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Use Colab as a base image. (#1444)
This change makes a number of major changes: - Colab is the base image - uv is the main package install tool - leveraging requirements.txt instead of many separate installs - stop building and installing tensorflow/torch/lightbgm/jax since those are managed by the Colab base image now In order to decide what packages to explicitly install I: - looked at what packages are in the Colab base image - looked at what packages were in the Kaggle image - looked at what packages were explicitly mentioned in Kaggle Dockerfile This may still take a few iterations to get all the right parts in the image, but this should hopefully make the image much more manageable. http://b/365782129
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‎Dockerfile.tmpl

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‎Jenkinsfile

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}
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stages {
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stage('Pre-build Packages from Source') {
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parallel {
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stage('torch') {
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options {
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timeout(time: 300, unit: 'MINUTES')
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}
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steps {
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sh '''#!/bin/bash
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set -exo pipefail
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source config.txt
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cd packages/
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./build_package --base-image $BASE_IMAGE_REPO/$GPU_BASE_IMAGE_NAME:$BASE_IMAGE_TAG \
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--package torch \
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--version $TORCH_VERSION \
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--build-arg TORCHAUDIO_VERSION=$TORCHAUDIO_VERSION \
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--build-arg TORCHVISION_VERSION=$TORCHVISION_VERSION \
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--build-arg CUDA_MAJOR_VERSION=$CUDA_MAJOR_VERSION \
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--build-arg CUDA_MINOR_VERSION=$CUDA_MINOR_VERSION \
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--push
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'''
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}
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}
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stage('lightgbm') {
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options {
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timeout(time: 10, unit: 'MINUTES')
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}
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steps {
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sh '''#!/bin/bash
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set -exo pipefail
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source config.txt
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cd packages/
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./build_package --base-image $BASE_IMAGE_REPO/$GPU_BASE_IMAGE_NAME:$BASE_IMAGE_TAG \
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--package lightgbm \
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--version $LIGHTGBM_VERSION \
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--build-arg CUDA_MAJOR_VERSION=$CUDA_MAJOR_VERSION \
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--build-arg CUDA_MINOR_VERSION=$CUDA_MINOR_VERSION \
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--push
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'''
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}
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}
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stage('jaxlib') {
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options {
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timeout(time: 300, unit: 'MINUTES')
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}
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steps {
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sh '''#!/bin/bash
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set -exo pipefail
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source config.txt
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cd packages/
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./build_package --base-image $BASE_IMAGE_REPO/$GPU_BASE_IMAGE_NAME:$BASE_IMAGE_TAG \
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--package jaxlib \
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--version $JAX_VERSION \
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--build-arg CUDA_MAJOR_VERSION=$CUDA_MAJOR_VERSION \
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--build-arg CUDA_MINOR_VERSION=$CUDA_MINOR_VERSION \
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--push
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'''
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}
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}
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}
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}
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stage('Build/Test/Diff') {
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parallel {
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stage('CPU') {

‎clean-layer.sh

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# Ensures the current working directory won't be deleted
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cd /usr/local/src/
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# Delete source files used for building binaries
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rm -rf /usr/local/src/*
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# Delete conda downloaded tarballs
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conda clean -y --tarballs
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rm -rf /usr/local/src/*

‎config.txt

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BASE_IMAGE_REPO=gcr.io/deeplearning-platform-release
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BASE_IMAGE_TAG=m122
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CPU_BASE_IMAGE_NAME=tf2-cpu.2-16.py310
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GPU_BASE_IMAGE_NAME=tf2-gpu.2-16.py310
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LIGHTGBM_VERSION=4.2.0
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TORCH_VERSION=2.4.0
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TORCHAUDIO_VERSION=2.4.0
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TORCHVISION_VERSION=0.19.0
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JAX_VERSION=0.4.26
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CUDA_MAJOR_VERSION=12
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CUDA_MINOR_VERSION=3
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CUDA_MINOR_VERSION=2

‎kaggle_requirements.txt

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altair>=5.4.0
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Babel
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Boruta
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Cartopy
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ImageHash
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Janome
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PyArabic
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PyUpSet
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Pympler
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Rtree
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shapely<2
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SimpleITK
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TPOT
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Theano
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Wand
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annoy
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arrow
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bayesian-optimization
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boto3
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catboost
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category-encoders
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cesium
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comm
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cytoolz
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dask-expr
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datasets
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datashader
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deap
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dipy
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docker
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easyocr
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eli5
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emoji
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fasttext
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featuretools
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fiona
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fury
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fuzzywuzzy
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geojson
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# geopandas > v0.14.4 breaks learn tools
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geopandas==v0.14.4
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google-cloud-aiplatform
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# google-cloud-automl 2.0.0 introduced incompatible API changes, need to pin to 1.0.1
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google-cloud-automl==1.0.1
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# b/315753846: Unpin translate package.
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google-cloud-translate==3.12.1
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google-cloud-videointelligence
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google-cloud-vision
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gpxpy
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h2o
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haversine
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hep-ml
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igraph
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ipympl
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ipywidgets==8.1.5
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isoweek
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jedi
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# b/276358430: fix Jupyter lsp freezing up the jupyter server
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jupyter-lsp==1.5.1
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# b/333854354: pin jupyter-server to version 2.12.5; later versions break LSP (b/333854354)
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jupyter_server==2.12.5
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jupyterlab
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jupyterlab-lsp
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kaggle-environments
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kagglehub>=0.3.4
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# Keras 3.6 broke test_keras.py > test_train > keras.datasets.mnist.load_data():
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# See https://github.com/keras-team/keras/commit/dcefb139863505d166dd1325066f329b3033d45a
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keras<3.6
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keras-cv
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keras-nlp
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keras-tuner
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kornia
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langid
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leven
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# b/328788268: libpysal 4.10 seems to fail with "module 'shapely' has no attribute 'Geometry'. Did you mean: 'geometry'"
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libpysal<=4.9.2
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lime
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line_profiler
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mamba
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mlcrate
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mne
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mpld3
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nbdev
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nilearn
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olefile
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onnx
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openslide-bin
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openslide-python
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optuna
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pandas-profiling
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pandasql
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papermill
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path
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path.py
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pdf2image
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plotly-express
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preprocessing
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pudb
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pyLDAvis
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pycryptodome
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pydegensac
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pydicom
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pydub
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pyemd
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pyexcel-ods
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pymc3
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pymongo
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pypdf
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pytesseract
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python-lsp-server
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pytorch-ignite
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pytorch-lightning
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qgrid
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qtconsole
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ray
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rgf-python
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s3fs
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scikit-learn-intelex
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scikit-multilearn
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scikit-optimize
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scikit-plot
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scikit-surprise
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git+https://github.com/facebookresearch/segment-anything.git
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shap
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squarify
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tensorflow-cloud
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tensorflow-io
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tensorflow-text
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tensorflow_decision_forests
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timm
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torchinfo
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torchmetrics
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tsfresh
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vtk
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wandb
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wavio
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xgboost==2.0.3
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xvfbwrapper
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ydata-profiling

‎test

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IMAGE_TAG='kaggle/python-build'
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IMAGE_TAG_OVERRIDE=''
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ADDITONAL_OPTS=''
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ADDITONAL_OPTS='--runtime runc ' # Use the CPU runtime by default
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PATTERN='test*.py'
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usage() {
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readonly PATTERN
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set -x
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docker run --rm --net=none -v /tmp/python-build:/tmp/python-build "$IMAGE_TAG" rm -rf /tmp/python-build/*
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docker rm jupyter_test || true
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mkdir -p /tmp/python-build/tmp
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mkdir -p /tmp/python-build/devshm
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mkdir -p /tmp/python-build/working
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# Note about `--hostname localhost` (b/158137436)
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# hostname defaults to the container name which fails DNS name
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# resolution with --net=none (required to keep tests hermetic). See details in bug.
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#
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# Note about CLOUDSDK_CONFIG=/tmp/.config/gcloud
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# We use the /tmp dir since the filesystem is --read-only and we need writable space for gcloud configs.
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docker run --rm -t --read-only --net=none \
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-e HOME=/tmp -e KAGGLE_DATA_PROXY_TOKEN=test-key \
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-e KAGGLE_USER_SECRETS_TOKEN_KEY=test-secrets-key \
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-e KAGGLE_DATA_PROXY_PROJECT=test \
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-e TF_FORCE_GPU_ALLOW_GROWTH=true \
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-e XLA_PYTHON_CLIENT_PREALLOCATE=false \
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-e CLOUDSDK_CONFIG=/tmp/.config/gcloud \
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--hostname localhost \
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--shm-size=2g \
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-v $PWD:/input:ro -v /tmp/python-build/working:/working \

‎tests/test_cuml.py

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class TestCuml(unittest.TestCase):
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@gpu_test
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@p100_exempt # b/342143152: cuML(>=24.4v) is inompatible with p100 GPUs.
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@unittest.skip("b/381287748 cuML is not installed in Colab.")
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def test_pca_fit_transform(self):
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import unittest
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import numpy as np

‎tests/test_fastai.py

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"/input/tests/data/train.csv",
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cont_names=["pixel"+str(i) for i in range(784)],
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y_names='label',
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procs=[FillMissing, Categorify, Normalize])
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procs=[FillMissing, Categorify, Normalize])
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learn = tabular_learner(dls, layers=[200, 100])
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learn.fit_one_cycle(n_epoch=1)
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with learn.no_bar():
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learn.fit_one_cycle(n_epoch=1)
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self.assertGreater(learn.smooth_loss, 0)
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self.assertGreater(learn.smooth_loss, 0)

‎tests/test_lightgbm.py

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self.assertEqual(1, gbm.best_iteration)
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# TODO(b/381256047): Colab needs to install GPU-enabled lightgbm.
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@gpu_test
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@unittest.skip("Skipping this test until b/381256047 is resolved.")
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def test_gpu(self):
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lgb_train, lgb_eval = self.load_datasets()
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