - def test_gluon_cv(self): - # create fake RGB image of 300x300 of shape: Height x Width x Channel as OpenCV expects - img = mx.random.uniform(0, 255, (300, 300, 3)).astype('uint8') - # resize image to 200x200. This call uses OpenCV - # GluonCV is not of much use if OpenCV is not there or fails - img = imresize(img, 200, 200) - self.assertEqual((200, 200, 3), img.shape) diff --git a/tests/test_natural_language.py b/tests/test_natural_language.py index e2939013..3586b8de 100644 --- a/tests/test_natural_language.py +++ b/tests/test_natural_language.py @@ -4,7 +4,7 @@ from unittest.mock import Mock, patch from kaggle_gcp import KaggleKernelCredentials, init_natural_language -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from google.cloud import language def _make_credentials(): diff --git a/tests/test_nbdev.py b/tests/test_nbdev.py new file mode 100644 index 00000000..d5c6b484 --- /dev/null +++ b/tests/test_nbdev.py @@ -0,0 +1,8 @@ +import unittest + +import nbdev + +class TestNbdev(unittest.TestCase): + def test(self): + self.assertGreater(len(nbdev.__version__), 0) + diff --git a/tests/test_nnabla.py b/tests/test_nnabla.py deleted file mode 100644 index ffb37fa4..00000000 --- a/tests/test_nnabla.py +++ /dev/null @@ -1,28 +0,0 @@ -import unittest - -import numpy as np -import nnabla as nn -import nnabla.functions as F -from nnabla.ext_utils import get_extension_context - -from common import gpu_test - - -class TestNNabla(unittest.TestCase): - def test_addition(self): - # entry variables - a = nn.Variable.from_numpy_array(np.random.random()) - b = nn.Variable.from_numpy_array(np.random.random()) - - # add operation - c = a + b - - # forward - c.forward() - - self.assertAlmostEqual(c.d, a.d + b.d, places=3) - - @gpu_test - def test_cuda_ext(self): - ctx = get_extension_context('cudnn', device_id='0') - nn.set_default_context(ctx) diff --git a/tests/test_numba.py b/tests/test_numba.py new file mode 100644 index 00000000..1b77ee3e --- /dev/null +++ b/tests/test_numba.py @@ -0,0 +1,35 @@ +import unittest + +import numpy as np +from numba import jit, cuda + +from common import gpu_test + +class TestNumba(unittest.TestCase): + def test_jit(self): + x = np.arange(100).reshape(10, 10) + + @jit(nopython=True) # Set "nopython" mode for best performance, equivalent to @njit + def go_fast(a): # Function is compiled to machine code when called the first time + trace = 0.0 + for i in range(a.shape[0]): # Numba likes loops + trace += np.tanh(a[i, i]) # Numba likes NumPy functions + return a + trace # Numba likes NumPy broadcasting + + self.assertEqual(10, go_fast(x).shape[0]) + + @gpu_test + def test_cuda_jit(self): + x = np.arange(10) + + @cuda.jit + def increment_by_one(an_array): + pos = cuda.grid(1) + if pos < an_array.size: + an_array[pos] += 1 + + threadsperblock = 32 + blockspergrid = (x.size + (threadsperblock - 1)) + self.assertEqual(0, x[0]) + increment_by_one[blockspergrid, threadsperblock](x) + self.assertEqual(1, x[0]) diff --git a/tests/test_numpy.py b/tests/test_numpy.py index 18f74b8c..ab7ec03c 100644 --- a/tests/test_numpy.py +++ b/tests/test_numpy.py @@ -1,16 +1,13 @@ import unittest +from distutils.version import StrictVersion + import numpy as np -from numpy.distutils.system_info import get_info +import io +from contextlib import redirect_stdout -class TestNumpy(unittest.TestCase): +class TestNumpy(unittest.TestCase): def test_array(self): array = np.array([1, 3]) self.assertEqual((2,), array.shape) - - # Numpy must be linked to the MKL. (Occasionally, a third-party package will muck up the installation - # and numpy will be reinstalled with an OpenBLAS backing.) - def test_mkl(self): - # This will throw an exception if the MKL is not linked correctly or return an empty dict. - self.assertTrue(get_info("blas_mkl")) diff --git a/tests/test_onnx.py b/tests/test_onnx.py new file mode 100644 index 00000000..c09cffc9 --- /dev/null +++ b/tests/test_onnx.py @@ -0,0 +1,8 @@ +import unittest + +import onnx + +class TestOnnx(unittest.TestCase): + def test_load(self): + model = onnx.load("/input/tests/data/mnist-8.onnx") + self.assertIn("CNTKGraph", onnx.helper.printable_graph(model.graph)) diff --git a/tests/test_opencv.py b/tests/test_opencv.py index be0e92f8..817f1597 100644 --- a/tests/test_opencv.py +++ b/tests/test_opencv.py @@ -2,8 +2,20 @@ import cv2 + class TestOpenCV(unittest.TestCase): def test_imread(self): img = cv2.imread('/input/tests/data/dot.png') self.assertEqual(1, img.shape[0]) + + def test_classifier(self): + face_classifier = cv2.CascadeClassifier() + # Pretrained classifier downloaded from here: https://github.com/opencv/opencv/tree/4.x/data/haarcascades + face_classifier.load('/input/tests/data/haarcascade_frontalface_alt.xml') + + img = cv2.imread('/input/tests/data/face.jpg') + frame_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + frame_gray = cv2.equalizeHist(frame_gray) + faces = face_classifier.detectMultiScale(frame_gray) + self.assertEqual(1, len(faces)) diff --git a/tests/test_openpyxl.py b/tests/test_openpyxl.py new file mode 100644 index 00000000..0a5adf7c --- /dev/null +++ b/tests/test_openpyxl.py @@ -0,0 +1,13 @@ +import unittest + +import pandas as pd +from pandas.testing import assert_frame_equal + + +class TestPandasOpenPyXL(unittest.TestCase): + + def test_rw_excel(self): + want = pd.DataFrame([[1, 10, 'a']], columns=['x', 'y', 'z']) + want.to_excel('./want.xlsx', index=False, engine="openpyxl") + got = pd.read_excel('./want.xlsx', engine="openpyxl") + assert_frame_equal(want, got) \ No newline at end of file diff --git a/tests/test_pandarralel.py b/tests/test_pandarralel.py deleted file mode 100644 index fe74b0e1..00000000 --- a/tests/test_pandarralel.py +++ /dev/null @@ -1,11 +0,0 @@ -import unittest - -import pandas as pd -from pandarallel import pandarallel - -pandarallel.initialize() - -class TestPandarallel(unittest.TestCase): - def test_pandarallel(self): - data = pd.read_csv("/input/tests/data/train.csv") - data['label_converted'] = data['label'].parallel_apply(lambda x: x+1) diff --git a/tests/test_pdpbox.py b/tests/test_pdpbox.py deleted file mode 100644 index d820f5b0..00000000 --- a/tests/test_pdpbox.py +++ /dev/null @@ -1,26 +0,0 @@ -import numpy as np -import pandas as pd -from pdpbox import pdp -import unittest -from sklearn.model_selection import train_test_split -from sklearn.tree import DecisionTreeClassifier - -class TestPdpbox(unittest.TestCase): - def test_simple_pdp(self): - # set up data - data = pd.read_csv("/input/tests/data/fifa_2018_stats.csv") - y = (data['Man of the Match'] == "Yes") - feature_names = [i for i in data.columns if data[i].dtype in [np.int64]] - X = data[feature_names] - train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=1) - # Build simple model - tree_model = DecisionTreeClassifier(random_state=0, - max_depth=3).fit(train_X, train_y) - - # Set up pdp as table - pdp_goals = pdp.pdp_isolate(model=tree_model, - dataset=val_X, - model_features=feature_names, - feature='Goal Scored') - # make plot - pdp.pdp_plot(pdp_goals, 'Goal Scored') diff --git a/tests/test_polars.py b/tests/test_polars.py new file mode 100644 index 00000000..8526bd29 --- /dev/null +++ b/tests/test_polars.py @@ -0,0 +1,15 @@ +import unittest + +import polars as pl + +class TestPolars(unittest.TestCase): + def test_read_csv(self): + data = pl.read_csv('/input/tests/data/train.csv') + + self.assertEqual(100, len(data)) + + def test_plot(self): + # This relies on the hvplot package + data = pl.read_csv('/input/tests/data/train.csv') + data.plot.line() + diff --git a/tests/test_prophet.py b/tests/test_prophet.py new file mode 100644 index 00000000..326dd5dc --- /dev/null +++ b/tests/test_prophet.py @@ -0,0 +1,18 @@ +import unittest + +import numpy as np +import pandas as pd + +from prophet import Prophet + +class TestProphet(unittest.TestCase): + def test_fit(self): + train = pd.DataFrame({ + 'ds': np.array(['2012-05-18', '2012-05-19', '2012-05-20', '2012-05-21', '2012-05-22']), + 'y': np.array([38.23, 21.25, 20.18, 20.01, 19.02]) + }) + + forecaster = Prophet(mcmc_samples=1) + forecaster.fit(train, adapt_engaged=False) + + self.assertEqual(len(train) + 1, len(forecaster.make_future_dataframe(periods=1))) diff --git a/tests/test_pydegensac.py b/tests/test_pydegensac.py deleted file mode 100644 index be72b53e..00000000 --- a/tests/test_pydegensac.py +++ /dev/null @@ -1,18 +0,0 @@ -import unittest - -import pydegensac -import numpy as np - - -class TestPydegensac(unittest.TestCase): - def test_find_homography(self): - src_pts = np.float32([ [0,0],[0,1],[1,1],[1,0] ]).reshape(-1,2) - dst_pts = np.float32([ [0,0],[0,-1],[-1,-1],[-1,0] ]).reshape(-1,2) - - H, mask = pydegensac.findHomography(src_pts, dst_pts, 4, 1) - - self.assertEqual(3, len(H)) - self.assertEqual(4, len(mask)) - - - diff --git a/tests/test_pyfasttext.py b/tests/test_pyfasttext.py deleted file mode 100644 index 1ebf5211..00000000 --- a/tests/test_pyfasttext.py +++ /dev/null @@ -1,9 +0,0 @@ -import unittest - -from pyfasttext import FastText - -class TestPyFasttext(unittest.TestCase): - def test_vector(self): - model = FastText() - - model.supervised(input='/input/tests/data/text.txt', output='model', epoch=1, lr=0.7) diff --git a/tests/test_pykalman.py b/tests/test_pykalman.py deleted file mode 100644 index 26d86003..00000000 --- a/tests/test_pykalman.py +++ /dev/null @@ -1,47 +0,0 @@ -import unittest -import numpy as np -from pykalman import KalmanFilter -from pykalman import UnscentedKalmanFilter -from pykalman.sqrt import CholeskyKalmanFilter, AdditiveUnscentedKalmanFilter - -class TestPyKalman(unittest.TestCase): - def test_kalman_filter(self): - kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]]) - measurements = np.asarray([[1,0], [0,0], [0,1]]) # 3 observations - kf = kf.em(measurements, n_iter=5) - (filtered_state_means, filtered_state_covariances) = kf.filter(measurements) - (smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements) - return filtered_state_means - - def test_kalman_missing(self): - kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]]) - measurements = np.asarray([[1,0], [0,0], [0,1]]) # 3 observations - measurements = np.ma.asarray(measurements) - measurements[1] = np.ma.masked - kf = kf.em(measurements, n_iter=5) - (filtered_state_means, filtered_state_covariances) = kf.filter(measurements) - (smoothed_state_means, smoothed_state_covariances) = kf.smooth(measurements) - return filtered_state_means - - def test_unscented_kalman(self): - ukf = UnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, transition_covariance=0.1) - (filtered_state_means, filtered_state_covariances) = ukf.filter([0, 1, 2]) - (smoothed_state_means, smoothed_state_covariances) = ukf.smooth([0, 1, 2]) - return filtered_state_means - - def test_online_update(self): - kf = KalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]]) - measurements = np.asarray([[1,0], [0,0], [0,1]]) # 3 observations - measurements = np.ma.asarray(measurements) - measurements[1] = np.ma.masked # measurement at timestep 1 is unobserved - kf = kf.em(measurements, n_iter=5) - (filtered_state_means, filtered_state_covariances) = kf.filter(measurements) - for t in range(1, 3): - filtered_state_means[t], filtered_state_covariances[t] = \ - kf.filter_update(filtered_state_means[t-1], filtered_state_covariances[t-1], measurements[t]) - return filtered_state_means - - def test_robust_sqrt(self): - kf = CholeskyKalmanFilter(transition_matrices = [[1, 1], [0, 1]], observation_matrices = [[0.1, 0.5], [-0.3, 0.0]]) - ukf = AdditiveUnscentedKalmanFilter(lambda x, w: x + np.sin(w), lambda x, v: x + v, observation_covariance=0.1) - diff --git a/tests/test_pytest.py b/tests/test_pytest.py new file mode 100644 index 00000000..a63e9da4 --- /dev/null +++ b/tests/test_pytest.py @@ -0,0 +1,10 @@ + +import subprocess +import unittest + + +class test_pytest(unittest.TestCase): + def test_pytest_on_pandas_test(self): + cmd = 'pytest /input/tests/test_pandas.py' + result = subprocess.run(cmd, shell=True, capture_output=True) + self.assertEqual(result.returncode, 0) diff --git a/tests/test_python_lsp_server.py b/tests/test_python_lsp_server.py new file mode 100644 index 00000000..b26df4b5 --- /dev/null +++ b/tests/test_python_lsp_server.py @@ -0,0 +1,18 @@ +import subprocess +import unittest + + +class TestPythonLspServer(unittest.TestCase): + def test_initialize(self): + server = subprocess.Popen( + 'python -m pylsp --check-parent-process', + shell=True, + stdin=subprocess.PIPE, + stdout=subprocess.PIPE) + + response = server.communicate(input= + b'Content-Length: 67\r\n' + b'Content-Type: application/vscode-jsonrpc; charset=utf8\r\n' + b'\r\n' + b'{"id": "a", "jsonrpc": "2.0", "method": "initialize", "params": {}}')[0] + assert 'capabilities' in response.decode('utf-8') diff --git a/tests/test_pytorch.py b/tests/test_pytorch.py index 61dd71ff..befa6f7e 100644 --- a/tests/test_pytorch.py +++ b/tests/test_pytorch.py @@ -15,6 +15,14 @@ def test_nn(self): data_torch = autograd.Variable(torch.randn(2, 5)) linear_torch(data_torch) + @gpu_test + def test_linalg(self): + A = torch.randn(3, 3).t().to('cuda') + B = torch.randn(3).t().to('cuda') + + result = torch.linalg.solve(A, B) + self.assertEqual(3, result.shape[0]) + @gpu_test def test_gpu_computation(self): cuda = torch.device('cuda') diff --git a/tests/test_pytorch_lightning.py b/tests/test_pytorch_lightning.py index 24d9f16d..24992e5f 100644 --- a/tests/test_pytorch_lightning.py +++ b/tests/test_pytorch_lightning.py @@ -62,7 +62,7 @@ def test_version(self): def test_mnist(self): dm = LitDataModule() model = LitClassifier() - trainer = pl.Trainer(gpus=None, max_epochs=1) + trainer = pl.Trainer(max_epochs=1, log_every_n_steps=1, devices=1) trainer.fit(model, datamodule=dm) self.assertIn("train_loss", trainer.logged_metrics) diff --git a/tests/test_qgrid.py b/tests/test_qgrid.py deleted file mode 100644 index e97ef2a1..00000000 --- a/tests/test_qgrid.py +++ /dev/null @@ -1,16 +0,0 @@ -import unittest - -import numpy as np -import pandas as pd - -from qgrid import QgridWidget - - -class TestQgrid(unittest.TestCase): - def test_nans(self): - df = pd.DataFrame([(pd.Timestamp('2017-02-02'), np.nan), - (4, 2), - ('foo', 'bar')]) - view = QgridWidget(df=df) - - self.assertIsNotNone(view.get_changed_df()) diff --git a/tests/test_rapids.py b/tests/test_rapids.py deleted file mode 100644 index 3b7239dd..00000000 --- a/tests/test_rapids.py +++ /dev/null @@ -1,22 +0,0 @@ -import unittest - -from common import gpu_test - - -class TestRapids(unittest.TestCase): - @gpu_test - def test_dbscan(self): - import cudf - from cuml.cluster import DBSCAN - - # Create and populate a GPU DataFrame - gdf_float = cudf.DataFrame() - gdf_float['0'] = [1.0, 2.0, 5.0] - gdf_float['1'] = [4.0, 2.0, 1.0] - gdf_float['2'] = [4.0, 2.0, 1.0] - - # Setup and fit clusters - dbscan_float = DBSCAN(eps=1.0, min_samples=1) - dbscan_float.fit(gdf_float) - - self.assertEqual(3, dbscan_float.labels_.size) diff --git a/tests/test_seaborn.py b/tests/test_seaborn.py index b20cbe27..5d9a76c1 100644 --- a/tests/test_seaborn.py +++ b/tests/test_seaborn.py @@ -7,7 +7,7 @@ class TestSeaborn(unittest.TestCase): # Fails if seaborn gets downgraded by other package installations. def test_version(self): - self.assertGreaterEqual(StrictVersion(sns.__version__), StrictVersion("0.9.0")) + self.assertGreaterEqual(StrictVersion(sns.__version__), StrictVersion("0.12.0")) def test_option(self): diff --git a/tests/test_sklearn.py b/tests/test_sklearn.py index d2d4b027..c1237ff5 100644 --- a/tests/test_sklearn.py +++ b/tests/test_sklearn.py @@ -12,7 +12,7 @@ def test_random_forest_classifier(self): rf1.fit(X,y) def test_linearn_classifier(self): - boston = datasets.load_boston() - X, y = boston.data, boston.target + iris = datasets.load_iris() + X, y = iris.data, iris.target lr1 = LinearRegression() lr1.fit(X,y) diff --git a/tests/test_sklearnex.py b/tests/test_sklearnex.py new file mode 100644 index 00000000..5809570a --- /dev/null +++ b/tests/test_sklearnex.py @@ -0,0 +1,12 @@ +import unittest + +import numpy as np + +class TestSklearnex(unittest.TestCase): + def test_dbscan(self): + from sklearnex.cluster import DBSCAN + X = np.array([[1., 2.], [2., 2.], [2., 3.], + [8., 7.], [8., 8.], [25., 80.]], dtype=np.float32) + + clustering = DBSCAN(eps=3, min_samples=2).fit(X) + np.testing.assert_array_equal(np.array([0, 0, 0, 1, 1, -1]), clustering.labels_) \ No newline at end of file diff --git a/tests/test_tensorflow_addons.py b/tests/test_tensorflow_addons.py deleted file mode 100644 index 21466d9d..00000000 --- a/tests/test_tensorflow_addons.py +++ /dev/null @@ -1,22 +0,0 @@ -import unittest - -import tensorflow as tf -import tensorflow_addons as tfa - - -class TestTensorflowAddons(unittest.TestCase): - def test_tfa_image(self): - img_raw = tf.io.read_file('/input/tests/data/dot.png') - img = tf.io.decode_image(img_raw) - img = tf.image.convert_image_dtype(img, tf.float32) - mean = tfa.image.mean_filter2d(img, filter_shape=1) - - self.assertEqual([1, 1, 3], mean.shape) - - # This test exercises TFA Custom Op. See: b/145555176 - def test_gelu(self): - x = tf.constant([[0.5, 1.2, -0.3]]) - layer = tfa.layers.GELU() - result = layer(x) - - self.assertEqual((1, 3), result.shape) \ No newline at end of file diff --git a/tests/test_tensorflow_cloud.py b/tests/test_tensorflow_cloud.py deleted file mode 100644 index 2875e121..00000000 --- a/tests/test_tensorflow_cloud.py +++ /dev/null @@ -1,8 +0,0 @@ -import unittest - -import tensorflow_cloud as tfc - - -class TestTensorflowCloud(unittest.TestCase): - def test_remote(self): - self.assertFalse(tfc.remote()) diff --git a/tests/test_tensorflow_credentials.py b/tests/test_tensorflow_credentials.py deleted file mode 100644 index 628b35c1..00000000 --- a/tests/test_tensorflow_credentials.py +++ /dev/null @@ -1,37 +0,0 @@ -import unittest - -import os -import tensorflow_gcs_config -from unittest.mock import patch -from test.support import EnvironmentVarGuard -from kaggle_secrets import UserSecretsClient - -class TestTensorflowCredentials(unittest.TestCase): - - @patch('tensorflow_gcs_config.configure_gcs') - def test_set_tensorflow_credential(self, mock_configure_gcs): - credential = '{"client_id":"fake_client_id",' \ - '"client_secret":"fake_client_secret",' \ - '"refresh_token":"not a refresh token",' \ - '"type":"authorized_user"}' - - env = EnvironmentVarGuard() - env.set('HOME', '/tmp') - env.set('GOOGLE_APPLICATION_CREDENTIALS', '') - - # These need to be set to make UserSecretsClient happy, but aren't - # pertinent to this test. - env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') - env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') - - user_secrets = UserSecretsClient() - user_secrets.set_tensorflow_credential(credential) - - credential_path = '/tmp/gcloud_credential.json' - self.assertEqual( - credential_path, os.environ['GOOGLE_APPLICATION_CREDENTIALS']) - with open(credential_path, 'r') as f: - saved_cred = f.read() - self.assertEqual(credential, saved_cred) - - mock_configure_gcs.assert_called_with(credentials=credential) diff --git a/tests/test_tensorflow_decision_forests.py b/tests/test_tensorflow_decision_forests.py new file mode 100644 index 00000000..aeeb2e7d --- /dev/null +++ b/tests/test_tensorflow_decision_forests.py @@ -0,0 +1,18 @@ +import unittest + +import numpy as np +import pandas as pd +import tensorflow_decision_forests as tfdf + +class TestTensorflowDecisionForest(unittest.TestCase): + def test_fit(self): + train_df = pd.read_csv("/input/tests/data/train.csv") + + # Convert the dataset into a TensorFlow dataset. + train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="label") + + # Train the model + model = tfdf.keras.RandomForestModel(num_trees=1) + model.fit(train_ds) + + self.assertEqual(1, model.count_params()) \ No newline at end of file diff --git a/tests/test_tensorflow_text.py b/tests/test_tensorflow_text.py new file mode 100644 index 00000000..5e801688 --- /dev/null +++ b/tests/test_tensorflow_text.py @@ -0,0 +1,12 @@ +import unittest + +import tensorflow_text as tf_text + + +class TestTensorflowText(unittest.TestCase): + def test_tokenizer(self): + word_tokenizer = tf_text.WhitespaceTokenizer() + + tokens = word_tokenizer.tokenize("I love Kaggle!") + + self.assertEqual((3,), tokens.shape) diff --git a/tests/test_theano.py b/tests/test_theano.py deleted file mode 100644 index 156beb7b..00000000 --- a/tests/test_theano.py +++ /dev/null @@ -1,20 +0,0 @@ -import unittest - -import theano -from theano import tensor - -class TestTheano(unittest.TestCase): - def test_addition(self): - # Declare two symbolic floating-point scalars. - a = tensor.dscalar() - b = tensor.dscalar() - - # Create a simple expression. - c = a + b - - # Convert the expression into a callable object that takes (a,b) - # values as input and computes a value for 'c'. - f = theano.function([a,b], c) - - # Bind 1.5 to 'a', 2.5 to 'b', and evaluate 'c'. - self.assertEqual(4.0, f(1.5, 2.5)) diff --git a/tests/test_timm.py b/tests/test_timm.py new file mode 100644 index 00000000..5dac0ec6 --- /dev/null +++ b/tests/test_timm.py @@ -0,0 +1,9 @@ +import unittest + +import timm + + +class TestTimm(unittest.TestCase): + def test_list_models(self): + models = timm.list_models() + self.assertGreater(len(models), 0) diff --git a/tests/test_torchinfo.py b/tests/test_torchinfo.py new file mode 100644 index 00000000..7636d450 --- /dev/null +++ b/tests/test_torchinfo.py @@ -0,0 +1,11 @@ +import unittest + +import torch.nn as tnn + +from torchinfo import summary + +class TestTorchinfo(unittest.TestCase): + def test_info(self): + model = tnn.Linear(5,3) + s = summary(model) + self.assertEqual(1, len(s.summary_list)) diff --git a/tests/test_torchtext.py b/tests/test_torchtext.py deleted file mode 100644 index f9fbf76f..00000000 --- a/tests/test_torchtext.py +++ /dev/null @@ -1,12 +0,0 @@ -import unittest - -from torchtext.data.metrics import bleu_score - - -class TestTorchtext(unittest.TestCase): - def test_bleu_score(self): - candidate = [['I', 'love', 'Kaggle', 'Notebooks']] - refs = [[['Completely', 'Different']]] - - self.assertEqual(0, bleu_score(candidate, refs)) - diff --git a/tests/test_torchtune.py b/tests/test_torchtune.py new file mode 100644 index 00000000..c4a702fd --- /dev/null +++ b/tests/test_torchtune.py @@ -0,0 +1,16 @@ +import unittest +import subprocess + +class TestTorchtune(unittest.TestCase): + def test_help(self): + result = subprocess.run( + ["tune", "--help"], + capture_output=True, + text=True + ) + + self.assertEqual(0, result.returncode) + self.assertIn( + "Download a model from the Hugging Face Hub or Kaggle", + result.stdout + ) diff --git a/tests/test_transformers.py b/tests/test_transformers.py index d6056293..910eab30 100644 --- a/tests/test_transformers.py +++ b/tests/test_transformers.py @@ -1,7 +1,8 @@ import unittest import torch -from transformers import AdamW +import torch.optim as optim +import transformers.pipelines # verify this import works class TestTransformers(unittest.TestCase): @@ -9,13 +10,12 @@ def assertListAlmostEqual(self, list1, list2, tol): self.assertEqual(len(list1), len(list2)) for a, b in zip(list1, list2): self.assertAlmostEqual(a, b, delta=tol) - def test_adam_w(self): w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True) target = torch.tensor([0.4, 0.2, -0.5]) criterion = torch.nn.MSELoss() # No warmup, constant schedule, no gradient clipping - optimizer = AdamW(params=[w], lr=2e-1, weight_decay=0.0) + optimizer = optim.AdamW(params=[w], lr=2e-1, weight_decay=0.0) for _ in range(100): loss = criterion(w, target) loss.backward() diff --git a/tests/test_translation.py b/tests/test_translation.py index a55132c9..52de2a08 100644 --- a/tests/test_translation.py +++ b/tests/test_translation.py @@ -4,9 +4,9 @@ from unittest.mock import Mock, patch from kaggle_gcp import KaggleKernelCredentials, KaggleKernelWithProjetCredentials, init_translation_v2, init_translation_v3 -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from google.api_core import client_options -from google.cloud import translate, translate_v2 +from google.cloud import translate_v3 as translate, translate_v2 def _make_credentials(): import google.auth.credentials @@ -48,7 +48,7 @@ def test_user_provided_credentials_v2(self): self.assertIsNotNone(client.credentials) self.assertNotIsInstance(client.credentials, KaggleKernelCredentials) - @patch("google.cloud.translate.TranslationServiceClient", new=FakeClient) + @patch("google.cloud.translate_v3.TranslationServiceClient", new=FakeClient) def test_default_credentials_v3(self): env = EnvironmentVarGuard() env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') @@ -60,7 +60,7 @@ def test_default_credentials_v3(self): self.assertIsInstance(client.credentials, KaggleKernelCredentials) - @patch("google.cloud.translate.TranslationServiceClient", new=FakeClient) + @patch("google.cloud.translate_v3.TranslationServiceClient", new=FakeClient) def test_user_provided_credentials_v3(self): credentials = _make_credentials() env = EnvironmentVarGuard() @@ -107,13 +107,12 @@ def test_monkeypatching_idempotent(self): self.assertEqual(client2_1, client2_2) self.assertEqual(client3_1, client3_2) - @patch("google.cloud.translate.TranslationServiceClient", new=FakeClient) + @patch("google.cloud.translate_v3.TranslationServiceClient", new=FakeClient) def test_client_credential_uniqueness_v3(self): """ Client instance must use unique KaggleKernelWithProjetCredentials with quota_project_id when client_options.quota_project_id provided. (even if quota_project_id is same) """ - credentials = _make_credentials() env = EnvironmentVarGuard() env.set('KAGGLE_USER_SECRETS_TOKEN', 'foobar') env.set('KAGGLE_KERNEL_INTEGRATIONS', 'CLOUDAI') diff --git a/tests/test_tsfresh.py b/tests/test_tsfresh.py index 8a1dbd55..b9d6a92f 100644 --- a/tests/test_tsfresh.py +++ b/tests/test_tsfresh.py @@ -12,7 +12,7 @@ def test_extract_feature(self): 'time': np.array([0,1,2,0,1,2]), 'x': np.array([3,4,5,7,8,10]) }) - extracted_features = extract_features(ts, column_id='id', column_sort='time') + extracted_features = extract_features(ts, column_id='id', column_sort='time', n_jobs=1) self.assertEqual(2, len(extracted_features)) diff --git a/tests/test_ucaip.py b/tests/test_ucaip.py index 2919bdba..3dfb117b 100644 --- a/tests/test_ucaip.py +++ b/tests/test_ucaip.py @@ -3,7 +3,7 @@ from unittest.mock import Mock from kaggle_gcp import KaggleKernelCredentials, init_ucaip -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard def _make_credentials(): import google.auth.credentials diff --git a/tests/test_user_secrets.py b/tests/test_user_secrets.py index 606a5c8c..c11432fe 100644 --- a/tests/test_user_secrets.py +++ b/tests/test_user_secrets.py @@ -4,10 +4,10 @@ import threading import unittest from http.server import BaseHTTPRequestHandler, HTTPServer -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from urllib.parse import urlparse from datetime import datetime, timedelta -import mock +from unittest.mock import Mock, patch from google.auth.exceptions import DefaultCredentialsError from google.cloud import bigquery @@ -40,7 +40,7 @@ def do_POST(s): class TestUserSecrets(unittest.TestCase): - SERVER_ADDRESS = urlparse(os.getenv(_KAGGLE_URL_BASE_ENV_VAR_NAME, default="http://127.0.0.1:8001")) + SERVER_ADDRESS = urlparse(os.getenv(_KAGGLE_URL_BASE_ENV_VAR_NAME, default="http://127.0.0.1:0")) def _test_client(self, client_func, expected_path, expected_body, secret=None, success=True): _request = {} @@ -66,6 +66,8 @@ def get_response(self): threading.Thread(target=httpd.serve_forever).start() try: + # Make sure this server is pointed to by clients (the port may have been dynamically chosen): + env.set(_KAGGLE_URL_BASE_ENV_VAR_NAME, "http://"+httpd.server_address[0]+":"+str(httpd.server_address[1])) client_func() finally: httpd.shutdown() @@ -164,11 +166,27 @@ def test_fn(): self._test_client(test_fn, '/requests/GetUserSecretByLabelRequest', {'Label': "__gcloud_sdk_auth__"}, secret=secret) - @mock.patch('kaggle_secrets.datetime') + def test_set_tensorflow_credential(self): + secret = '{"client_id":"gcloud","type":"authorized_user","refresh_token":"refresh_token"}' + + def test_fn(): + client = UserSecretsClient() + creds = client.get_gcloud_credential() + client.set_tensorflow_credential(creds) + + expected_creds_file = '/tmp/gcloud_credential.json' + self.assertEqual(expected_creds_file, os.environ['GOOGLE_APPLICATION_CREDENTIALS']) + + with open(expected_creds_file, 'r') as f: + self.assertEqual(secret, '\n'.join(f.readlines())) + + self._test_client(test_fn, '/requests/GetUserSecretByLabelRequest', {'Label': "__gcloud_sdk_auth__"}, secret=secret) + + @patch('kaggle_secrets.datetime') def test_get_access_token_succeeds(self, mock_dt): secret = '12345' now = datetime(1993, 4, 24) - mock_dt.utcnow = mock.Mock(return_value=now) + mock_dt.utcnow = Mock(return_value=now) def call_get_bigquery_access_token(): client = UserSecretsClient() @@ -182,22 +200,6 @@ def call_get_cloudai_access_token(): client = UserSecretsClient() secret_response = client._get_cloudai_access_token() self.assertEqual(secret_response, (secret, now + timedelta(seconds=3600))) - def call_get_translation_access_token(): - client = UserSecretsClient() - secret_response = client._get_translation_access_token() - self.assertEqual(secret_response, (secret, now + timedelta(seconds=3600))) - def call_get_natural_lang_access_token(): - client = UserSecretsClient() - secret_response = client._get_natural_language_access_token() - self.assertEqual(secret_response, (secret, now + timedelta(seconds=3600))) - def call_get_video_intell_access_token(): - client = UserSecretsClient() - secret_response = client._get_video_intelligence_access_token() - self.assertEqual(secret_response, (secret, now + timedelta(seconds=3600))) - def call_get_vision_access_token(): - client = UserSecretsClient() - secret_response = client._get_vision_access_token() - self.assertEqual(secret_response, (secret, now + timedelta(seconds=3600))) self._test_client(call_get_bigquery_access_token, '/requests/GetUserSecretRequest', {'Target': GcpTarget.BIGQUERY.target}, diff --git a/tests/test_user_session.py b/tests/test_user_session.py index 57f2de8c..373fcfc0 100644 --- a/tests/test_user_session.py +++ b/tests/test_user_session.py @@ -4,7 +4,7 @@ import unittest from http.server import BaseHTTPRequestHandler, HTTPServer -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from urllib.parse import urlparse from kaggle_session import UserSessionClient from kaggle_web_client import (_KAGGLE_URL_BASE_ENV_VAR_NAME, @@ -30,7 +30,7 @@ def do_POST(s): s.wfile.write(json.dumps(s.get_response()).encode("utf-8")) class TestUserSessionClient(unittest.TestCase): - SERVER_ADDRESS = urlparse(os.getenv(_KAGGLE_URL_BASE_ENV_VAR_NAME, default="http://127.0.0.1:8001")) + SERVER_ADDRESS = urlparse(os.getenv(_KAGGLE_URL_BASE_ENV_VAR_NAME, default="http://127.0.0.1:0")) TEST_JWT = 'test-secrets-key' def _test_client(self, client_func, expected_path, expected_body, source=None, success=True): @@ -55,6 +55,8 @@ def get_response(self): threading.Thread(target=httpd.serve_forever).start() try: + # Make sure this server is pointed to by clients (the port may have been dynamically chosen): + env.set(_KAGGLE_URL_BASE_ENV_VAR_NAME, "http://"+httpd.server_address[0]+":"+str(httpd.server_address[1])) client_func() finally: httpd.shutdown() diff --git a/tests/test_vaex.py b/tests/test_vaex.py deleted file mode 100644 index b64061b0..00000000 --- a/tests/test_vaex.py +++ /dev/null @@ -1,10 +0,0 @@ -import unittest - -import vaex - -class TestVaex(unittest.TestCase): - def test_read_csv(self): - df = vaex.read_csv("/input/tests/data/train.csv") - - self.assertEqual((100, 785), df.shape) - self.assertEqual(10, df['label'].nunique()) \ No newline at end of file diff --git a/tests/test_video_intelligence.py b/tests/test_video_intelligence.py index 15267c85..564d947e 100644 --- a/tests/test_video_intelligence.py +++ b/tests/test_video_intelligence.py @@ -4,7 +4,7 @@ from unittest.mock import Mock, patch from kaggle_gcp import KaggleKernelCredentials, init_video_intelligence -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from google.cloud import videointelligence def _make_credentials(): diff --git a/tests/test_vision.py b/tests/test_vision.py index 5fc112bf..d9d2d160 100644 --- a/tests/test_vision.py +++ b/tests/test_vision.py @@ -4,7 +4,7 @@ from unittest.mock import Mock, patch from kaggle_gcp import KaggleKernelCredentials, init_vision -from test.support import EnvironmentVarGuard +from test.support.os_helper import EnvironmentVarGuard from google.cloud import vision def _make_credentials(): diff --git a/tests/test_vowpalwabbit.py b/tests/test_vowpalwabbit.py deleted file mode 100644 index 839aed05..00000000 --- a/tests/test_vowpalwabbit.py +++ /dev/null @@ -1,10 +0,0 @@ -import unittest - -from vowpalwabbit import pyvw - -class TestVowpalwabbit(unittest.TestCase): - def test_basic(self): - vw = pyvw.vw(quiet=True) - ex = vw.example('1 | a b c') - vw.learn(ex) - self.assertGreater(vw.predict(ex), 0) diff --git a/tests/test_wordbatch.py b/tests/test_wordbatch.py deleted file mode 100644 index 906ca130..00000000 --- a/tests/test_wordbatch.py +++ /dev/null @@ -1,14 +0,0 @@ -import unittest - -from wordbatch.extractors import WordBag -from wordbatch.pipelines import WordBatch - -class TestWordBatch(unittest.TestCase): - def test_wordbatch(self): - WordBatch(extractor=(WordBag, { - "hash_ngrams":2, - "hash_ngrams_weights":[0.5, -1.0], - "hash_size":2**23, - "norm":'l2', - "tf":'log', - "idf":50.0})) diff --git a/tests/test_xgboost.py b/tests/test_xgboost.py index 6b6c6ec1..68166813 100644 --- a/tests/test_xgboost.py +++ b/tests/test_xgboost.py @@ -17,10 +17,9 @@ def test_classifier(self): X_test = np.random.random((100, 28)) y_test = np.random.randint(10, size=(100, 1)) - xgb1 = XGBClassifier(n_estimators=3, use_label_encoder=False) + xgb1 = XGBClassifier(n_estimators=3, use_label_encoder=False, eval_metric='mlogloss') xgb1.fit( X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], - eval_metric='mlogloss', ) self.assertIn("validation_0", xgb1.evals_result()) diff --git a/packages/README.md b/tests/utils/__init__.py similarity index 100% rename from packages/README.md rename to tests/utils/__init__.py diff --git a/tests/utils/kagglehub.py b/tests/utils/kagglehub.py new file mode 100644 index 00000000..7a2a8995 --- /dev/null +++ b/tests/utils/kagglehub.py @@ -0,0 +1,64 @@ +import json +import os +import threading +import re + +from contextlib import contextmanager +from urllib.parse import urlparse +from test.support.os_helper import EnvironmentVarGuard +from http.server import BaseHTTPRequestHandler, HTTPServer + +from kagglesdk.kaggle_env import get_endpoint, get_env + +class KaggleAPIHandler(BaseHTTPRequestHandler): + """ + Fake Kaggle API server supporting the download endpoint. + Serving files under /input/tests/data/kagglehub. + """ + def do_HEAD(self): + self.send_response(200) + + def do_POST(self): + content_length = int(self.headers.get('Content-Length', 0)) + body_bytes = self.rfile.read(content_length) + request_body = json.loads(body_bytes.decode('utf-8')) + + if self.path != "/api/v1/models.ModelApiService/DownloadModelInstanceVersion": + self.send_response(404) + self.wfile.write(bytes(f"Unhandled path: {self.path}", "utf-8")) + return + + model_handle = f"{request_body["ownerSlug"]}/{request_body["modelSlug"]}/keras/{request_body["instanceSlug"]}/{request_body["versionNumber"]}" + path = request_body["path"] + filepath = f"/input/tests/data/kagglehub/models/{model_handle}/{path}" + if not os.path.isfile(filepath): + self.send_error(404, "Internet is disabled in our tests " + "kagglehub uses a fake API server. " + f"Use `kagglehub.model_download('{model_handle}', path='{path}')` to download the missing file " + f"and copy it to `./docker-python/tests/data/kagglehub/models/{model_handle}/{path}`.") + return + + with open(filepath, "rb") as f: + self.send_response(200) + self.send_header("Content-type", "application/octet-stream") + self.send_header("Content-Length", os.path.getsize(filepath)) + self.end_headers() + self.wfile.write(f.read()) + +@contextmanager +def create_test_kagglehub_server(): + env = EnvironmentVarGuard() + env.set('KAGGLE_API_ENVIRONMENT', 'TEST') + with env: + endpoint = get_endpoint(get_env()) + test_server_address = urlparse(endpoint) + + with HTTPServer((test_server_address.hostname, test_server_address.port), KaggleAPIHandler) as httpd: + threading.Thread(target=httpd.serve_forever).start() + + try: + yield httpd + finally: + httpd.shutdown() + + diff --git a/tpu/Dockerfile b/tpu/Dockerfile new file mode 100644 index 00000000..343443ae --- /dev/null +++ b/tpu/Dockerfile @@ -0,0 +1,91 @@ +ARG BASE_IMAGE + +FROM $BASE_IMAGE + +# We need to define the ARG here to get the ARG below the FROM statement to access it within this build context +# See: https://docs.docker.com/engine/reference/builder/#understand-how-arg-and-from-interact +ARG PYTHON_WHEEL_VERSION +ARG PYTHON_VERSION_PATH +ARG TENSORFLOW_VERSION +ARG TORCH_LINUX_WHEEL_VERSION +ARG TORCH_VERSION +ARG TORCHVISION_VERSION +ARG TORCHAUDIO_VERSION + +ENV ISTPUVM=1 + +ADD tpu/clean-layer.sh /tmp/clean-layer.sh +ADD patches/nbconvert-extensions.tpl /opt/kaggle/nbconvert-extensions.tpl +ADD patches/template_conf.json /opt/kaggle/conf.json + +# Add BigQuery client proxy settings, kaggle secrets etc. +ENV PYTHONUSERBASE "/root/.local" +ADD patches/kaggle_secrets.py /root/.local/lib/${PYTHON_VERSION_PATH}/site-packages/kaggle_secrets.py +ADD patches/kaggle_session.py /root/.local/lib/${PYTHON_VERSION_PATH}/site-packages/kaggle_session.py +ADD patches/kaggle_web_client.py /root/.local/lib/${PYTHON_VERSION_PATH}/site-packages/kaggle_web_client.py +ADD patches/kaggle_datasets.py /root/.local/lib/${PYTHON_VERSION_PATH}/site-packages/kaggle_datasets.py + +# Prereqs +# This is needed for cv2 (opencv-python): +# https://stackoverflow.com/questions/55313610/importerror-libgl-so-1-cannot-open-shared-object-file-no-such-file-or-directo +RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y + +# Install all the packages together for maximum compatibility. +# Additional useful packages should be added in the requirements.txt +# Bring in the requirements.txt and replace variables in it: +RUN apt-get install -y gettext +ADD tpu/requirements.in /kaggle_requirements.in +RUN envsubst < /kaggle_requirements.in > /requirements.in + +# Install uv and then install the requirements: +RUN curl -LsSf https://astral.sh/uv/install.sh | sh +RUN export PATH="${HOME}/.local/bin:${PATH}" && \ + uv pip compile --system --prerelease=allow \ + --verbose \ + --upgrade \ + --find-links=https://storage.googleapis.com/jax-releases/libtpu_releases.html \ + --find-links=https://storage.googleapis.com/libtpu-releases/index.html \ + --find-links=https://storage.googleapis.com/libtpu-wheels/index.html \ + --find-links=https://download.pytorch.org/whl/torch_stable.html \ + --emit-find-links \ + --no-emit-package pip \ + --no-emit-package setuptools \ + --output-file /requirements.txt \ + /requirements.in && \ + uv pip install --system --prerelease=allow --force-reinstall \ + -r /requirements.txt && \ + uv cache clean && \ + /tmp/clean-layer.sh +ENV PATH="~/.local/bin:${PATH}" + +# We install a libtpu version compatible with both jax 0.7.2 and torch 2.8.0. +# Why? tunix latest -> flax 0.12 -> jax 0.7.2 -> libtpu 0.0.23. However, that +# libtpu causes pjrt api errors for torch 2.8.0. screenshot/5heUtdyaJ4MmR3D +# https://github.com/pytorch/xla/blob/d517649bdef6ab0519c30c704bde8779c8216502/setup.py#L111 +# https://github.com/jax-ml/jax/blob/3489529b38d1f11d1e5caf4540775aadd5f2cdda/setup.py#L26 +RUN export PATH="${HOME}/.local/bin:${PATH}" && \ + uv pip install --system --force-reinstall libtpu==0.0.17 && \ + uv cache clean && \ + /tmp/clean-layer.sh + +# Kaggle Model Hub patches: +ADD patches/kaggle_module_resolver.py /usr/local/lib/${PYTHON_VERSION_PATH}/site-packages/tensorflow_hub/kaggle_module_resolver.py +RUN sed -i '/from tensorflow_hub import uncompressed_module_resolver/a from tensorflow_hub import kaggle_module_resolver' /usr/local/lib/${PYTHON_VERSION_PATH}/site-packages/tensorflow_hub/config.py +RUN sed -i '/_install_default_resolvers()/a \ \ registry.resolver.add_implementation(kaggle_module_resolver.KaggleFileResolver())' /usr/local/lib/${PYTHON_VERSION_PATH}/site-packages/tensorflow_hub/config.py + +# Set these env vars so that they don't produce errs calling the metadata server to load them: +ENV TPU_PROCESS_ADDRESSES=local + +# Metadata +ARG GIT_COMMIT=unknown +ARG BUILD_DATE=unknown + +LABEL git-commit=$GIT_COMMIT +LABEL build-date=$BUILD_DATE +ENV GIT_COMMIT=${GIT_COMMIT} +ENV BUILD_DATE=${BUILD_DATE} + +LABEL kaggle-lang=python + +# Correlate current release with the git hash inside the kernel editor by running `!cat /etc/git_commit`. +RUN echo "$GIT_COMMIT" > /etc/git_commit && echo "$BUILD_DATE" > /etc/build_date diff --git a/tpu/build b/tpu/build new file mode 100755 index 00000000..00524080 --- /dev/null +++ b/tpu/build @@ -0,0 +1,57 @@ +#!/bin/bash +set -e + +usage() { +cat << EOF +Usage: $0 [OPTIONS] +Build a new Python TPU 1VM Docker image. + +Options: + -c, --use-cache Use layer cache when building a new image. +EOF +} + +CACHE_FLAG='--no-cache' +DOCKERFILE='Dockerfile' +IMAGE_TAG='kaggle/python-tpuvm-build' +BUILD_ARGS='' + +while :; do + case "$1" in + -h|--help) + usage + exit + ;; + -c|--use-cache) + CACHE_FLAG='' + ;; + -?*) + usage + printf 'ERROR: Unknown option: %s\n' "$1" >&2 + exit + ;; + *) + break + esac + + shift +done + +BUILD_ARGS+=" --build-arg GIT_COMMIT=$(git rev-parse HEAD)" +BUILD_ARGS+=" --build-arg BUILD_DATE=$(date '+%Y%m%d-%H%M%S')" + +# Read build args from config.txt file. +SRCDIR=$(dirname "${BASH_SOURCE[0]}") +for l in `cat ${SRCDIR}/config.txt`; do + BUILD_ARGS+=" --build-arg $l" +done + +readonly CACHE_FLAG +readonly DOCKERFILE +readonly IMAGE_TAG +readonly BUILD_ARGS + +DOCKERFILE_PATH="$SRCDIR/$DOCKERFILE" + +set -x +docker build --rm --pull $CACHE_FLAG -t "$IMAGE_TAG" -f "$DOCKERFILE_PATH" $BUILD_ARGS . diff --git a/tpu/clean-layer.sh b/tpu/clean-layer.sh new file mode 100755 index 00000000..467e1cac --- /dev/null +++ b/tpu/clean-layer.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# +# This scripts should be called at the end of each RUN command +# in the Dockerfiles. +# +# Each RUN command creates a new layer that is stored separately. +# At the end of each command, we should ensure we clean up downloaded +# archives and source files used to produce binary to reduce the size +# of the layer. +set -e +set -x + +# Delete files that pip caches when installing a package. +rm -rf /root/.cache/pip/* +# Delete old downloaded archive files +apt-get autoremove -y +# Delete downloaded archive files +apt-get clean +# Ensures the current working directory won't be deleted +cd /usr/local/src/ +# Delete source files used for building binaries +rm -rf /usr/local/src/* \ No newline at end of file diff --git a/tpu/config.txt b/tpu/config.txt new file mode 100644 index 00000000..ab933ba7 --- /dev/null +++ b/tpu/config.txt @@ -0,0 +1,12 @@ +BASE_IMAGE=python:3.12 +PYTHON_WHEEL_VERSION=cp312 +PYTHON_VERSION_PATH=python3.12 +TENSORFLOW_VERSION=2.20.0 +# gsutil ls gs://pytorch-xla-releases/wheels/tpuvm/* | grep libtpu | grep torch_xla | grep -v -E ".*rc[0-9].*" | sed 's/.*torch_xla-\(.*\)+libtpu.*/\1/' | sort -rV +# Supports nightly +TORCH_VERSION=2.8.0 +# https://github.com/pytorch/audio supports nightly +TORCHAUDIO_VERSION=2.8.0 +# https://github.com/pytorch/vision supports nightly +TORCHVISION_VERSION=0.23.0 +TORCH_LINUX_WHEEL_VERSION=manylinux_2_28_x86_64 diff --git a/tpu/requirements.in b/tpu/requirements.in new file mode 100644 index 00000000..1fceeebb --- /dev/null +++ b/tpu/requirements.in @@ -0,0 +1,54 @@ +# TPU Utils +tpu-info +# Tensorflow packages +# TODO: b/447621961 - re-enable tensorflow-tpu when a compatible libtpu can be found. +tensorflow-cpu==${TENSORFLOW_VERSION} +tensorflow_hub +tensorflow-io +tensorflow-probability +tensorflow_datasets +# Torch packages +https://download.pytorch.org/whl/cpu/torch-${TORCH_VERSION}%2Bcpu-${PYTHON_WHEEL_VERSION}-${PYTHON_WHEEL_VERSION}-${TORCH_LINUX_WHEEL_VERSION}.whl +https://download.pytorch.org/whl/cpu/torchaudio-${TORCHAUDIO_VERSION}%2Bcpu-${PYTHON_WHEEL_VERSION}-${PYTHON_WHEEL_VERSION}-${TORCH_LINUX_WHEEL_VERSION}.whl +https://download.pytorch.org/whl/cpu/torchvision-${TORCHVISION_VERSION}%2Bcpu-${PYTHON_WHEEL_VERSION}-${PYTHON_WHEEL_VERSION}-${TORCH_LINUX_WHEEL_VERSION}.whl +https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-${TORCH_VERSION}-${PYTHON_WHEEL_VERSION}-${PYTHON_WHEEL_VERSION}-${TORCH_LINUX_WHEEL_VERSION}.whl +# Jax packages +jax[tpu] +distrax +flax +git+https://github.com/deepmind/dm-haiku +jraph +optax +trax +# Tunix GRPO +git+https://github.com/google/tunix +git+https://github.com/google/qwix +grain +# Jupyter packages +jupyter-lsp==1.5.1 +jupyterlab +notebook +papermill +python-lsp-server[all] +# Keras Packages +keras>3 +keras-cv +keras-nlp +# Kaggle Packages +kagglehub +# Other useful packages, add more here +accelerate +albumentations +diffusers +einops +fastparquet +ipywidgets +matplotlib +opencv-python +opencv-python-headless +pandas +pyarrow +scikit-learn +seaborn +timm +transformers