ARG BASE_IMAGE_REPO \

BASE_IMAGE_TAG \

CPU_BASE_IMAGE_NAME \

GPU_BASE_IMAGE_NAME \

LIGHTGBM_VERSION \

TORCH_VERSION \

TORCHAUDIO_VERSION \

TORCHVISION_VERSION \

JAX_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 gcr.io/kaggle-images/python-jaxlib-whl:${GPU_BASE_IMAGE_NAME}-${BASE_IMAGE_TAG}-${JAX_VERSION} AS jaxlib_whl

FROM ${BASE_IMAGE_REPO}/${GPU_BASE_IMAGE_NAME}:${BASE_IMAGE_TAG}

{{ else }}

FROM ${BASE_IMAGE_REPO}/${CPU_BASE_IMAGE_NAME}:${BASE_IMAGE_TAG}

{{ end }}

# Ensures shared libraries installed with conda can be found by the dynamic link loader.

ENV LIBRARY_PATH="$LIBRARY_PATH:/opt/conda/lib" \

LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/opt/conda/lib"

{{ if eq .Accelerator "gpu" }}

ARG CUDA_MAJOR_VERSION \

CUDA_MINOR_VERSION

ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \

CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}

# Make sure we are on the right version of CUDA

RUN update-alternatives --set cuda /usr/local/cuda-$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION

# 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.

LD_LIBRARY_PATH_NO_STUBS="$LD_LIBRARY_PATH" \

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

{{ end }}

# Keep these variables in sync if base image is updated.

ENV TENSORFLOW_VERSION=2.16.1 \

# See https://github.com/tensorflow/io#tensorflow-version-compatibility

TENSORFLOW_IO_VERSION=0.37.0

# 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 \

TORCH_VERSION \

TORCHAUDIO_VERSION \

TORCHVISION_VERSION \

JAX_VERSION

# 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 \

# Also make the KMP logs noverbose.

# https://stackoverflow.com/questions/70250304/stop-tensorflow-from-printing-warning-message

KMP_SETTINGS=false \

# Remove the pip as the root user warning.

PIP_ROOT_USER_ACTION=ignore

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

# Update GPG key per documentation at https://cloud.google.com/compute/docs/troubleshooting/known-issues

RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -

RUN curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key --keyring /usr/share/keyrings/cloud.google.gpg add -

# 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 --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 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_DATA to points to the proj4 cartographic library.

ENV PROJ_DATA=/opt/conda/share/proj

# Install micromamba, setup channels, and replace conda with micromamba

ENV MAMBA_ROOT_PREFIX=/opt/conda

RUN curl -L "https://micro.mamba.pm/install.sh" -o /tmp/micromamba-install.sh \

&& bash /tmp/micromamba-install.sh \

&& rm /tmp/micromamba-install.sh \

&& mv ~/.local/bin/micromamba /usr/bin/micromamba \

&& (!(which conda) || cp /usr/bin/micromamba $(which conda)) \

&& micromamba config append channels nvidia \

&& micromamba config append channels rapidsai \

&& micromamba config append channels conda-forge \

&& micromamba config set channel_priority flexible \

&& python -m nb_conda_kernels.install --disable

# 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 micromamba install -y mkl cartopy imagemagick pyproj "shapely<2" && \

rm -rf /opt/conda/lib/python3.10/site-packages/pyproj/proj_dir/ && \

/tmp/clean-layer.sh

# Install spacy

# b/232247930: uninstall pyarrow to avoid double installation with the GPU specific version.

# b/341938540: unistall grpc-cpp to allow >=v24.4 cudf and cuml to be installed.

{{ if eq .Accelerator "gpu" }}

RUN pip uninstall -y pyarrow && \

micromamba install -vvvy spacy "cudf>=24.4" "cuml>=24.4" cupy cuda-version=$CUDA_MAJOR_VERSION.$CUDA_MINOR_VERSION && \

/tmp/clean-layer.sh

{{ else }}

RUN pip install spacy && \

/tmp/clean-layer.sh

{{ end}}

# Install PyTorch

# b/356397043: magma-cuda121 is the latest version

{{ if eq .Accelerator "gpu" }}

COPY --from=torch_whl /tmp/whl/*.whl /tmp/torch/

# b/356397043: We are currently using cuda 12.3,

# but magma-cuda121 is the latest compatible version

RUN micromamba install -y -c pytorch magma-cuda121 && \

pip install /tmp/torch/*.whl && \

sudo apt -y install libsox-dev && \

rm -rf /tmp/torch && \

/tmp/clean-layer.sh

{{ else }}

RUN pip install \

torch==$TORCH_VERSION+cpu \

torchvision==$TORCHVISION_VERSION+cpu \

torchaudio==$TORCHAUDIO_VERSION+cpu \

--index-url https://download.pytorch.org/whl/cpu && \

/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

{{ if eq .Accelerator "gpu" }}

COPY --from=jaxlib_whl /tmp/whl/*.whl /tmp/jax/

# b/319722433#comment9: Use pip wheels once versions matches our CUDA version.

RUN pip install /tmp/jax/*.whl jax==$JAX_VERSION && \

/tmp/clean-layer.sh

{{ else }}

RUN pip install jax[cpu] && \

/tmp/clean-layer.sh

{{ end }}

# Install GPU specific packages

{{ if eq .Accelerator "gpu" }}

# Install GPU-only packages

# No specific package for nnabla-ext-cuda 12.x minor versions.

RUN export PATH=/usr/local/cuda/bin:$PATH && \

export CUDA_ROOT=/usr/local/cuda && \

pip install pycuda \

pynvrtc \

pynvml && \

/tmp/clean-layer.sh

{{ end }}

# b/308525631: Pin Matplotlib until seaborn can be upgraded

# to >0.13.0 (now it's stuck by a package conflict with ydata-profiling 4.5.1).

RUN JAXVER=$(pip freeze | grep -e "^jax==") && \

pip install --upgrade \

"matplotlib<3.8.0" \

# ipympl adds interactive widget support for matplotlib

ipympl==0.7.0 \

"seaborn==0.12.2" \

pyupset \

python-dateutil dask dask-expr igraph \

pyyaml joblib geopy mne pyshp \

pandas \

polars \

flax \

"${JAXVER}" && \

/tmp/clean-layer.sh

RUN apt-get update && \

apt-get install -y default-jre && \

/tmp/clean-layer.sh

RUN pip install -f http://h2o-release.s3.amazonaws.com/h2o/latest_stable_Py.html h2o && /tmp/clean-layer.sh

RUN pip install \

"tensorflow==${TENSORFLOW_VERSION}" \

"tensorflow-io==${TENSORFLOW_IO_VERSION}" \

tensorflow-probability \

tensorflow_decision_forests \

tensorflow-text \

"tensorflow_hub>=0.16.0" \

tf-keras \

"keras>3" \

keras-cv \

keras-nlp && \

/tmp/clean-layer.sh

ADD patches/keras_internal.py \

patches/keras_internal_test.py \

/opt/conda/lib/python3.10/site-packages/tensorflow_decision_forests/keras/

# b/350573866: xgboost v2.1.0 breaks learntools

RUN apt-get install -y libfreetype6-dev && \

apt-get install -y libglib2.0-0 libxext6 libsm6 libxrender1 libfontconfig1 --fix-missing && \

rm -rf /opt/conda/lib/python3.10/site-packages/numpy* && \

pip install "numpy==1.26.4" && \

pip install gensim \

textblob \

wordcloud \

"xgboost==2.0.3" \

pydot \

hep_ml && \

# NLTK Project datasets

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 \

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 \

europarl_raw floresta gazetteers genesis gutenberg \

ieer inaugural indian jeita kimmo knbc large_grammars lin_thesaurus mac_morpho machado \

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 \

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 && \

pip install scikit-image && \

pip install opencv-contrib-python opencv-python && \

/tmp/clean-layer.sh

RUN pip install cython \

fasttext \

opencv-contrib-python \

opencv-python \

"scipy<1.14.0" \

# Scikit-learn accelerated library for x86

"scikit-learn-intelex>=2023.0.1" \

# HDF5 support

h5py \

# PUDB, for local debugging convenience

pudb \

imbalanced-learn \

# Profiling and other utilities

line_profiler \

bokeh \

numba \

datashader \

# b/328788268: libpysal 4.10 seems to fail with "module 'shapely' has no attribute 'Geometry'. Did you mean: 'geometry'"

"libpysal==4.9.2" \

# b/276344496: Install specific version of boto3, because 1.26.103 is broken.

"boto3==1.26.100" \

Boruta && \

# Pandoc is a dependency of deap

apt-get install -y pandoc && \

/tmp/clean-layer.sh

RUN apt-get install -y git-lfs && \

# vtk with dependencies

apt-get install -y libgl1-mesa-glx && \

pip install vtk && \

# xvfbwrapper with dependencies

apt-get install -y xvfb && \

pip install xvfbwrapper && \

/tmp/clean-layer.sh

RUN pip install mpld3 \

gpxpy \

arrow \

nilearn \

nibabel \

imgaug \

preprocessing \

path.py && \

pip install deap \

# b/302136621: Fix eli5 import for learntools, newer version require scikit-learn > 1.3

"tpot==0.12.1" \

scikit-optimize \

haversine \

toolz cytoolz \

plotly \

hyperopt \

langid \

# Useful data exploration libraries (for missing data and generating reports)

missingno \

pandas-profiling \

bayesian-optimization \

matplotlib-venn \

pyldavis \

mlxtend \

altair \

ImageHash \

ecos \

CVXcanon \

pymc3 \

tifffile \

geojson \

pydicom \

wavio \

SimpleITK \

squarify \

fuzzywuzzy \

python-louvain \

pyexcel-ods \

sklearn-pandas \

prophet \

holidays \

holoviews \

scikit-multilearn \

leven \

catboost \

folium \

scikit-plot \

fury dipy \

plotnine \

scikit-surprise \

pymongo \

eli5 \

kaggle \

kagglehub \

google-generativeai \

pytest && \

/tmp/clean-layer.sh

# Add google PAIR-code Facets

RUN 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 librosa \

sentencepiece \

cufflinks \

lime \

memory_profiler && \

/tmp/clean-layer.sh

RUN pip install annoy \

category_encoders && \

# 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 && \

# google-cloud-automl 2.0.0 introduced incompatible API changes, need to pin to 1.0.1

# After launch this should be installed from pip

pip install git+https://github.com/googleapis/python-aiplatform.git@mb-release \

google-cloud-automl==1.0.1 \

google-api-core==1.33.2 \

google-cloud-bigquery \

google-cloud-storage && \

# Split these installations to avoid `pip._vendor.resolvelib.resolvers.ResolutionTooDeep: 200000`

# b/315753846: Unpin translate package.

pip install google-cloud-translate==3.12.1 \

google-cloud-language==2.* \

google-cloud-videointelligence==2.* \

google-cloud-vision==2.* \

protobuf==3.20.3 \

# Pandas data reader

pandas-datareader \

emoji \

# Add Japanese morphological analysis engine

janome \

# yellowbrick machine learning visualization library

yellowbrick \

mlcrate && \

/tmp/clean-layer.sh

# b/273059949: The pre-installed nbconvert is slow on html conversions and has to be force-uninstalled.

# b/274619697: learntools also requires a specific nbconvert right now

RUN rm -rf /opt/conda/lib/python3.10/site-packages/{nbconvert,nbclient,mistune,platformdirs}*

RUN pip install bleach \

certifi \

cycler \

decorator \

entrypoints \

html5lib \

ipykernel \

ipython \

ipython-genutils \

# Fix qgrid by pinning ipywidgets https://github.com/quantopian/qgrid/issues/376

ipywidgets==7.7.1 \

isoweek \

jedi \

jsonschema \

jupyter-client \

jupyter-console \

jupyter-core \

jupyterlab-lsp \

MarkupSafe \

mistune \

nbformat \

notebook \

"nbconvert==6.4.5" \

papermill \

python-lsp-server[all] \

olefile \

kornia \

pandas_summary \

pandocfilters \

pexpect \

pickleshare \

Pillow && \

# Install openslide and its python binding

apt-get install -y openslide-tools && \

pip install openslide-python \

ptyprocess \

Pygments \

pyparsing \

pytz \

PyYAML \

pyzmq \

qtconsole \

six \

terminado \

tornado \

tqdm \

traitlets \

wcwidth \

webencodings \

widgetsnbextension \

# Require pyarrow newer than https://github.com/advisories/GHSA-5wvp-7f3h-6wmm

{{ if eq .Accelerator "gpu" }} pyarrow {{ else }} "pyarrow>=14.0.1" {{ end }}

RUN python -m spacy download en_core_web_sm && python -m spacy download en_core_web_lg && \

apt-get update && 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 rm /opt/conda/lib/python3.10/site-packages/google*/direct_url.json && \

rm /opt/conda/lib/python3.10/site-packages/google*/REQUESTED

# dlib has a libmkl incompatibility:

# test_dlib_face_detector (test_dlib.TestDLib) ... INTEL MKL ERROR: /opt/conda/bin/../lib/libmkl_avx512.so.2: undefined symbol: mkl_sparse_optimize_bsr_trsm_i8.

# Intel MKL FATAL ERROR: Cannot load libmkl_avx512.so.2 or libmkl_def.so.2.

# nnabla breaks protobuf compatibiilty:

RUN pip install wandb \

pyemd \

pympler \

featuretools \

#-e git+https://github.com/SohierDane/BigQuery_Helper#egg=bq_helper \

git+https://github.com/Kaggle/learntools \

ray \

gym \

pyarabic \

pandasql \

# b/302136621: Fix eli5 import for learntools

scikit-learn==1.2.2 \

# b/329869023 shap 0.45.0 breaks learntools

shap==0.44.1 \

cesium \

rgf_python \

jieba \

tsfresh \

optuna \

plotly_express \

albumentations \

Rtree \

accelerate && \

apt-get -y install libspatialindex-dev && \

# b/370860329: newer versions are not capable with current tensorflow

rm -rf /opt/conda/lib/python3.10/site-packages/numpy* && \

pip install "numpy==1.26.4" && \

pip install pytorch-ignite \

qgrid \

bqplot \

earthengine-api \

transformers \

datasets \

s3fs \

gcsfs \

kaggle-environments \

# geopandas > v0.14.4 breaks learn tools

geopandas==v0.14.4 \

"shapely<2" \

pydub \

pydegensac \

torchmetrics \

pytorch-lightning \

sympy \

# flask is used by agents in the simulation competitions.

flask \

# pycrypto is used by competitions team.

pycryptodome \

nbdev \

easyocr \

onnx \

tables \

openpyxl \

timm \

torchinfo && \

pip install git+https://github.com/facebookresearch/segment-anything.git && \

# b/370860329: newer versions are not capable with current tensorflow

pip install --no-dependencies fastai fastdownload && \

# b/343971718: remove duplicate aiohttp installs, and reinstall it

rm -rf /opt/conda/lib/python3.10/site-packages/aiohttp* && \

micromamba install --force-reinstall -y aiohttp && \

/tmp/clean-layer.sh

# Download base easyocr models.

# https://github.com/JaidedAI/EasyOCR#usage

RUN mkdir -p /root/.EasyOCR/model && \

wget --no-verbose "https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/latin_g2.zip" -O /root/.EasyOCR/model/latin.zip && \

unzip /root/.EasyOCR/model/latin.zip -d /root/.EasyOCR/model/ && \

rm /root/.EasyOCR/model/latin.zip && \

wget --no-verbose "https://github.com/JaidedAI/EasyOCR/releases/download/v1.3/english_g2.zip" -O /root/.EasyOCR/model/english.zip && \

unzip /root/.EasyOCR/model/english.zip -d /root/.EasyOCR/model/ && \

rm /root/.EasyOCR/model/english.zip && \

wget --no-verbose "https://github.com/JaidedAI/EasyOCR/releases/download/pre-v1.1.6/craft_mlt_25k.zip" -O /root/.EasyOCR/model/craft_mlt_25k.zip && \

unzip /root/.EasyOCR/model/craft_mlt_25k.zip -d /root/.EasyOCR/model/ && \

rm /root/.EasyOCR/model/craft_mlt_25k.zip && \

/tmp/clean-layer.sh

# Tesseract and some associated utility packages

RUN apt-get install tesseract-ocr -y && \

pip install pytesseract \

wand \

pdf2image \

PyPDF && \

/tmp/clean-layer.sh

ENV TESSERACT_PATH=/usr/bin/tesseract \

# For Facets

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 && \

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/python3.10/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 && \

# b/333854354: pin jupyter-server to version 2.12.5; later versions break LSP (b/333854354)

pip install --force-reinstall --no-deps jupyter_server==2.12.5 && \

/tmp/clean-layer.sh

# Fix to import bq_helper library without downgrading setuptools

RUN mkdir -p ~/src && git clone https://github.com/SohierDane/BigQuery_Helper ~/src/BigQuery_Helper && \

mkdir -p ~/src/BigQuery_Helper/bq_helper && \

mv ~/src/BigQuery_Helper/bq_helper.py ~/src/BigQuery_Helper/bq_helper/__init__.py && \

mv ~/src/BigQuery_Helper/test_helper.py ~/src/BigQuery_Helper/bq_helper/ && \

sed -i 's/)/packages=["bq_helper"])/g' ~/src/BigQuery_Helper/setup.py && \

pip install -e ~/src/BigQuery_Helper && \

/tmp/clean-layer.sh

# 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 \

patches/log.py \

patches/sitecustomize.py \

/root/.local/lib/python3.10/site-packages/

# Override default imagemagick policies

ADD patches/imagemagick-policy.xml /etc/ImageMagick-6/policy.xml

# Add Kaggle module resolver

ADD patches/kaggle_module_resolver.py /opt/conda/lib/python3.10/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' /opt/conda/lib/python3.10/site-packages/tensorflow_hub/config.py && \

sed -i '/_install_default_resolvers()/a \ \ registry.resolver.add_implementation(kaggle_module_resolver.KaggleFileResolver())' /opt/conda/lib/python3.10/site-packages/tensorflow_hub/config.py && \

# Disable preloaded jupyter modules (they add to startup, and break when they are missing)

sed -i /bq_stats/d /etc/ipython/ipython_kernel_config.py && \

sed -i /beatrix/d /etc/ipython/ipython_kernel_config.py && \

sed -i /bigquery/d /etc/ipython/ipython_kernel_config.py && \

sed -i /sql/d /etc/ipython/ipython_kernel_config.py

# Force only one libcusolver

{{ if eq .Accelerator "gpu" }}

RUN rm /opt/conda/bin/../lib/libcusolver.so.11 && ln -s /usr/local/cuda/lib64/libcusolver.so.11 /opt/conda/bin/../lib/libcusolver.so.11

{{ else }}

RUN ln -s /usr/local/cuda/lib64/libcusolver.so.11 /opt/conda/bin/../lib/libcusolver.so.11

{{ end }}

# b/270147159: conda ships with a version of libtinfo which is missing version info causing warnings, replace it with a good version.

RUN rm /opt/conda/lib/libtinfo.so.6 && ln -s /usr/lib/x86_64-linux-gnu/libtinfo.so.6 /opt/conda/lib/libtinfo.so.6 && \

# b/276358430: fix Jupyter lsp freezing up the jupyter server

pip install "jupyter-lsp==1.5.1"

# Set backend for matplotlib

ENV MPLBACKEND="agg" \

# Set LC_ALL

# https://github.com/explosion/spaCy/issues/12872#issuecomment-1661847588

LC_ALL="POSIX"

ARG GIT_COMMIT=unknown \

BUILD_DATE=unknown

LABEL git-commit=$GIT_COMMIT \

build-date=$BUILD_DATE

ENV GIT_COMMIT=${GIT_COMMIT} \

BUILD_DATE=${BUILD_DATE}

LABEL tensorflow-version=$TENSORFLOW_VERSION \

# Used in the Jenkins `Docker GPU Build` step to restrict the images being pruned.

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

{{ if eq .Accelerator "gpu" }}

# Remove the CUDA stubs.

ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH_NO_STUBS" \

# Add the CUDA home.

CUDA_HOME=/usr/local/cuda

{{ end }}